Motor diagnosis component and method, electronic device, storage medium

Abstract

The application relates to the technical field of power plants, in particular to a motor diagnosis assembly and method, an electronic device and a storage medium. The motor diagnosis assembly comprises a host computer and at least one auxiliary computer. In the host computer, a host computer power module is used for power supply of the host computer, a host computer acquisition module is used for external acquisition of a first type of device signal, and a host computer board comprises a host computer storage module used for storing first type data and a diagnosis analysis module used for executing a motor diagnosis operation. In the auxiliary computer, an auxiliary computer power module is used for power supply of the auxiliary computer, an auxiliary computer acquisition module is used for external acquisition of a second type of device signal, and an auxiliary computer board comprises an auxiliary computer storage module used for storing second type data, and a host computer data transmission module is used for cooperating with an auxiliary computer data transmission module to perform data collaborative transmission of the host computer and the auxiliary computer. Through cooperation of the host computer and the at least one auxiliary computer, different types of signals can be subjected to diagnosis analysis under a unified time axis.

Description

Technical Field

[0001] This application relates to the field of power plant technology, and in particular to a motor diagnostic component and method, electronic equipment, and storage medium. Background Technology

[0002] Electric motors are widely used equipment in the industrial field. After the project is completed, the motor needs to be debugged. The debugging process requires monitoring parameters such as starting current, voltage, short-circuit current, operating current and voltage, motor temperature, vibration, and noise.

[0003] In the early stages of the project, the factory's network communication was inadequate, and there was no integrated online monitoring platform for electrical and mechanical signals. Monitoring activities had to rely on portable equipment. Measurements of non-electrical parameters such as vibration, temperature, and noise often required separate devices like portable vibration meters, hand thermometers, and noise meters. Electrical monitoring was typically conducted at the distribution cabinet level, while mechanical monitoring was performed at the equipment level. Traditional data acquisition required numerous sensors located in scattered locations, and signals could not be shared. Under these circumstances, it was difficult to compare and analyze different parameters over a unified timeline, leading to the failure to detect potential motor problems in a timely manner. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a motor diagnostic component and method, electronic device, and storage medium capable of diagnosing and analyzing different types of signals on a unified time axis.

[0005] The motor diagnostic component according to a first aspect embodiment of this application includes:

[0006] The host includes a host power module, a host acquisition module, a host computer board, and a host data transmission module; wherein, the host power module is used to supply power to the host, the host acquisition module is used to acquire signals from a first type of device, and the host computer board includes a host storage module for storing the first type of data and a diagnostic analysis module for performing motor diagnostic operations;

[0007] At least one auxiliary machine, the auxiliary machine including an auxiliary machine power supply module, an auxiliary machine acquisition module, an auxiliary machine computer board, and an auxiliary machine data transmission module; wherein, the auxiliary machine power supply module is used to supply power to the auxiliary machine, the auxiliary machine acquisition module is used to acquire signals from a second type of device, the auxiliary machine computer board includes an auxiliary machine storage module for storing the second type of data, and the host data transmission module is used to cooperate with the auxiliary machine data transmission module to perform collaborative data transmission between the host and the auxiliary machine.

[0008] According to some embodiments of this application, the host acquisition module is installed on the electrical equipment corresponding to the target motor to acquire electrical equipment signals as the first type of equipment signals, and the auxiliary acquisition module is installed on the mechanical equipment corresponding to the target motor to acquire mechanical equipment signals as the second type of equipment signals.

[0009] According to some embodiments of this application, the auxiliary acquisition module integrates a temperature sensing unit, a sound sensing unit, and a vibration sensing unit; wherein, the temperature sensing unit, the sound sensing unit, and the vibration sensing unit are used to be installed on the target motor to acquire temperature sensing signals, sound sensing signals, and vibration sensing signals, and integrate the temperature sensing signals, the sound sensing signals, and the vibration sensing signals into the second type of data.

[0010] According to some embodiments of this application, a first auxiliary machine transmission path and a second auxiliary machine transmission path are provided between the auxiliary machine acquisition module and the auxiliary machine computer board.

[0011] According to some embodiments of this application, the first auxiliary machine transmission path is provided with a first node, and the second auxiliary machine transmission path is provided with a second node. The first node and the second node are used to connect to the host computer board.

[0012] According to some embodiments of this application, a normally closed switch is provided between the second node and the auxiliary computer board on the second auxiliary machine transmission path. The normally closed switch is configured to disconnect when the host data transmission module and the auxiliary machine data transmission module are connected.

[0013] The motor diagnostic method according to a second aspect embodiment of this application, applied to the motor diagnostic component described in any one of the first aspect embodiments, includes:

[0014] The host and auxiliary machines of the motor diagnostic component are connected through the host data transmission module and the auxiliary machine data transmission module, and a reference time synchronization operation is performed on the electrical and mechanical equipment in the target power plant to align the acquisition reference time of the electrical and mechanical equipment.

[0015] During the operation of the target motor, the host control unit performs first-type signal acquisition according to the acquisition reference time to obtain first-type device signal;

[0016] During the operation of the target motor, the auxiliary machine is controlled to acquire the second type of signal according to the acquisition reference time to obtain the second type of equipment signal.

[0017] The signal of the second type of device is transmitted to the host data transmission module via the auxiliary data transmission module;

[0018] In the host, the timestamp information of the first type of device signal and the second type of device signal are aligned to obtain the first type parameter and the second type parameter;

[0019] Based on the first type of parameters and the second type of parameters, a motor diagnostic operation is performed to obtain a motor diagnostic result corresponding to the target motor.

[0020] According to some embodiments of this application, the method further includes:

[0021] During the acquisition of the first type of signal and the acquisition of the second type of signal, data monitoring is performed on the acquired first type of device signal and second type of device signal;

[0022] In response to the abnormal signals detected in data monitoring, attribution analysis is performed based on the abnormal signals to obtain abnormal situation information;

[0023] Based on the abnormal situation information, execute the corresponding situation response operation.

[0024] According to some embodiments of this application, the first type of device signal is an electrical device signal, and the second type of signal is a mechanical device signal. The control of the host computer to acquire the first type of signal according to the acquisition reference time, to obtain the first type of device signal, includes:

[0025] The host computer is controlled to collect signals from the electrical equipment corresponding to the target motor according to the acquisition reference time, thereby obtaining the electrical equipment signal; wherein, the timestamp information corresponding to the electrical equipment signal is electrical timestamp information;

[0026] The control of the auxiliary machine to perform second-type signal acquisition according to the acquisition reference time, to obtain second-type device signals, includes:

[0027] The host computer is controlled to collect signals from the mechanical equipment corresponding to the target motor according to the acquisition reference time, thereby obtaining the mechanical equipment signal; wherein, the timestamp information corresponding to the mechanical equipment signal is mechanical timestamp information.

[0028] According to some embodiments of this application, aligning the timestamp information of the first type of device signal and the second type of device signal to obtain the first type parameter and the second type parameter includes:

[0029] Based on the electrical timestamp information and the mechanical timestamp information, determine the electrical acquisition time and mechanical acquisition time corresponding to each acquisition sequence;

[0030] For each of the aforementioned acquisition timing sequences, the time offset value between the electrical acquisition time and the mechanical acquisition time is calculated sequentially;

[0031] Based on the time offset value corresponding to each acquisition time sequence, a deviation correction factor corresponding to each acquisition time sequence is determined;

[0032] Based on the deviation correction factor corresponding to each acquisition time sequence, a timestamp alignment operation is performed on the electrical timestamp information and the mechanical timestamp information in each acquisition time sequence to obtain the first type parameter and the second type parameter.

[0033] According to some embodiments of this application, determining the deviation correction factor corresponding to each acquisition time sequence based on the time offset value corresponding to each acquisition time sequence includes:

[0034] Based on each acquisition time sequence and the time offset value corresponding to each acquisition time sequence, offset value vector mapping data is established; wherein, the offset value vector mapping data is used to reflect the relationship between the time offset value and the acquisition time sequence.

[0035] Based on the offset value vector mapping data, the vector change rate is calculated to determine the offset value change rate corresponding to each of the acquisition time sequences;

[0036] Based on the rate of change of the offset value of each acquisition time sequence, configure the deviation correction factor corresponding to each acquisition time sequence.

[0037] According to some embodiments of this application, configuring the deviation correction factor corresponding to each acquisition time sequence based on the rate of change of the offset value of each acquisition time sequence includes:

[0038] Abrupt change is calculated based on the rate of change of the offset value in each of the aforementioned acquisition time sequences;

[0039] In response to the determination of a sudden change in the rate of change of the offset value during the mutation calculation, the offset value vector mapping data is sliced ​​based on the acquisition time series where the mutation occurred to obtain offset value slice data; wherein, the offset value slice data contains a first number of offset value sub-data, and each offset value sub-data corresponds to one acquisition time series;

[0040] Calculate the acquisition deviation ratio corresponding to the offset slice data;

[0041] Based on the acquisition deviation ratio, configure the deviation correction factor corresponding to each acquisition time sequence in the offset value slice data.

[0042] According to some embodiments of this application, the abrupt change calculation based on the rate of change of the offset value of each of the acquisition time series includes:

[0043] Based on the time offset value corresponding to each of the acquisition time sequences, the cumulative total offset value is determined; wherein, the number of acquisition time sequences is a second number;

[0044] Based on the cumulative total offset value and the second number, the mutation definition conditions are set;

[0045] In response to the existence of a rate of change of the offset value satisfying the mutation definition condition, it is determined that the corresponding rate of change of the offset value has undergone a mutation.

[0046] According to some embodiments of this application, determining that a sudden change in the corresponding rate of change of the offset value has occurred in response to the existence of a rate of change of the offset value satisfying the abrupt change definition condition includes:

[0047] Based on the cumulative total offset value and the second number, the mutation boundary value is calculated;

[0048] The rate of change of the offset value in the acquisition time series is compared with the mutation threshold value;

[0049] If the rate of change of the offset value exceeds the mutation threshold, it is determined that a mutation has occurred in the rate of change of the offset value.

[0050] According to some embodiments of this application, calculating the acquisition deviation ratio corresponding to the offset slice data includes:

[0051] Obtain the mutation occurrence time sequence corresponding to the offset value slice data;

[0052] A first offset parameter is determined based on the mutation occurrence time sequence; wherein, the first offset parameter is the time offset value between the electrical acquisition time and the mechanical acquisition time in the mutation occurrence time sequence;

[0053] Based on the mutation occurrence time sequence corresponding to the previous acquisition time sequence, a corresponding second offset parameter is determined; wherein, the second offset parameter is the time offset value between the electrical acquisition time and the mechanical acquisition time in the previous acquisition time sequence corresponding to the mutation occurrence time sequence;

[0054] Based on the first offset parameter and the second offset parameter, the acquisition deviation ratio corresponding to the offset value slice data is calculated.

[0055] According to some embodiments of this application, the step of calculating the acquisition deviation ratio corresponding to the offset value slice data based on the first offset parameter and the second offset parameter includes:

[0056] The acquisition deviation ratio is obtained by performing quotient processing based on the first offset parameter and the second offset parameter.

[0057] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the motor diagnostic method as described in any one of the embodiments of the first aspect of this application.

[0058] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program that is executed by a processor to implement the motor diagnostic method as described in any one of the embodiments of the first aspect of this application.

[0059] The motor diagnostic component of this application includes a main unit and at least one auxiliary unit. The main unit includes a main unit power supply module, a main unit acquisition module, a main unit computer board, and a main unit data transmission module; wherein, the main unit power supply module supplies power to the main unit, the main unit acquisition module acquires signals from a first type of device, and the main unit computer board includes a main unit storage module for storing the first type of data and a diagnostic analysis module for performing motor diagnostic operations. The auxiliary unit includes an auxiliary unit power supply module, an auxiliary unit acquisition module, an auxiliary unit computer board, and an auxiliary unit data transmission module; wherein, the auxiliary unit power supply module supplies power to the auxiliary unit, the auxiliary unit acquisition module acquires signals from a second type of device, the auxiliary unit computer board includes an auxiliary unit storage module for storing the second type of data, and the main unit data transmission module cooperates with the auxiliary unit data transmission module to perform collaborative data transmission between the main unit and the auxiliary unit. By cooperating with at least one auxiliary unit, different types of signals can be diagnosed and analyzed on a unified time axis.

[0060] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0061] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0062] Figure 1 A schematic block diagram of the motor diagnostic component provided in an embodiment of this application;

[0063] Figure 2 This is a connection diagram of the motor diagnostic component provided in an embodiment of this application;

[0064] Figure 3 A schematic diagram of the architecture for diagnosing motor equipment provided in an embodiment of this application;

[0065] Figure 4 This is a schematic diagram of the structure of the host and auxiliary machine provided in the embodiments of this application;

[0066] Figure 5This is a flowchart illustrating the motor diagnostic method provided in the embodiments of this application;

[0067] Figure 6 This is another flowchart illustrating the motor diagnostic method provided in this application embodiment;

[0068] Figure 7 This is another flowchart illustrating the motor diagnostic method provided in this application embodiment;

[0069] Figure 8 This is another flowchart illustrating the motor diagnostic method provided in this application embodiment;

[0070] Figure 9 This is another flowchart illustrating the motor diagnostic method provided in this application embodiment;

[0071] Figure 10 This is another flowchart illustrating the motor diagnostic method provided in this application embodiment;

[0072] Figure 11 This is another flowchart illustrating the motor diagnostic method provided in this application embodiment;

[0073] Figure 12 This is another flowchart illustrating the motor diagnostic method provided in this application embodiment;

[0074] Figure 13 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0075] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0076] In the description of this application, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0077] In the description of this application, it should be understood that the orientation descriptions, such as up, down, left, right, front, and back, are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0078] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0079] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setting," "installation," and "connection" should be interpreted broadly. Those skilled in the art can reasonably determine the specific meaning of the above terms in this application based on the specific content of the technical solution. Furthermore, the identification of specific steps in the following text does not imply a limitation on the order of steps or execution logic. The execution order and logic between each step should be understood and inferred from the content described in the embodiments.

[0080] Electric motors are widely used equipment in the industrial field. After the project is completed, the motor needs to be debugged. The debugging process requires monitoring parameters such as starting current, voltage, short-circuit current, operating current and voltage, motor temperature, vibration, and noise.

[0081] In the early stages of the project, the factory's network communication was not perfect, and there was no online monitoring platform that integrated electrical and mechanical signals on site. Monitoring activities had to be carried out using portable equipment. Measurements of non-electrical parameters such as vibration, temperature, and noise often required the use of independent equipment such as portable vibration meters, hand thermometers, and noise meters.

[0082] However, this multi-device approach has brought about many problems, including the clutter and inconvenience of using the equipment on site. Moreover, since these devices are independent, it is impossible to compare and analyze different parameters on a unified time axis, which makes it difficult to detect some potential motor hazards in a timely manner.

[0083] While some motor manufacturers possess large-scale motor testing platforms capable of simultaneously measuring electrical and mechanical parameters, these platforms have significant limitations. First, their complex structures and high costs require substantial resources for construction and maintenance. Second, they lack portability, hindering rapid deployment and use in industrial settings and failing to meet the urgent need for on-site online testing and diagnostics.

[0084] The main problems with these technologies can be summarized as follows: First, the integration of the technologies is low, requiring the use of multiple types of equipment to measure different parameters. This not only increases the overall cost of the equipment but also makes on-site operation complex and cumbersome. Second, the use of multiple devices leads to compatibility and communication issues between them, affecting the efficiency and reliability of the entire testing and diagnostic process. Third, the measured parameters cannot be compared and analyzed on the same timeline, making it difficult to comprehensively and accurately assess the motor's operating status and thus hindering the timely detection of potential faults.

[0085] Furthermore, current technologies have shortcomings in meeting diverse monitoring scenarios. For example, in the early stages of engineering construction, the factory's network communication may not be fully developed, lacking an online monitoring platform that integrates electrical and mechanical signals. This necessitates portable devices for monitoring activities. However, traditional data acquisition methods require collecting large amounts of sensor data, and the dispersed locations of sensors and the inability to communicate signals undoubtedly pose significant challenges to the application of portable monitoring equipment. These devices need to address how to achieve high integration, how to resolve the issues of dispersed acquisition and centralized display and analysis of electrical and mechanical signals, and how to adapt to the diverse needs of monitoring scenarios. In short, the relevant motor starting test equipment and technical solutions have significant deficiencies in terms of integration, portability, data integration, and analysis capabilities, limiting their effectiveness and reliability in practical applications.

[0086] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a motor diagnostic component and method, electronic device, and storage medium capable of diagnosing and analyzing different types of signals on a unified time axis.

[0087] The following explanation is based on the accompanying drawings.

[0088] Reference Figure 1 The motor diagnostic component according to the embodiments of this application may include:

[0089] The host includes a host power module, a host acquisition module, a host computer board, and a host data transmission module. The host power module is used to supply power to the host, the host acquisition module is used to acquire signals from the first type of device, and the host computer board includes a host storage module for storing the first type of data and a diagnostic analysis module for performing motor diagnostic operations.

[0090] At least one auxiliary unit, which includes an auxiliary power supply module, an auxiliary acquisition module, an auxiliary computer board, and an auxiliary data transmission module; wherein, the auxiliary power supply module is used to supply power to the auxiliary unit, the auxiliary acquisition module is used to acquire signals from the second type of device, the auxiliary computer board includes an auxiliary storage module for storing the second type of data, and the host data transmission module is used to cooperate with the auxiliary data transmission module to perform collaborative data transmission between the host and the auxiliary unit.

[0091] It should be noted that in the motor diagnostic component of this application embodiment, the signal acquisition functions of the main unit and the auxiliary unit have a certain degree of flexibility and adaptability, and can be adjusted according to actual application scenarios and needs. Typically, the main unit is primarily responsible for acquiring electrical equipment signals, while the auxiliary unit is responsible for acquiring mechanical equipment signals; however, this division of labor is not fixed. In some cases, the main unit can also detect mechanical equipment signals as first-type equipment signals, and in other cases, the auxiliary unit can also detect electrical equipment signals as second-type equipment signals.

[0092] The main unit, as the core of the diagnostic component, is equipped with a series of functional modules, including a main unit power module, a main unit acquisition module, a main unit computer board, and a main unit data transmission module. The main unit power module ensures stable operation of the main unit even without an external power supply, providing necessary power support to other modules. The main unit acquisition module has the capability to acquire signals from primary equipment. Under normal circumstances, the main unit acquisition module is used to collect signals from electrical equipment, such as current and voltage parameters. This is because electrical equipment is usually installed in fixed locations, such as distribution cabinets, and the acquisition of electrical equipment signals does not require frequent relocation, thus better protecting the main unit and ensuring its stable operation. However, in certain special cases, if the need to collect signals from mechanical equipment is more urgent, or if the main unit is located closer to the mechanical equipment, the main unit acquisition module can also be flexibly used to collect signals from mechanical equipment, such as vibration and temperature parameters. In this case, the main unit storage module on the main unit computer board will store this mechanical equipment signal data, while the diagnostic analysis module will perform in-depth analysis of the stored data according to preset algorithms and logic to determine whether there are potential mechanical faults in the motor, providing technical assurance for the safe operation of the motor.

[0093] As a crucial auxiliary component of the main unit, the auxiliary machine is also equipped with functional modules such as an auxiliary power module, an auxiliary data acquisition module, an auxiliary computer board, and an auxiliary data transmission module. The auxiliary power module ensures stable operation even without an external power supply, while the auxiliary data acquisition module efficiently collects signals from secondary equipment. Generally, the auxiliary data acquisition module is used to collect mechanical equipment signals, such as vibration and temperature parameters, as mechanical equipment is typically distributed widely, making auxiliary acquisition of these signals more flexible and convenient. However, in certain special cases, if the demand for electrical equipment signal acquisition increases, or if the auxiliary machine is located closer to the electrical equipment, the auxiliary data acquisition module can also be flexibly used to collect electrical equipment signals, such as current and voltage parameters. In this case, the auxiliary storage module on the auxiliary computer board will store this electrical equipment signal data. Through the collaborative work of the main unit's data transmission module and the auxiliary unit's data transmission module, efficient data transmission and integration between the main unit and the auxiliary machine are achieved, ensuring that electrical and mechanical equipment signals can be comprehensively analyzed on a unified timeline, providing complete and accurate data support for comprehensive motor diagnostics.

[0094] This design allows the motor diagnostic component to flexibly adjust the signal acquisition tasks of the main unit and auxiliary unit according to the actual application scenario, improving the adaptability and efficiency of the entire diagnostic system. For example, in some industrial sites, if the main unit is located closer to the mechanical equipment and the auxiliary unit is located closer to the electrical equipment, the acquisition tasks of the main unit and auxiliary unit can be adjusted to achieve more efficient data acquisition and analysis. Simultaneously, by rationally allocating the functions of the main unit and auxiliary unit, the stability of the main unit is protected while fully utilizing the flexibility of the auxiliary unit, ensuring the comprehensiveness and accuracy of motor diagnostics. In summary, the motor diagnostic component of this application, through flexible modular design and function allocation, achieves efficient collaboration between the main unit and auxiliary unit, better adapting to different working environments and task requirements, and providing strong technical support for motor maintenance and management.

[0095] Reference Figure 2 According to some embodiments of this application, the host acquisition module is installed on the electrical equipment corresponding to the target motor to acquire electrical equipment signals as a first type of equipment signal, and the auxiliary acquisition module is installed on the mechanical equipment corresponding to the target motor to acquire mechanical equipment signals as a second type of equipment signal.

[0096] It should be noted that the motor diagnostic component in this application embodiment is designed with full consideration of the flexibility and practicality of motor diagnostics. Through the division of labor and cooperation between the host and the auxiliary machine, it realizes the comprehensive acquisition and efficient analysis of motor equipment signals.

[0097] As the core component of the diagnostic module, the host unit, in addition to basic signal acquisition functions, also undertakes signal diagnosis and analysis, as well as other important functions. Typically, the host unit is more suitable for acquiring signals from electrical equipment as the primary type of equipment signal. This is because electrical equipment is usually installed in fixed locations, such as distribution cabinets, and the acquisition of electrical equipment signals does not require frequent relocation, thus better protecting the host unit and ensuring its stable operation. The host power module provides stable power support, ensuring the host unit's continuous operation. The host acquisition module is responsible for acquiring primary type of equipment signals, i.e., electrical equipment signals, such as current and voltage parameters. The host computer board integrates a host storage module and a diagnostic analysis module. The host storage module stores the acquired electrical equipment signal data, which forms the basis for subsequent diagnostic analysis. The diagnostic analysis module, based on preset algorithms and logic, performs in-depth analysis of the stored data to determine whether there are potential electrical faults in the motor, providing technical assurance for the safe operation of the motor.

[0098] The auxiliary unit, as a crucial support component to the main unit, is primarily responsible for acquiring signals from the second type of equipment. While the main unit acquires signals from electrical equipment, the auxiliary unit focuses on acquiring signals from mechanical equipment, such as vibration and temperature parameters. Since mechanical equipment is typically distributed widely, using an auxiliary unit to acquire these signals is more flexible and convenient. The auxiliary unit's power module ensures stable operation even without an external power supply, and its acquisition module efficiently acquires signals from the second type of equipment. The auxiliary unit's storage module on its computer board stores the acquired mechanical equipment signal data. The main unit's data transmission module works in conjunction with the auxiliary unit's data transmission module to achieve efficient data transmission and integration between the two units. This ensures that electrical and mechanical equipment signals can be comprehensively analyzed on a unified timeline, providing complete and accurate data support for comprehensive motor diagnostics.

[0099] This design allows the motor diagnostic components to flexibly adjust the signal acquisition tasks of the main unit and auxiliary unit according to the actual application scenario, improving the adaptability and efficiency of the entire diagnostic system. At the same time, by rationally allocating the functions of the main unit and auxiliary unit, the stability of the main unit is protected while fully leveraging the flexibility of the auxiliary unit, ensuring the comprehensiveness and accuracy of motor diagnostics.

[0100] According to some embodiments of this application, the auxiliary acquisition module integrates a temperature sensing unit, a sound sensing unit, and a vibration sensing unit; wherein, the temperature sensing unit, the sound sensing unit, and the vibration sensing unit are used to be installed on the target motor to collect temperature sensing signals, sound sensing signals, and vibration sensing signals, and integrate the temperature sensing signals, sound sensing signals, and vibration sensing signals into a second type of data.

[0101] It should be noted that the auxiliary acquisition module is highly integrated in its design to meet the need for comprehensive monitoring of the motor's mechanical condition. The auxiliary acquisition module integrates temperature sensing units, sound sensing units, and vibration sensing units. These sensing units each undertake specific monitoring tasks and work together to provide comprehensive data on the motor's operating status. The primary responsibility of the temperature sensing unit is to monitor temperature changes during motor operation. By directly placing the temperature sensor on key parts of the motor, it can capture temperature fluctuations caused by load changes, poor heat dissipation, or other thermal faults in real time. Acquiring temperature sensing signals is crucial for the early detection of motor overheating problems, as overheating is often a precursor to issues such as insulation aging and winding damage.

[0102] The sound sensing unit focuses on capturing the sound signals generated by the motor during operation. The sound characteristics of a motor differ significantly between normal operation and fault conditions. Sound sensors can detect abnormal sounds, such as high-frequency noise caused by bearing wear or electromagnetic noise due to uneven air gap. The acquisition and analysis of sound sensing signals provide crucial information for motor fault diagnosis, helping technicians quickly locate potential problems.

[0103] The vibration sensing unit is one of the core components for monitoring the mechanical condition of a motor. During operation, motors vibrate to varying degrees due to imbalance, misalignment, bearing failure, and other reasons. The vibration sensing unit can monitor the vibration amplitude and frequency characteristics of the motor in real time, converting the vibration signal into an electrical signal and transmitting it to the auxiliary acquisition module. By analyzing the vibration signal, it is possible to accurately determine whether there is a mechanical fault in the motor, providing data support for timely maintenance measures.

[0104] In practical applications, these three sensing units work closely together to comprehensively monitor the mechanical condition of the motor. The temperature, sound, and vibration sensing units collect temperature, sound, and vibration signals respectively, and then integrate these signals into a second type of data. This integration not only improves the efficiency of data acquisition but also ensures the integrity and consistency of the data, providing a reliable foundation for subsequent fault diagnosis and analysis. By integrating multiple sensor signals into a unified data format, the auxiliary equipment can process and transmit data more efficiently, ultimately achieving a comprehensive assessment of the motor's operating status.

[0105] This integrated design of the auxiliary acquisition module significantly enhances the flexibility and adaptability of the motor diagnostic system. In different application scenarios, the auxiliary module can flexibly adjust its monitoring focus according to actual needs. For example, in temperature-sensitive motor applications, the data acquisition frequency and accuracy of the temperature sensing unit can be increased; while in environments where vibration is a significant issue, the focus can be on data analysis from the vibration sensing unit. This flexibility allows the auxiliary module to better adapt to various complex industrial environments and meet diverse monitoring needs.

[0106] Reference Figure 3 This diagram illustrates the architecture for diagnosing motor equipment using a motor diagnostic component, which is divided into an extension layer, a data acquisition and application layer, an auxiliary data acquisition module, and a device layer.

[0107] The expansion layer includes computer equipment, indicating that the motor diagnostic component supports connection and data transfer with external computer devices. Remote data transmission and analysis can be achieved via Ethernet cable or Wi-Fi, expanding the functionality and application scenarios of the diagnostic component. This is particularly useful when complex data analysis or remote monitoring is required.

[0108] At the data acquisition and application layer, the motor diagnostic component possesses independent data acquisition, storage, and display capabilities, eliminating the need for external computer equipment. This demonstrates the portability and self-sufficiency of the motor diagnostic component, making it particularly suitable for environments without network communication and meeting the needs of rapid on-site diagnostics. Furthermore, the motor diagnostic component is powered by a built-in battery, an external battery, or an external power adapter, ensuring stable and flexible power supply to adapt to various on-site conditions. Moreover, the motor diagnostic component features a built-in 10.1-inch display screen, facilitating direct viewing of data and diagnostic results on-site, thus enhancing ease of use.

[0109] The auxiliary data acquisition module integrates temperature, sound, and vibration sensors. These units are responsible for collecting the motor's temperature, sound, and vibration signals and combining them into secondary data. This demonstrates that the auxiliary data acquisition module has the capability to comprehensively monitor the motor's mechanical state, capturing changes in various mechanical parameters during motor operation and providing rich data support for fault diagnosis.

[0110] At the equipment level, it's the motor itself. This is the object of monitoring for the entire diagnostic system. The various components revolve around the motor, ensuring the accuracy and relevance of the monitoring data.

[0111] According to some embodiments of this application, a first auxiliary machine transmission path and a second auxiliary machine transmission path are provided between the auxiliary machine acquisition module and the auxiliary machine computer board.

[0112] It should be noted that the connection between the auxiliary acquisition module and the auxiliary computer board is designed with two transmission paths: a first auxiliary transmission path and a second auxiliary transmission path. This design enhances the flexibility and adaptability of data transmission to meet the data transmission needs of different operating modes. The first and second auxiliary transmission paths exist in parallel, providing multiple possible routing options for data transmission and ensuring its reliability and efficiency.

[0113] According to some embodiments of this application, a first auxiliary transmission path is provided with a first node, and a second auxiliary transmission path is provided with a second node. The first node and the second node are used to connect to the host computer board.

[0114] It should be noted that the first auxiliary machine has a first node on its transmission path, while the second auxiliary machine has a second node on its transmission path. These two nodes perform crucial connection functions, allowing them to connect to the host computer board. This design enables the auxiliary machine to communicate and exchange data with the host, allowing the host to access and integrate data collected by the auxiliary machine. When the host needs to obtain data collected by the auxiliary machine, it can establish a connection with the auxiliary machine through these two nodes, thereby achieving coordinated data transmission.

[0115] According to some embodiments of this application, a normally closed switch is provided between the second node and the auxiliary computer board on the second auxiliary machine transmission path. The normally closed switch is configured to disconnect when the host data transmission module and the auxiliary data transmission module are connected.

[0116] It should be noted that a normally closed switch is installed between the second node and the auxiliary computer board on the second auxiliary machine's transmission path. This normally closed switch remains closed under normal circumstances, keeping the second auxiliary machine's transmission path connected. However, when the host data transmission module connects to the auxiliary data transmission module, the normally closed switch is triggered and disconnects. This design ensures effective control of data flow during data transmission between the host and auxiliary machines, avoiding data conflicts and interference. In this way, the host can smoothly take over the data collected by the auxiliary machine, enabling data integration and analysis.

[0117] This design not only enhances the collaborative working capability between the main unit and auxiliary units but also improves the reliability and adaptability of the entire motor diagnostic component. Through flexible transmission paths and intelligent switching control, the data transmission method can be automatically adjusted according to the actual operating conditions, ensuring accurate data transmission and efficient processing. This flexible transmission mechanism provides strong support for the stable operation of the motor diagnostic component in different application scenarios.

[0118] Reference Figure 4This exhibit showcases the internal structure of the main unit and auxiliary unit, along with their interconnections, detailing the data transmission paths and the interactions between key components. The main unit comprises a main unit acquisition module and a main unit computer board containing a main unit storage module (hard drive), while the auxiliary unit comprises an auxiliary unit acquisition module and an auxiliary unit computer board containing an auxiliary unit storage module (hard drive). The acquisition modules of the main unit and auxiliary unit are connected to their respective computer boards, each containing a hard drive. This design enables both the main unit and auxiliary unit to independently complete data acquisition, processing, and storage.

[0119] The connection between the host and the auxiliary unit is achieved through two data transmission paths. The first path, called the first auxiliary unit transmission path, connects the auxiliary unit's acquisition module and the auxiliary unit's computer board, and includes a first node. The second path, called the second auxiliary unit transmission path, also connects the auxiliary unit's acquisition module and the auxiliary unit's computer board, but includes a second node. This design of two paths ensures the flexibility and reliability of data transmission and provides a foundation for subsequent collaborative work between the host and the auxiliary unit.

[0120] On the second auxiliary machine's transmission path, a normally closed switch is installed between the second node and the auxiliary machine's computer board. Under normal circumstances, this normally closed switch remains closed, keeping the second auxiliary machine's transmission path open, allowing the auxiliary machine to independently complete data acquisition, processing, and storage. However, when the host data transmission module connects to the auxiliary machine's data transmission module, the normally closed switch will disconnect, cutting off the second auxiliary machine's transmission path. This design ensures effective control of data flow during data transmission between the host and auxiliary machines, avoiding data conflicts and interference.

[0121] When the transmission modules of the host and auxiliary units are connected, the data collected by the auxiliary unit can be transmitted to the host computer board through the first auxiliary unit transmission path, thereby enabling the host to integrate and analyze the data collected by the auxiliary unit. This design not only enhances the collaborative working capability between the host and auxiliary units, but also improves reliability and adaptability, ensuring the efficiency and accuracy of data transmission.

[0122] Reference Figure 5 According to the motor diagnostic method of this application embodiment, applied to the motor diagnostic component of any one of the embodiments of this application, the motor diagnostic method of this application embodiment may include:

[0123] Step S501: The host and auxiliary machines of the motor diagnostic component are connected through the host data transmission module and the auxiliary data transmission module, and a reference time synchronization operation is performed on the electrical and mechanical equipment in the target power plant to align the acquisition reference time of the electrical and mechanical equipment.

[0124] Step S502: During the operation of the target motor, the control host acquires the first type of signal according to the acquisition reference time to obtain the first type of device signal;

[0125] Step S503: During the operation of the target motor, the auxiliary machine is controlled to acquire the second type of signal according to the acquisition reference time to obtain the second type of equipment signal;

[0126] Step S504: The signal of the second type of equipment is transmitted to the host data transmission module via the auxiliary data transmission module;

[0127] Step S505: In the host, align the timestamp information of the first type of device signal and the second type of device signal to obtain the first type parameter and the second type parameter;

[0128] Step S506: Perform motor diagnostic operation based on the first type parameter and the second type parameter to obtain motor diagnostic results corresponding to the target motor.

[0129] The motor diagnostic method of this application is a systematic, efficient, and accurate diagnostic process, specifically tailored for the motor diagnostic components proposed in this application. It aims to comprehensively monitor and analyze the operating status of electrical and mechanical equipment in a power plant environment. The method begins with the connection and reference time synchronization between the main unit and auxiliary units, progressively advancing to signal acquisition, data transmission, timestamp alignment, and finally, the motor diagnostic operation, ensuring the accuracy and reliability of the diagnostic results.

[0130] In some embodiments, step S501 connects the host and auxiliary units of the motor diagnostic component through the host data transmission module and the auxiliary data transmission module, and performs reference time synchronization operation on the electrical and mechanical equipment in the target power plant to align the acquisition reference time of the electrical and mechanical equipment.

[0131] It should be noted that a stable connection is established between the main unit and the auxiliary unit through the main unit data transmission module and the auxiliary unit data transmission module. This connection is not only a physical link, but also a bridge for data transmission and synchronization. Performing reference time synchronization on the electrical and mechanical equipment in the target power plant is a key step to ensure that all monitoring equipment operates within the same time frame. Through reference time synchronization, the data acquisition reference time of the electrical and mechanical equipment is aligned, laying the foundation for subsequent timestamp alignment of data and ensuring the comparability and correlation of data from different sources on the same timeline.

[0132] In some more specific embodiments, the process of how the master and slave devices perform timing marking after the reference time synchronization operation is as follows:

[0133]

[0134] It should be noted that the timing mark is defined as the device marking the time each time a minimum reference pulse occurs. This process begins with the initial time synchronization, when the timing mark is 1, corresponding to the host's time. Ds and auxiliary machine time Js As monitoring progresses, whenever a minimum reference pulse is detected, the two units, according to their respective time stamps, are sequentially recorded by the host as time2. D time3 D ...time i D time END D The auxiliary machine records the mechanical acquisition time as time2. J time3 J ...time i J time END J Each time stamp corresponds to a specific moment, and these moments form the basis of the acquisition time sequence.

[0135] In some embodiments, to ensure data accuracy after monitoring is completed, the electrical and mechanical equipment can be synchronized a second time via USB or Bluetooth. In this case, the timing marker is updated to the endpoint acquisition timing, with the host's endpoint acquisition timing marker being N, the auxiliary device's endpoint acquisition timing marker being n, and the host's electrical acquisition time corresponding to the endpoint acquisition timing being marked as time. END D The electrical acquisition time corresponding to the timing sequence of the auxiliary machine at the endpoint is marked as time. END J This operation ensures that the time base of the two monitoring units remains consistent even in environments where communication is impossible, providing a reliable time reference for subsequent data alignment and analysis.

[0136] In this way, the main unit and auxiliary unit can independently perform timing marking while maintaining time consistency through initial and final synchronization. This not only solves the synchronization problem of data acquisition in environments without communication networks but also provides the necessary timing information for subsequent offset calculation and data alignment. The table above clearly shows the relationship between the timing markings of the electrical and auxiliary units and their corresponding times, making the process of determining the acquisition timing more transparent and easier to understand.

[0137] In some embodiments, steps S502 to S503 involve the following steps: during the operation of the target motor, the control host acquires a first type of signal according to the acquisition reference time to obtain a first type of device signal; during the operation of the target motor, the control auxiliary machine acquires a second type of signal according to the acquisition reference time to obtain a second type of device signal.

[0138] It should be noted that during the operation of the target motor, the main unit and auxiliary unit respectively acquire Type I and Type II signals according to a pre-set acquisition reference time. The main unit focuses on acquiring Type I equipment signals from the electrical equipment, which may include key electrical parameters such as current and voltage; while the auxiliary unit is responsible for acquiring Type II equipment signals from the mechanical equipment, such as mechanical parameters like vibration and temperature. In this way, the motor diagnostic component can simultaneously acquire comprehensive electrical and mechanical data on motor operation, providing a detailed data foundation for subsequent integrated analysis.

[0139] According to some embodiments of this application, the first type of device signal is an electrical device signal, and the second type of signal is a mechanical device signal. Step S502, where the control host acquires the first type of signal according to the acquisition reference time to obtain the first type of device signal, may include:

[0140] The control host acquires signals from the electrical equipment corresponding to the target motor according to the acquisition reference time, and obtains the electrical equipment signals; among them, the timestamp information corresponding to the electrical equipment signals is electrical timestamp information;

[0141] The auxiliary control unit performs second-type signal acquisition according to the acquisition reference time to obtain second-type equipment signals, which may include:

[0142] The control host acquires signals from the mechanical equipment corresponding to the target motor according to the acquisition reference time, and obtains the mechanical equipment signals; among them, the timestamp information corresponding to the mechanical equipment signals is the mechanical timestamp information.

[0143] It should be noted that the host computer acquires signals from the electrical equipment corresponding to the target motor according to the acquisition reference time, thereby obtaining the electrical equipment signals. The key to this step is that the host computer must adhere to the acquisition reference time determined by the previous reference time synchronization. This ensures that the acquired electrical equipment signals are not only comparable in time but also can be integrated and analyzed on a unified timeline with data acquired from other devices. The timestamp information corresponding to the electrical equipment signals is called electrical timestamp information. It accurately records the acquisition time of each electrical signal data point, providing a precise time reference for subsequent data processing and fault diagnosis.

[0144] Similarly, in step S503, the auxiliary machine is controlled to perform the second type of signal acquisition according to the acquisition reference time to obtain the mechanical equipment signal. This step complements the main machine's signal acquisition process, and the auxiliary machine also performs its signal acquisition task based on a unified acquisition reference time. The timestamp information corresponding to the mechanical equipment signal is called mechanical timestamp information, which also serves to record the data acquisition time, ensuring the traceability and synchronization of the mechanical equipment signal in time. By accurately timestamping the mechanical equipment signal, this embodiment of the application can ensure that in the subsequent data integration and analysis process, the mechanical equipment signal can be accurately mapped to the same time point as the electrical equipment signal, thereby achieving a comprehensive and accurate assessment of the motor's operating status.

[0145] As can be seen, by introducing a unified acquisition reference time and corresponding timestamp information during the signal acquisition process of the host and auxiliary machines, the motor diagnosis method of this application embodiment can effectively ensure the time consistency and accuracy of the acquired data, providing a solid data foundation for subsequent data integration and fault diagnosis. This time synchronization and data annotation method not only improves the reliability of the diagnostic results, but also enhances the real-time performance and accuracy of motor operating status monitoring.

[0146] In some embodiments, step S504 involves transmitting the second type of device signal to the host data transmission module via the auxiliary data transmission module.

[0147] It should be noted that the signals from the second type of equipment are transmitted to the main data transmission module via the auxiliary machine data transmission module. This process is a crucial step in the motor diagnostic component's data integration. Through data transmission, the main unit can acquire the mechanical equipment signals collected by the auxiliary machine, thereby achieving comprehensive monitoring of the motor's operating status. The stability and efficiency of data transmission directly affect the reliability and speed of the entire diagnostic process.

[0148] In step S505 of some embodiments, the timestamp information of the first type device signal and the second type device signal are aligned in the host to obtain the first type parameter and the second type parameter;

[0149] It should be noted that the timestamp information of the first type of device signals and the second type of device signals is aligned in the host to obtain the first type of parameters and the second type of parameters. Timestamp alignment is a core step in ensuring data accuracy and correlation. By precisely aligning different types of signal data on the timeline, the host can ensure the correspondence between electrical and mechanical parameters at the same point in time, thereby enabling subsequent diagnostic analysis to be based on synchronized data and improving the accuracy and reliability of diagnostic results.

[0150] Reference Figure 6According to some embodiments of this application, step S505, which aligns the timestamp information of the first type device signal and the second type device signal to obtain the first type parameter and the second type parameter, may include:

[0151] Step S601: Based on the electrical timestamp information and the mechanical timestamp information, determine the electrical acquisition time and the mechanical acquisition time corresponding to each acquisition sequence;

[0152] Step S602: For each acquisition timing sequence, calculate the time offset between the electrical acquisition time and the mechanical acquisition time.

[0153] Step S603: Based on the time offset value corresponding to each acquisition time sequence, determine the deviation correction factor corresponding to each acquisition time sequence;

[0154] Step S604: Based on the deviation correction factor corresponding to each acquisition time sequence, perform a timestamp alignment operation on the electrical timestamp information and the mechanical timestamp information in each acquisition time sequence to obtain the first type parameter and the second type parameter.

[0155] In some embodiments of this application, step S505 involves aligning the timestamp information of a first type of device signal (electrical device signal) and a second type of device signal (mechanical device signal) to obtain first-type parameters and second-type parameters that can be used for comprehensive analysis. The key to this process is ensuring that the timestamps of the two different types of signals can be compared and integrated on a unified timeline, thereby providing an accurate data foundation for subsequent motor diagnostics.

[0156] In some embodiments, step S601 determines the electrical acquisition time and mechanical acquisition time corresponding to each acquisition sequence based on electrical timestamp information and mechanical timestamp information.

[0157] It should be noted that, based on electrical and mechanical timestamp information, the electrical and mechanical acquisition times corresponding to each acquisition sequence are determined. This step is fundamental to time alignment. By accurately recording the acquisition time of each signal data point, this embodiment of the application can establish the time correspondence between electrical and mechanical signals. The electrical and mechanical timestamp information correspond to the acquisition times of electrical equipment signals and mechanical equipment signals, respectively, providing necessary data support for subsequent time offset calculation and correction.

[0158] In step S602 of some embodiments, the time offset value between the electrical acquisition time and the mechanical acquisition time is calculated sequentially for each acquisition timing sequence;

[0159] It should be noted that, for each acquisition sequence, the time offset between the electrical and mechanical acquisition times is calculated sequentially. This step quantifies the time difference between the electrical and mechanical signals by comparing their acquisition times under the same acquisition sequence. The time offset reflects the degree of misalignment between the two signals in time and is a key parameter for timestamp alignment. Calculating the time offset requires high-precision time measurement and computational capabilities to ensure its accuracy.

[0160] In step S603 of some embodiments, a deviation correction factor is determined for each acquisition time sequence based on the time offset value corresponding to each acquisition time sequence.

[0161] It should be noted that a deviation correction factor is determined for each acquisition time sequence based on the time offset value corresponding to each acquisition time sequence. The deviation correction factor is an important parameter used to adjust timestamp differences; it is calculated based on the time offset value and is used to correct timestamp information in subsequent steps. Determining the deviation correction factor requires comprehensive consideration of the offsets of multiple acquisition time sequences to ensure the accuracy and reliability of the correction.

[0162] Reference Figure 7 According to some embodiments of this application, step S603, based on the time offset value corresponding to each acquisition time sequence, determines the deviation correction factor corresponding to each acquisition time sequence, which may include:

[0163] Step S701: Based on each acquisition time sequence and the time offset value corresponding to each acquisition time sequence, establish offset value vector mapping data; wherein, the offset value vector mapping data is used to reflect the relationship between the time offset value and the acquisition time sequence.

[0164] Step S702: Calculate the vector change rate based on the offset value vector mapping data to determine the offset value change rate corresponding to each acquisition time sequence;

[0165] Step S703: Configure the deviation correction factor corresponding to each acquisition time sequence according to the rate of change of the offset value of each acquisition time sequence.

[0166] In step S701 of some embodiments, offset value vector mapping data is established based on each acquisition time sequence and the time offset value corresponding to each acquisition time sequence; wherein, the offset value vector mapping data is used to reflect the relationship between the time offset value and the acquisition time sequence.

[0167] It's important to note that this process first requires establishing offset vector mapping data. This step involves associating each acquisition time sequence with its corresponding time offset value, forming a vector mapping data structure. This structure clearly reflects how the time offset value changes as the acquisition time sequence changes, providing an intuitive data foundation for subsequent analysis. Through this mapping relationship, it's easier to identify the trend and pattern of offset value changes, which is crucial for understanding the time differences between electrical and mechanical signals.

[0168] In some embodiments, step S702 involves calculating the vector change rate based on the offset value vector mapping data to determine the offset value change rate corresponding to each acquisition time sequence.

[0169] It should be noted that, next, based on the established offset vector mapping data, the vector change rate is calculated to determine the offset change rate corresponding to each acquisition time sequence. Calculating the vector change rate involves analyzing the magnitude and direction of the offset value change between adjacent acquisition time sequences, thereby quantifying the rate of change of the offset value. This step is indicative of abrupt or gradual changes in the offset pattern, as different change rates may indicate different system behaviors or the impact of external disturbances.

[0170] In step S703 of some embodiments, a deviation correction factor is configured for each acquisition time sequence based on the rate of change of the offset value of each acquisition time sequence.

[0171] It should be noted that a deviation correction factor is configured for each acquisition time series based on the rate of change of its offset value. The configuration of the deviation correction factor aims to provide a suitable correction amount for each acquisition time series by taking into account the rate of change of the offset value, so that it can be applied in subsequent timestamp alignment operations. This step ensures that the correction factor can dynamically adapt to changes in the offset value, thereby improving the accuracy and reliability of timestamp alignment. In this way, electrical and mechanical monitoring data can be aligned more accurately, providing a more precise data foundation for assessing the operational status of power plant equipment.

[0172] Reference Figure 8 According to some embodiments of this application, step S703, which configures the deviation correction factor corresponding to each acquisition time sequence based on the rate of change of the offset value of each acquisition time sequence, may include:

[0173] Step S801: Perform abrupt change calculation based on the rate of change of offset values ​​for each acquisition time series;

[0174] Step S802: In response to the determination of a sudden change in the rate of change of offset value in the mutation calculation, the offset value vector mapping data is sliced ​​based on the acquisition time series where the mutation occurred to obtain offset value slice data; wherein, the offset value slice data contains multiple offset value sub-data, and each offset value sub-data corresponds to an acquisition time series;

[0175] Step S803: Calculate the acquisition deviation ratio corresponding to the offset slice data;

[0176] Step S804: Based on the acquisition deviation ratio, configure the deviation correction factor corresponding to each acquisition time sequence in the offset value slice data.

[0177] In some embodiments, step S801 involves calculating abrupt changes based on the rate of change of offset values ​​for each acquisition time sequence.

[0178] It should be noted that the deviation correction factor for each acquisition time series is configured based on the rate of change of the offset values ​​for each acquisition time series. This process first includes abrupt change calculation based on the rate of change of the offset values ​​for each acquisition time series. The purpose of abrupt change calculation is to identify whether there are significant abrupt changes in the rate of change of offset values. These abrupt changes may indicate that the pattern of offset value change has changed. By identifying these abrupt changes, the characteristics of offset value change over time can be understood more accurately.

[0179] Reference Figure 9 According to some embodiments of this application, step S801, which calculates abrupt changes based on the rate of change of offset values ​​for each acquisition time series, may include:

[0180] Step S901: Determine the cumulative total offset value based on the time offset value corresponding to each acquisition time sequence; wherein, the number of acquisition time sequences is the second number;

[0181] Step S902: Based on the cumulative total offset value and the second number, set the mutation definition conditions;

[0182] Step S903: In response to the existence of an offset value change rate that satisfies the mutation definition condition, determine that the corresponding offset value change rate has undergone a mutation.

[0183] In some embodiments, step S901 involves determining the cumulative total offset value based on the time offset value corresponding to each acquisition time sequence; wherein the number of acquisition time sequences is a second number.

[0184] It should be noted that the cumulative total offset value is determined based on the time offset value corresponding to each acquisition time series. The number of acquisition time series is defined as the second number, representing the total number of data points acquired during the monitoring process. The cumulative total offset value not only reflects the sum of time offset values ​​across all acquisition time series but also provides important quantitative basis for subsequently setting abrupt change criteria.

[0185] According to some embodiments of this application, step S901, based on the time offset value corresponding to each acquisition time sequence, determines the cumulative total offset value, which may include:

[0186] Determine the endpoint acquisition sequence from each acquisition sequence;

[0187] The time offset value corresponding to the endpoint acquisition time sequence is determined as the cumulative total offset value.

[0188] It should be noted that the endpoint acquisition sequence is identified from all acquisition sequence numbers. The endpoint acquisition sequence refers to the last acquisition sequence in a series of acquisition sequence numbers, representing the end point of the monitoring process. By determining this endpoint, it can be ensured that the calculation of the cumulative total offset value covers the entire monitoring period, thus providing a complete picture of the accumulated offset.

[0189] Next, the time offset value corresponding to this endpoint acquisition time sequence is directly determined as the cumulative total offset value. This method assumes that during the monitoring process, the time offset value of each acquisition time sequence is calculated relative to the same reference time. Therefore, the offset value of the endpoint acquisition time sequence naturally reflects the total time offset from the start to the end of monitoring. This approach not only simplifies the calculation process but also effectively reflects the cumulative offset effect throughout the entire monitoring period.

[0190] Determining the cumulative total offset in this way not only provides an intuitive measure of accumulated offset but also lays the foundation for setting subsequent abrupt change criteria. As a key indicator, the cumulative total offset helps the monitoring system identify whether the offset exceeds the normal range, thereby promptly detecting potential system anomalies or equipment failures. This endpoint-based acquisition time-series method ensures that the calculation of the cumulative total offset is both accurate and efficient, making it suitable for various complex monitoring scenarios.

[0191] In some more specific embodiments, the host sequentially records the electrical acquisition times as time2. D time3 D ...time i D time END D The auxiliary machine records the mechanical acquisition time as time2. J time3 J ...time i J time END J The time offset value corresponding to each acquisition time sequence refers to the deviation between the electrical acquisition time and the mechanical acquisition time corresponding to the same sequence mark, denoted as time. D -timeJ The example given here is the case where the electrical acquisition time is faster than the mechanical acquisition time.

[0192] Based on this, the time offset value corresponding to the endpoint acquisition time sequence is expressed as:

[0193] Z = time END D -time END J

[0194] In some embodiments, step S902 sets a mutation boundary condition based on the cumulative total offset value and the second number.

[0195] It should be noted that a mutation threshold is set based on the cumulative total offset and the number of data acquisition sequences. Setting this threshold is a crucial step in mutation detection, as it determines under what circumstances the rate of change in the offset is considered a mutation. When setting the mutation threshold, both the magnitude of the cumulative total offset and the number of data acquisition sequences need to be considered to determine a reasonable threshold. This threshold reflects the expected range of the rate of change in the offset under normal circumstances; any rate of change exceeding this range will be considered abnormal.

[0196] In some embodiments, step S903 involves determining that a mutation has occurred in the corresponding rate of change of the offset value in response to the existence of an offset value change rate that satisfies the mutation definition condition.

[0197] It should be noted that, in response to situations where the rate of change of the offset value meets the abrupt change definition condition, a sudden change in the corresponding rate of change of the offset value is determined. This means that when the monitored rate of change of the offset value exceeds a previously set threshold, this embodiment of the application will determine that a sudden change has occurred at that acquisition time. This determination is crucial for the timely detection of abnormal changes in the system, as it may indicate changes in the device's operating state or other events requiring attention. In this way, abrupt change calculation not only improves monitoring sensitivity but also enhances the responsiveness to changes in the device's operating state.

[0198] Reference Figure 10 According to some embodiments of this application, step S903, in response to the existence of an offset value change rate satisfying a mutation definition condition, determining that the corresponding offset value change rate has undergone a mutation, may include:

[0199] Step S1001: Calculate the mutation boundary value based on the cumulative total offset value and the second number;

[0200] Step S1002: Compare the rate of change of the offset value of the acquisition time series with the mutation boundary value;

[0201] Step S1003: If the rate of change of the offset value exceeds the mutation threshold, it is determined that a mutation has occurred in the rate of change of the offset value.

[0202] In step S1001 of some embodiments, a mutation boundary value is calculated based on the cumulative total offset value and the second number;

[0203] It should be noted that the mutation threshold is calculated based on the cumulative total offset and the second number (i.e., the total number of acquisition time series). The cumulative total offset reflects the sum of the offset values ​​at all acquisition time series, while the second number represents the number of data points acquired. By dividing the cumulative total offset by the second number, an average offset can be obtained. This average value serves as the mutation threshold, used to determine whether the rate of change of the offset value in a single acquisition time series exceeds the normal range. If the rate of change of the offset value exceeds the mutation threshold, it means that the rate of change of the offset value meets the mutation threshold condition.

[0204] In some more specific embodiments, the mutation threshold can be determined by multiplying the mutation coefficient by the average offset. The core of this approach is the introduction of a mutation coefficient, typically set greater than 1.1, to ensure that the mutation threshold effectively identifies significant shifts. Specifically, the mutation coefficient is set to amplify the average offset, thereby providing a more sensitive threshold for mutation detection.

[0205] First, the average offset is calculated statistically from the time offset values ​​of all acquisition time series, reflecting the average level of offset during the monitoring process. The method for calculating the average offset typically involves dividing the cumulative total offset value by the number of acquisition time series, i.e., the second number. This average value provides the basis for subsequent abrupt change thresholds.

[0206] Next, by multiplying the mutation coefficient by the average offset, the mutation threshold value can be obtained. This method, based on the mutation coefficient and average offset, has the advantage of dynamically adapting to different monitoring environments and data characteristics. By adjusting the mutation coefficient, the monitoring system can flexibly respond to various situations. For example, in monitoring scenarios sensitive to mutations, the mutation coefficient can be appropriately increased to reduce false alarms; while in scenarios requiring more stringent mutation detection, the mutation coefficient can be appropriately decreased. This method not only improves the flexibility of mutation detection but also enhances adaptability and robustness.

[0207] In some more specific embodiments, if the cumulative total offset is represented by Z and the second number by n, then the average offset can be represented as... If the mutation coefficient is denoted as r, then the mutation threshold can be expressed as: If the rate of change of the offset value is expressed as Therefore, the mutation threshold can be expressed as:

[0208]

[0209] If the rate of change of the offset value Exceeding the mutation threshold This confirms that a sudden change has occurred in the rate of change of the offset value.

[0210] The mutation coefficient is typically set to a value greater than 1.1, meaning the mutation threshold is slightly higher than the average offset. This setting ensures that a mutation is only considered to have occurred when the rate of change of the offset significantly exceeds the average level. For example, if the average offset is 10 units and the mutation coefficient is set to 1.2, then the mutation threshold is 12 units. Any rate of change of the offset exceeding this value will be considered a mutation.

[0211] In step S1002 of some embodiments, the rate of change of the offset value of the acquisition time series is compared with the mutation boundary value;

[0212] It should be noted that the rate of change of the offset value for each acquisition time series is compared with this mutation threshold. This step is achieved by comparing each rate of change with the mutation threshold. If the rate of change of the offset value for a certain acquisition time series exceeds the mutation threshold, it indicates that the rate of change is significantly higher than the average offset level, which may mean that a mutation has occurred at that acquisition time series.

[0213] In step S1003 of some embodiments, if the rate of change of the offset value exceeds the mutation threshold, it is determined that a mutation has occurred in the rate of change of the offset value.

[0214] It should be noted that if the rate of change of the offset value exceeds the abrupt change threshold, then an abrupt change in the rate of change of the offset value can be determined. This method of identifying abrupt changes helps the monitoring system to promptly identify abnormal changes in the offset pattern, thereby enabling timely implementation of appropriate measures. In this way, the monitoring system can detect potential equipment failures or operational anomalies at an early stage, providing strong support for the stable operation of power plant equipment.

[0215] According to some embodiments of this application, determining that a sudden change in the rate of change of the offset value has occurred if the rate of change of the offset value exceeds a mutation threshold may include:

[0216] Set a third number;

[0217] If the rate of change of the offset value corresponding to the third consecutive number of acquisition time sequences exceeds the mutation threshold, it is determined that a mutation has occurred in the rate of change of the offset value.

[0218] It's important to note that determining whether a sudden change in the offset rate of change has occurred involves setting a parameter called the "third number." The introduction of the third number aims to ensure the reliability and accuracy of mutation detection, avoiding misjudgments due to chance factors or short-term fluctuations. Specifically, the third number can be defined as a threshold number of consecutive acquisition time series. Only when the offset rate of change for a consecutive third number of acquisition time series exceeds the mutation threshold will a sudden change in the offset rate of change be definitively determined.

[0219] This method increases the rigor of mutation determination by requiring multiple consecutive acquisition time series to meet the exceedance condition. For example, if the third number is set to 3, then the mutation determination mechanism will only be triggered if the rate of change of the offset value of three consecutive acquisition time series exceeds the mutation threshold. This setting helps filter out offset value fluctuations caused by random noise or transient interference, thereby improving the accuracy of mutation detection.

[0220] Furthermore, the specific value of the third number can be adjusted according to the data characteristics and monitoring needs in the actual application scenario. In some monitoring scenarios with high real-time requirements, the third number can be appropriately reduced to detect potential mutations more quickly; while in scenarios with high data stability requirements, the third number can be appropriately increased to reduce the possibility of false alarms. In this way, the monitoring system can flexibly adjust the sensitivity and reliability of mutation detection in different application environments, thereby better meeting actual monitoring needs.

[0221] According to some specific embodiments of this application, setting a third number may include:

[0222] In the case where the mechanical equipment corresponds to the motor equipment, obtain the number of time pulses corresponding to one-quarter revolution of the bearing of the motor equipment;

[0223] Set a third number such that the third number is less than or equal to the number of time pulses corresponding to a quarter revolution of the bearing of the motor.

[0224] It should be noted that setting the third number involves careful consideration of the rotational characteristics of the motor's bearings. Specifically, when mechanical equipment corresponds to motor equipment, the number of time pulses corresponding to one-quarter revolution of the motor's bearing can be obtained first. This parameter is obtained based on the motor's rotational speed and the bearing's structural characteristics, reflecting the minimum time unit during normal operation of the motor. Accurately measuring or calculating this number of time pulses provides a crucial reference for setting the subsequent third number.

[0225] Next, when setting the third number, it should be ensured that it is less than or equal to the number of time pulses corresponding to one-quarter revolution of the motor bearing. This setting aims to ensure the timeliness and accuracy of abrupt change detection. If the third number is set too high, it may cause a lag in abrupt change detection, failing to capture changes in the offset pattern in a timely manner; conversely, if it is set too low, it may increase the risk of false alarms. Therefore, limiting the third number to within the number of time pulses corresponding to one-quarter revolution of the bearing can ensure the sensitivity of abrupt change detection while avoiding excessive false alarms due to overly frequent detection.

[0226] In some embodiments, the third number can be represented as m. Based on this, if the cumulative total offset is represented as Z and the second number as n, then the average offset can be represented as... If the mutation coefficient is denoted as r, then the mutation threshold can be expressed as: If the rate of change of the offset value is expressed as Therefore, the mutation boundary condition can be expressed as: m consecutive calculations

[0227] If the rate of change of the offset value is calculated through m consecutive calculations... The mutation threshold was exceeded in all m calculations. This confirms that a sudden change has occurred in the rate of change of the offset value.

[0228] According to some embodiments of this application, after step S801 performs abrupt change calculation based on the rate of change of offset values ​​for each acquisition time sequence, the method may further include:

[0229] In response to the fact that there was no sudden change in the rate of change of offset value in the mutation calculation, and that the rate of change of offset value is constant, the acquisition deviation ratio is calculated based on the offset value vector mapping data corresponding to each acquisition time sequence.

[0230] Based on the acquisition deviation ratio, configure the deviation correction factor corresponding to each acquisition time sequence in the offset value vector mapping data.

[0231] It should be noted that after calculating the abrupt changes in the rate of change of offset values ​​based on each acquisition time sequence, if the calculation results show that there are no abrupt changes in the rate of change of offset values, and it is further confirmed that the rate of change of offset values ​​remains constant throughout the monitoring process, then a different method can be used to calculate the acquisition deviation ratio, and the deviation correction factor can be configured accordingly. Specifically, when the rate of change of offset values ​​is constant, this indicates that the time offset between electrical and mechanical signals has not changed significantly during the monitoring period. Therefore, the offset value vector mapping data corresponding to all acquisition time sequences can be used to perform a more stable deviation ratio calculation.

[0232] In this context, the calculation of the acquisition bias ratio can be based on all available offset vector mapping data. This involves a comprehensive analysis of the offset values ​​across all acquisition time series to determine a holistic, stable bias ratio. Since there are no abrupt changes and the rate of change of the offset values ​​is constant, it is reasonable to assume that the offset is uniform throughout the monitoring period, making it possible to calculate the bias ratio using all data points. The bias ratio obtained in this way reflects the average bias throughout the monitoring process, thus providing a global basis for subsequent bias correction.

[0233] Next, based on the calculated acquisition deviation ratio, a deviation correction factor can be configured for each acquisition time sequence in the offset value vector mapping data. Since the rate of change of the offset value is constant, a uniform deviation correction factor can be applied to each acquisition time sequence, or it can be appropriately adjusted according to the specific offset value of each time sequence. This configuration method ensures that all acquired signals are consistently corrected throughout the entire monitoring data time range, eliminating time offsets caused by initial clock differences or other constant factors. Through this method, even in the absence of abrupt changes, precise time alignment of electrical and mechanical monitoring data can be achieved, thus providing a reliable data foundation for subsequent joint data analysis and equipment condition assessment.

[0234] In step S802 of some embodiments, in response to the determination of a sudden change in the rate of change of offset value in the mutation calculation, the offset value vector mapping data is sliced ​​based on the acquisition time sequence in which the mutation occurs to obtain offset value slice data; wherein, the offset value slice data contains multiple offset value sub-data, and each offset value sub-data corresponds to an acquisition time sequence;

[0235] It should be noted that, in response to a mutation, if a sudden change in the rate of change of the offset value is determined, the offset vector mapping data needs to be sliced ​​based on the acquisition time sequence at which the mutation occurred, resulting in offset slice data, which can be represented as Q. i Slicing divides the original offset vector mapping data into multiple sub-data segments, each called an offset sub-data, and each sub-data corresponds to a specific acquisition time sequence. This slicing process helps to break down the data into smaller, more manageable parts, enabling more detailed analysis and processing of each part.

[0236] In some embodiments, step S803 involves calculating the acquisition deviation ratio corresponding to the offset slice data;

[0237] It's important to note that the acquisition bias ratio is a key metric used to quantify the offset within each data slice. It reflects the degree of deviation of the offset value from a reference value at a specific acquisition time sequence. Methods for calculating the acquisition bias ratio may include statistical analysis, trend analysis, or other mathematical methods, depending on the characteristics of the data and the analysis objectives.

[0238] Reference Figure 11 According to some embodiments of this application, step S803, calculating the acquisition deviation ratio corresponding to the offset slice data, may include:

[0239] Step S1101: Obtain the mutation occurrence time sequence corresponding to the offset slice data;

[0240] Step S1102: Determine the corresponding first offset parameter based on the mutation occurrence time sequence; wherein, the first offset parameter is the time offset value between the electrical acquisition time and the mechanical acquisition time in the mutation occurrence time sequence;

[0241] Step S1103: Determine the corresponding second offset parameter based on the previous acquisition time sequence corresponding to the mutation occurrence time sequence; wherein, the second offset parameter is the time offset value between the electrical acquisition time and the mechanical acquisition time in the previous acquisition time sequence corresponding to the mutation occurrence time sequence;

[0242] Step S1104: Based on the first offset parameter and the second offset parameter, calculate the acquisition deviation ratio corresponding to the offset value slice data.

[0243] In some embodiments, step S1101 involves obtaining the mutation occurrence time sequence corresponding to the offset slice data;

[0244] It's important to note that obtaining the mutation occurrence timeline corresponding to the offset value slice data marks the moment when the rate of change of the offset value changes significantly. Determining the mutation occurrence timeline is based on the mutation calculation in the previous steps, and it helps identify the key points of the offset pattern transition. By identifying this timeline, we can more accurately analyze the changes in the offset value over different time periods.

[0245] In some embodiments, step S1102 involves determining a corresponding first offset parameter based on the timing of the mutation; wherein the first offset parameter is the time offset value between the electrical acquisition time and the mechanical acquisition time in the timing of the mutation.

[0246] It should be noted that the first offset parameter is determined based on the timing of the abrupt change. The first offset parameter is defined as the time offset between the electrical acquisition moment and the mechanical acquisition moment within the timing sequence of the abrupt change. This step quantifies the offset at the instant the abrupt change occurs, providing a crucial data point for subsequent deviation ratio calculations. The first offset parameter reflects the time difference between the electrical and mechanical signals at the time of the abrupt change.

[0247] In some embodiments, step S1103 involves determining a corresponding second offset parameter based on the previous acquisition timing corresponding to the mutation occurrence timing; wherein the second offset parameter is the time offset value between the electrical acquisition time and the mechanical acquisition time in the previous acquisition timing corresponding to the mutation occurrence timing.

[0248] It should be noted that the second offset parameter is determined based on the acquisition sequence preceding the mutation. The second offset parameter is the time offset between the electrical and mechanical acquisition times in the acquisition sequence before the mutation. The purpose of this step is to capture the offset before the mutation so that it can be compared with the offset after the mutation. By comparing the offset values ​​before and after the mutation, the magnitude and direction of the offset change can be assessed more accurately.

[0249] In some embodiments, step S1104 involves calculating the acquisition deviation ratio corresponding to the offset value slice data based on the first offset parameter and the second offset parameter.

[0250] It should be noted that the acquisition bias ratio corresponding to the offset value slice data is calculated based on the first and second offset parameters. The calculation method for the acquisition bias ratio typically involves comparing these two offset parameters, such as calculating their difference or ratio, to quantify the degree of change in the offset value before and after the abrupt change. This bias ratio provides a crucial basis for subsequently configuring the bias correction factor, helping to achieve more accurate timestamp alignment, thereby improving the accuracy and reliability of the monitoring data.

[0251] According to some embodiments of this application, the acquisition deviation ratio corresponding to the offset value slice data is calculated based on the first offset parameter and the second offset parameter, which may include:

[0252] The acquisition deviation ratio is obtained by performing quotient processing based on the first offset parameter and the second offset parameter.

[0253] It should be noted that calculating the acquisition deviation ratio involves quotienting the first and second offset parameters. Specifically, this quotient is achieved by dividing the first offset parameter by the second offset parameter, thus obtaining the acquisition deviation ratio. This ratio not only reflects the relative change in offset at the time of abrupt change but also provides a crucial quantitative indicator for subsequent deviation correction. This mathematical processing effectively converts the absolute value of offset change into a relative value, allowing for comparison of offset changes between different acquisition time series on a uniform scale. This quantification of relative change is significant for assessing and correcting time differences between electrical and mechanical monitoring data, as it helps identify key points of offset change and provides a basis for adjustment for each acquisition time series. Furthermore, the acquisition deviation ratio obtained through quotient calculation can also reveal trends in offset change, such as whether there are signs of acceleration or deceleration, thus providing a deeper insight into the operating status of the monitoring equipment.

[0254] In some more specific embodiments, the first offset parameter is expressed as The second offset parameter is expressed as: The sampling deviation ratio is expressed as:

[0255] In some embodiments, step S804 involves configuring a deviation correction factor for each acquisition timing sequence in the offset slice data based on the acquisition deviation ratio.

[0256] It should be noted that, based on the calculated acquisition deviation ratio, a deviation correction factor is configured for each acquisition time series in the offset value slice data. The configuration of the deviation correction factor aims to provide an appropriate correction amount for each acquisition time series to compensate for time differences caused by variations in the offset value. In this way, the accuracy of timestamp alignment can be improved, ensuring the temporal consistency of electrical and mechanical monitoring data, thereby providing a more accurate basis for subsequent joint data analysis and equipment condition assessment.

[0257] In some more specific embodiments, the offset slice data is represented as Q. i The sampling deviation ratio is expressed as The acquisition bias ratio can be directly determined as the bias correction factor w. Based on this, the offset slice data Q i The corresponding offset correction factor can be expressed as:

[0258]

[0259] If the rate of change of the offset value changes abruptly at the i-th acquisition time sequence, then the i-th acquisition time sequence is the time sequence in which the abrupt change occurs, and the (i-1)-th acquisition time sequence, which is also the time sequence in which the abrupt change occurs, corresponds to the previous acquisition time sequence. This represents the electrical acquisition time corresponding to the i-th acquisition time sequence after calculation. This represents the mechanical acquisition time corresponding to the i-th acquisition timing after calculation. This indicates the electrical acquisition time corresponding to the (i-1)th acquisition sequence after calculation. This indicates the mechanical acquisition time corresponding to the (i-1)th acquisition sequence after calculation.

[0260] It should be understood that the deviation correction factor w is equal to the ratio of the acquisition deviation. Closely related, the acquisition deviation ratio in the above embodiments It can be directly identified as the deviation correction factor w. In other embodiments, the deviation correction factor w can also be expressed in combination with other parameters based on the deviation correction factor, and is not limited to the examples mentioned above.

[0261] In some embodiments, step S604 involves performing a timestamp alignment operation on the electrical timestamp information and the mechanical timestamp information in each acquisition time sequence based on the deviation correction factor corresponding to each acquisition time sequence.

[0262] It should be noted that, based on the deviation correction factor corresponding to each acquisition time sequence, a timestamp alignment operation is performed on the electrical and mechanical timestamp information at each acquisition time sequence. This step is the core of the entire monitoring method; by aligning the timestamps, the consistency of electrical and mechanical signals in time can be ensured. This not only solves the signal asynchrony problem caused by differences in equipment clock pulses, but also provides accurate data support for subsequent joint data analysis and equipment operating status assessment. Through this method, effective synchronous acquisition and monitoring of electrical and mechanical signals of power plant equipment can be achieved even in environments without communication networks, thereby improving the accuracy and reliability of equipment operating status assessment.

[0263] In some more specific embodiments, the offset slice data is represented as Q. i The deviation correction factor is denoted as w. When the electrical acquisition time is faster than the mechanical acquisition time, the offset value slice data Q is... i The electrical and mechanical timestamp information within the data undergoes timestamp alignment at each acquisition time sequence, which can be represented as:

[0264] time J =w·time J

[0265] The surface mechanical timestamp information is corrected to align with the electrical timestamp information under the action of the deviation correction factor w.

[0266] It should be understood that timestamp alignment operations can be performed in various ways, and are not limited to the examples mentioned above.

[0267] In some embodiments, step S506 involves performing a motor diagnostic operation based on a first type parameter and a second type parameter to obtain a motor diagnostic result corresponding to the target motor.

[0268] It should be noted that, based on the aligned first and second type parameters, the host performs motor diagnostic operations to obtain motor diagnostic results corresponding to the target motor. The diagnostic analysis module utilizes these parameters, combined with an expert knowledge base and preset diagnostic algorithms, to comprehensively evaluate the motor's operating status. This process can identify potential fault modes, predict possible causes of failure, and provide corresponding maintenance suggestions, thereby helping maintenance personnel take timely measures to avoid production interruptions and economic losses caused by equipment failures.

[0269] The motor diagnostic method of this application embodiment can comprehensively and accurately monitor and diagnose the motor's operating status. From the connection and reference time synchronization of the host and auxiliary machine, to signal acquisition, data transmission, timestamp alignment, and finally the generation of the motor diagnostic results, each link is closely connected. Different types of signals are diagnosed and analyzed under a unified time axis, ensuring the efficiency of the diagnostic process and the reliability of the results.

[0270] Reference Figure 12 According to some embodiments of this application, the motor diagnostic method of this application may further include:

[0271] Step S1201: During the acquisition of the first type of signal and the acquisition of the second type of signal, data monitoring is performed on the acquired first type of device signal and second type of device signal.

[0272] Step S1202: In response to the abnormal signal detected in the data monitoring, attribution analysis is performed based on the abnormal signal to obtain abnormal situation information;

[0273] Step S1203: Based on the abnormal situation information, execute the corresponding situation response operation.

[0274] In some embodiments, step S1201 involves monitoring the acquired first-type device signals and second-type device signals during the acquisition of the first type of signal and the acquisition of the second type of signal.

[0275] It should be noted that, simultaneously with the acquisition of the first and second types of signals, data monitoring is performed on the acquired signals from both types of equipment. This step ensures that, during the data acquisition process, the embodiments of this application can monitor the acquired electrical and mechanical equipment signals in real time, so as to promptly detect any anomalies. Data monitoring not only helps to ensure the integrity and accuracy of the acquired data, but also enables the rapid identification of potential problems, preventing them from worsening.

[0276] In some embodiments, step S1202 involves responding to the discovery of an abnormal signal during data monitoring by performing attribution analysis based on the abnormal signal to obtain abnormal condition information.

[0277] It should be noted that attribution analysis is performed on abnormal signals detected during data monitoring to obtain detailed information about the anomaly. This process is crucial for timely and accurate identification of problems in motor operation. When the monitoring system detects an abnormal signal, this embodiment of the application immediately initiates the attribution analysis procedure. The core of this step lies in analyzing the characteristics of the abnormal signal, such as its amplitude, frequency, and waveform, to determine the nature and source of the anomaly.

[0278] It should be noted that attribution analysis can rely on the operating principles of the motor and the support of an expert knowledge base. The system compares the detected abnormal signals with known fault modes and uses the vast amount of experience and data stored in the expert knowledge base to perform in-depth analysis of the abnormal signals. Through this analysis, embodiments of this application can identify the potential causes behind the abnormal signals, such as whether they are caused by electrical faults, mechanical wear, imbalance, or other problems. The results of attribution analysis typically generate abnormal condition information, which details the type of abnormality, its location, and possible causes.

[0279] Furthermore, the attribution process may involve time-series analysis of anomalous signals to determine whether the anomaly is sudden or gradually developing. This time-dimensional analysis helps to further understand the development trend and urgency of the anomaly. In some cases, embodiments of this application may combine multiple anomalous signals for comprehensive analysis to reveal more complex failure modes. In this way, the attribution step not only improves the accuracy of diagnosis but also provides detailed information support for subsequent situation response operations.

[0280] In some embodiments, step S1203 involves performing a corresponding status response operation based on the abnormal status information.

[0281] It should be noted that step S1203 is a crucial step in the entire motor diagnostic process, aiming to translate the diagnostic results into practical maintenance or operational measures to address abnormal situations that occur during motor operation. The specific content and methods of the situation response operation will vary depending on the nature and severity of the abnormal situation information. Its core objective is to take timely and effective measures to prevent the further development of the fault and reduce the impact on equipment and production.

[0282] Specifically, after determining the type, location, and cause of the anomaly based on the abnormality information, corresponding operations will be executed according to a preset response strategy. For example, if the abnormality information indicates a minor overheating of the motor, the response operation might be to issue an alarm to notify maintenance personnel to inspect the motor, while simultaneously adjusting the motor's load or operating parameters to reduce the temperature. If the anomaly is more serious, such as detecting severe wear of the motor bearings or a short circuit in the windings, the response operation might include immediately shutting down the machine to prevent equipment damage, while simultaneously starting a backup motor to ensure production continuity. Furthermore, embodiments of this application may also automatically generate maintenance work orders, recording anomaly details and recommended repair measures, so that the maintenance team can take swift action.

[0283] To enable these response actions, motor diagnostic components are typically integrated with motor control systems and the plant's maintenance management system. This ensures that diagnostic results directly trigger corresponding control commands or workflows. For example, through integration with the motor control system, the diagnostic component can directly send a stop command when a serious anomaly is detected. Simultaneously, integration with the maintenance management system allows the diagnostic component to update equipment status promptly, alert maintenance personnel, and provide detailed anomaly information to support maintenance decisions.

[0284] The design of condition response operations needs to fully consider the motor's operating environment and application scenarios. In some cases, such as in continuous production plants, response operations need to minimize downtime, so temporary measures may be prioritized to maintain operation while scheduled maintenance is arranged. In other cases, such as in environments with extremely high safety requirements, any anomaly may trigger immediate shutdown and a comprehensive inspection. Furthermore, response operations may also include further analysis of motor operating data to determine whether diagnostic parameters need to be adjusted or the expert knowledge base updated, thereby improving the accuracy and efficiency of future diagnostics.

[0285] It should be understood that translating abnormal situation information into concrete response actions ensures the practical application value of motor diagnostic methods. This step not only helps to address abnormal situations during motor operation in a timely manner but also improves equipment management efficiency and production reliability through integration with control and maintenance management systems. Through response strategies, motor diagnostic methods can effectively support stable motor operation, reduce maintenance costs, and minimize potential production losses.

[0286] Reference Figure 13 , Figure 13 This illustration shows the hardware structure of an electronic device according to another embodiment. The electronic device may include:

[0287] The processor 1301 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0288] The memory 1302 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1302 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1302 and is called and executed by the processor 1301 using the motor diagnostic method of the embodiments of this application.

[0289] The input / output interface 1303 is used to implement information input and output;

[0290] The communication interface 1304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0291] Bus 1305 transmits information between various components of the device (e.g., processor 1301, memory 1302, input / output interface 1303, and communication interface 1304);

[0292] The processor 1301, memory 1302, input / output interface 1303 and communication interface 1304 are connected to each other within the device via bus 1305.

[0293] This application also provides a computer program product, which includes a computer program. The processor of a computer device reads and executes the computer program, causing the computer device to perform the motor diagnostic method described above.

[0294] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in this disclosure and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “including,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatuses.

[0295] It should be understood that in this disclosure, "at least one item" means one or more, and "more than one" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0296] It should be understood that in the description of the embodiments of this application, "multiple" means two or more, "greater than", "less than", "exceeding" etc. are understood to exclude the number itself, and "above", "below", "within" etc. are understood to include the number itself.

[0297] In the several embodiments provided in this disclosure, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0298] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0299] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0300] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned storage medium may include: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code.

[0301] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.

[0302] The above is a detailed description of the embodiments of this disclosure. However, this disclosure is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this disclosure. All such equivalent modifications or substitutions are included within the scope defined by the claims of this disclosure.

Claims

1. A method for diagnosing motors, characterized in that, Applied to motor diagnostic components, the method includes: The host and auxiliary machines of the motor diagnostic component are connected through the host data transmission module and the auxiliary machine data transmission module, and a reference time synchronization operation is performed on the electrical and mechanical equipment in the target power plant to align the acquisition reference time of the electrical and mechanical equipment. During the operation of the target motor, the host control unit performs first-type signal acquisition according to the acquisition reference time to obtain first-type device signal; Wherein, the first type of device signal is an electrical device signal, and the host control performs first type signal acquisition according to the acquisition reference time to obtain the first type of device signal, including: The host computer is controlled to collect signals from the electrical equipment corresponding to the target motor according to the acquisition reference time, thereby obtaining the electrical equipment signal; wherein, the timestamp information corresponding to the electrical equipment signal is electrical timestamp information; During the operation of the target motor, the auxiliary machine is controlled to acquire the second type of signal according to the acquisition reference time to obtain the second type of equipment signal. The second type of equipment signal is a mechanical equipment signal. The auxiliary machine is controlled to acquire the second type of signal according to the acquisition reference time to obtain the second type of equipment signal, including: The auxiliary machine is controlled to collect signals from the mechanical equipment corresponding to the target motor according to the acquisition reference time, thereby obtaining the mechanical equipment signal; wherein, the timestamp information corresponding to the mechanical equipment signal is mechanical timestamp information; The signal of the second type of device is transmitted to the host data transmission module via the auxiliary data transmission module; In the host, the timestamp information of the first type of device signal and the second type of device signal are aligned to obtain the first type parameter and the second type parameter; The process of aligning the timestamp information of the first type of device signal and the second type of device signal to obtain the first type parameter and the second type parameter includes: Based on the electrical timestamp information and the mechanical timestamp information, determine the electrical acquisition time and mechanical acquisition time corresponding to each acquisition sequence; For each of the aforementioned acquisition timing sequences, the time offset value between the electrical acquisition time and the mechanical acquisition time is calculated sequentially; Based on each acquisition time sequence and the time offset value corresponding to each acquisition time sequence, offset value vector mapping data is established; wherein, the offset value vector mapping data is used to reflect the relationship between the time offset value and the acquisition time sequence. Based on the offset value vector mapping data, the vector change rate is calculated to determine the offset value change rate corresponding to each of the acquisition time sequences; Abrupt change is calculated based on the rate of change of the offset value in each of the aforementioned acquisition time sequences; In response to the determination of a sudden change in the rate of change of the offset value during the mutation calculation, the offset value vector mapping data is sliced ​​based on the acquisition time series where the mutation occurred to obtain offset value slice data; wherein, the offset value slice data contains a first number of offset value sub-data, and each offset value sub-data corresponds to one acquisition time series; Obtain the mutation occurrence time sequence corresponding to the offset value slice data; A first offset parameter is determined based on the mutation occurrence time sequence; wherein, the first offset parameter is the time offset value between the electrical acquisition time and the mechanical acquisition time in the mutation occurrence time sequence; Based on the mutation occurrence time sequence corresponding to the previous acquisition time sequence, a corresponding second offset parameter is determined; wherein, the second offset parameter is the time offset value between the electrical acquisition time and the mechanical acquisition time in the previous acquisition time sequence corresponding to the mutation occurrence time sequence; Based on the first offset parameter and the second offset parameter, the acquisition deviation ratio corresponding to the offset value slice data is calculated; Based on the acquisition deviation ratio, configure the deviation correction factor corresponding to each acquisition time sequence in the offset value slice data; Based on the deviation correction factor corresponding to each acquisition time sequence, a timestamp alignment operation is performed on the electrical timestamp information and the mechanical timestamp information in each acquisition time sequence to obtain the first type parameter and the second type parameter; Based on the first type of parameters and the second type of parameters, a motor diagnostic operation is performed to obtain a motor diagnostic result corresponding to the target motor.

2. The method according to claim 1, characterized in that, Also includes: During the acquisition of the first type of signal and the acquisition of the second type of signal, data monitoring is performed on the acquired first type of device signal and second type of device signal; In response to an abnormal signal detected in data monitoring, attribution analysis is performed based on the abnormal signal to obtain abnormal situation information; Based on the abnormal situation information, execute the corresponding situation response operation.

3. The method according to claim 1, characterized in that, The abrupt change calculation based on the rate of change of the offset value of each of the acquisition time series includes: Based on the time offset value corresponding to each of the acquisition time sequences, the cumulative total offset value is determined; wherein, the number of acquisition time sequences is a second number; Based on the cumulative total offset value and the second number, the mutation definition conditions are set; In response to the existence of a rate of change of the offset value satisfying the mutation definition condition, it is determined that the corresponding rate of change of the offset value has undergone a mutation.

4. The method according to claim 3, characterized in that, The step of determining that a sudden change in the corresponding rate of change of the offset value has occurred in response to the existence of a rate of change that satisfies the abrupt change criteria includes: Based on the cumulative total offset value and the second number, the mutation boundary value is calculated; The rate of change of the offset value in the acquisition time series is compared with the mutation threshold value; If the rate of change of the offset value exceeds the mutation threshold, it is determined that a mutation has occurred in the rate of change of the offset value.

5. The method according to claim 1, characterized in that, The step of calculating the acquisition deviation ratio corresponding to the offset value slice data based on the first offset parameter and the second offset parameter includes: The acquisition deviation ratio is obtained by performing quotient processing based on the first offset parameter and the second offset parameter.

6. A motor diagnostic component, characterized in that, An application to the motor diagnostic method according to any one of claims 1 to 5, comprising: The host includes a host power module, a host acquisition module, a host computer board, and a host data transmission module; wherein, the host power module is used to supply power to the host, the host acquisition module is used to acquire signals from a first type of device, and the host computer board includes a host storage module for storing the first type of data and a diagnostic analysis module for performing motor diagnostic operations; At least one auxiliary machine, the auxiliary machine including an auxiliary machine power supply module, an auxiliary machine acquisition module, an auxiliary machine computer board, and an auxiliary machine data transmission module; wherein, the auxiliary machine power supply module is used to supply power to the auxiliary machine, the auxiliary machine acquisition module is used to acquire signals from a second type of device, the auxiliary machine computer board includes an auxiliary machine storage module for storing second type of data, and the host data transmission module is used to cooperate with the auxiliary machine data transmission module to perform collaborative data transmission between the host and the auxiliary machine.

7. The motor diagnostic component according to claim 6, characterized in that, The host acquisition module is installed on the electrical equipment corresponding to the target motor to acquire electrical equipment signals as the first type of equipment signals. The auxiliary acquisition module is installed on the mechanical equipment corresponding to the target motor to acquire mechanical equipment signals as the second type of equipment signals.

8. The motor diagnostic component according to claim 7, characterized in that, The auxiliary acquisition module integrates a temperature sensing unit, a sound sensing unit, and a vibration sensing unit; wherein, the temperature sensing unit, the sound sensing unit, and the vibration sensing unit are used to be installed on the target motor to collect temperature sensing signals, sound sensing signals, and vibration sensing signals, and integrate the temperature sensing signals, the sound sensing signals, and the vibration sensing signals into the second type of data.

9. The motor diagnostic component according to claim 6, characterized in that, A first auxiliary machine transmission path and a second auxiliary machine transmission path are provided between the auxiliary machine acquisition module and the auxiliary machine computer board.

10. The motor diagnostic component according to claim 9, characterized in that, The first auxiliary machine transmission path is provided with a first node, and the second auxiliary machine transmission path is provided with a second node. The first node and the second node are used to connect to the host computer board.

11. The motor diagnostic component according to claim 10, characterized in that, On the second auxiliary machine transmission path, a normally closed switch is provided between the second node and the auxiliary machine computer board. The normally closed switch is configured to disconnect when the host data transmission module and the auxiliary machine data transmission module are connected.

12. An electronic device, characterized in that, include: The device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the motor diagnostic method as described in any one of claims 1 to 5.

13. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the motor diagnostic method as described in any one of claims 1 to 5.

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