Handheld motor data acquisition device, control method and fault diagnosis system

Abstract

The embodiment of the invention provides a handheld motor data acquisition device, a control method and a fault diagnosis system. The handheld motor data acquisition device comprises a sensor unit, a data processing unit, a wireless transmission module, a man-machine interaction module and a power management module, the sensor unit is used for realizing multi-parameter synchronous acquisition through a timestamp synchronization algorithm; the data processing unit is used for realizing data real-time filtering, FFT (Fast Fourier Transform) analysis and feature extraction by utilizing a built-in chip and an embedded processor, and generating a diagnosis report based on a detection parameter threshold value; the man-machine interaction module is used for providing a touch screen supporting real-time waveform display and operation instruction input and displaying the diagnosis report; and the power management module is used for supporting a fast charging mode and a low power consumption mode to charge the equipment. Through cooperative work of the modules, synchronous acquisition and analysis of multiple parameters of the motor, intelligent data processing, convenient man-machine interaction and reliable power supply guarantee are realized, the motor detection efficiency and accuracy are improved, and the maintenance cost is reduced.

Description

Technical Field

[0001] This disclosure relates to the field of data acquisition technology, and in particular to a handheld motor data acquisition device, control method and fault diagnosis system. Background Technology

[0002] In modern industrial production and equipment maintenance, motors, as core power equipment, require real-time monitoring and accurate diagnosis of their operating status to ensure production continuity and reduce equipment failure rates. Traditional motor data acquisition equipment often adopts a fixed architecture or is bulky in design. This makes it difficult to flexibly adapt to diverse industrial field testing scenarios. When faced with motor equipment in confined spaces, scattered locations, or requiring mobile testing, it is often impossible to deploy quickly and operate efficiently.

[0003] Meanwhile, existing handheld motor data acquisition devices suffer from significant functional deficiencies. Most only support the acquisition of a single parameter, such as current, vibration, or temperature, making it difficult to meet the needs of simultaneous monitoring and comprehensive analysis of multi-dimensional motor operating parameters under complex working conditions. This results in a lack of comprehensiveness and accuracy in fault diagnosis. Regarding data transmission, some devices still rely on wired connections, which not only complicates the operation process and increases the complexity of on-site work but also poses safety hazards such as wire wear and leakage in industrial environments. Furthermore, most current acquisition devices lack built-in intelligent data processing capabilities, requiring the acquired data to be transmitted to an external computer for analysis. This process not only reduces detection efficiency but also limits the timeliness of real-time on-site diagnosis and decision-making.

[0004] With the accelerated advancement of industrial intelligence and automation, higher demands are being placed on the portability, multifunctionality, efficient data transmission, and intelligent processing capabilities of motor data acquisition equipment. Developing a handheld motor data acquisition device that can overcome these technical bottlenecks has become an urgent direction for meeting the needs of efficient motor testing and maintenance in industrial settings. Summary of the Invention

[0005] This disclosure provides a handheld motor data acquisition device, a control method, and a fault diagnosis system to solve the problems existing in the current technical solutions.

[0006] In view of the above problems, a first aspect is to provide a handheld motor data acquisition device, including: a sensor unit, a data processing unit, a human-computer interaction module and a power management module;

[0007] The sensor unit is used to achieve synchronous acquisition of multiple parameters through a timestamp synchronization algorithm;

[0008] The data processing unit is used to perform real-time data filtering, FFT analysis and feature extraction using built-in chips and embedded processors, and to generate diagnostic reports based on detection parameter thresholds.

[0009] The human-computer interaction module is used to provide a touchscreen that supports real-time waveform display and operation command input, and to display the diagnostic report;

[0010] The power management module supports fast charging and low-power modes for charging the device.

[0011] In conjunction with the first aspect, in one possible implementation, the sensor unit includes at least one of the following: a near-field communication (NFC) module, a vibration sensor, a temperature sensor, a noise sensor, and a non-invasive current clamp probe.

[0012] In conjunction with the first aspect, in one possible implementation, the device further includes: a wireless transmission module for supporting Wi-Fi, Bluetooth and 4G multimode communication, capable of uploading data to a cloud platform and / or a local server and receiving cloud diagnostic reports issued by the cloud platform and / or the local server;

[0013] The human-computer interaction module is also used to display the cloud diagnostic report.

[0014] In conjunction with the first aspect, in one possible implementation, the device employs a magnetic housing.

[0015] In conjunction with the first aspect, in one possible implementation, the non-invasive current clamp probe has a foldable structure.

[0016] Secondly, a control method for a handheld motor data acquisition device is provided, including:

[0017] The motor number is identified by the near field communication (NFC) module, and the corresponding detection parameter threshold is retrieved based on the motor number.

[0018] The sensor unit synchronously collects motor operation data for a preset duration and displays waveforms and spectrum diagrams in real time through the human-machine interaction module.

[0019] A diagnostic report is generated based on the detection parameter thresholds, and the risk level is indicated through the human-computer interaction module based on the diagnostic report;

[0020] The motor operation data is uploaded to the cloud platform, and the cloud diagnostic report generated by the cloud platform is received and displayed through the human-computer interaction module.

[0021] In conjunction with the second aspect, in one possible implementation, the sensor unit includes at least one of the following: a near-field communication (NFC) module, a vibration sensor, a temperature sensor, a noise sensor, and a non-invasive current clamp probe;

[0022] The sensor unit synchronously collects motor operation data for a preset duration, including:

[0023] At least two sensors in the sensor unit collect motor operation data for a preset duration and use a timestamp synchronization algorithm to reduce the time delay error between the at least two sensors.

[0024] In conjunction with the second aspect, in one possible implementation, the motor operating data includes at least one of the following: vibration frequency, noise level, temperature, and current;

[0025] The generation of a diagnostic report based on detection parameter thresholds includes:

[0026] The motor operating data are compared with the corresponding detection parameter thresholds.

[0027] If the vibration frequency is not within the range of the corresponding vibration threshold, a diagnostic report is generated indicating vibration abnormality; and / or

[0028] If the noise value is outside the range of the corresponding noise threshold, a diagnostic report is generated indicating noise abnormality; and / or

[0029] If the temperature is outside the corresponding temperature threshold range, a diagnostic report is generated indicating a temperature abnormality; and / or

[0030] If the current is outside the range of the corresponding current threshold, a diagnostic report is generated as "current abnormality".

[0031] Thirdly, a motor fault diagnosis system is provided, comprising: a handheld motor data acquisition device and a cloud platform as described in the first aspect, or in any possible embodiment of the first aspect;

[0032] The handheld motor data acquisition device is used to collect motor operation data within a preset time period; it uses an installed embedded processor to generate a diagnostic report based on detection parameter thresholds, and uses the human-computer interaction module to indicate the risk level based on the diagnostic report;

[0033] The handheld motor data acquisition device is also used to send the motor operation data to the cloud platform, receive the cloud diagnostic report issued by the cloud platform, and display it through the human-computer interaction module.

[0034] The cloud platform is used to input the motor operation data into a pre-trained fault diagnosis model to obtain a cloud diagnosis report.

[0035] In conjunction with the third aspect, in one possible implementation, the cloud platform is further configured to analyze the received motor operation data based on the motor number, obtain the degree of motor degradation, and adjust the detection parameter threshold based on the degree of motor degradation.

[0036] The degree of degradation of the motor does not exceed a preset value.

[0037] The beneficial effects of the embodiments disclosed herein include:

[0038] This disclosure provides a handheld motor data acquisition device, control method, and fault diagnosis system, applicable to scenarios such as industrial production sites, equipment maintenance and repair, and motor R&D testing. It can monitor the operating status and diagnose faults in various types of motors. The sensor unit employs a timestamp synchronization algorithm, enabling simultaneous acquisition of multiple parameters such as motor current, vibration, and temperature, ensuring consistent time references for each parameter and providing fundamental data for multi-dimensional comprehensive analysis. Its purpose is to solve the problem of single-parameter acquisition in existing equipment. By synchronously acquiring multiple parameters, it provides the possibility of comprehensively understanding the motor's operating status, helping to more accurately detect potential motor faults. The data processing unit, utilizing a built-in chip and embedded processor, performs real-time filtering, FFT analysis, and feature extraction on the acquired data, and generates a diagnostic report based on the detection parameter thresholds. This module aims to achieve intelligent data processing, avoiding reliance on external computers, improving data analysis efficiency, supporting rapid on-site diagnosis, and allowing staff to understand the motor's condition promptly. The human-machine interaction module uses a touchscreen to display real-time waveforms, input operation commands, and display diagnostic reports, facilitating intuitive access to motor data and diagnostic results for operators and reducing operational complexity. The power management module supports fast charging and low power consumption modes, ensuring stable operation of the equipment for extended periods and meeting the needs of long-term on-site testing.

[0039] In summary, this device solves the technical problems of traditional equipment, such as poor flexibility, limited functionality, inconvenient data transmission, and insufficient intelligent processing. Through the collaborative work of its modules, it achieves synchronous acquisition and analysis of multiple motor parameters, intelligent data processing, convenient human-machine interaction, and reliable power supply, thereby improving the efficiency and accuracy of motor testing and reducing maintenance costs. Attached Figure Description

[0040] Figure 1 This is one of the structural schematic diagrams of a handheld motor data acquisition device provided in an embodiment of this disclosure;

[0041] Figure 2 A schematic diagram of the physical structure of the handheld motor data acquisition device provided in the embodiments of this disclosure;

[0042] Figure 3 This is a second schematic diagram of the structure of a handheld motor data acquisition device provided in an embodiment of the present disclosure;

[0043] Figure 4 A diagram illustrating a control method for a handheld motor data acquisition device provided in an embodiment of this disclosure;

[0044] Figure 5 This is a schematic diagram of a fault diagnosis system provided in an embodiment of the present disclosure;

[0045] Figure 6 A framework diagram of the fault diagnosis model provided in the embodiments of this disclosure. Detailed Implementation

[0046] This disclosure provides a handheld motor data acquisition device, control method, and fault diagnosis system. Preferred embodiments of this disclosure are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of this disclosure. Furthermore, the embodiments and features described herein can be combined with each other unless otherwise specified.

[0047] To achieve efficient monitoring and fault diagnosis of motor operating status, embodiments of this disclosure provide a handheld motor data acquisition device, such as... Figure 1 As shown, it includes: a sensor unit 101, a data processing unit 102, a human-computer interaction module 103, and a power management module 104;

[0048] Sensor unit 101 is used to achieve synchronous acquisition of multiple parameters through a timestamp synchronization algorithm;

[0049] The data processing unit 102 is used to perform real-time data filtering, FFT analysis and feature extraction using the built-in chip and embedded processor, and to generate a diagnostic report based on the detection parameter threshold.

[0050] The human-computer interaction module 103 is used to provide a touch screen that supports real-time waveform display and operation command input, and to display diagnostic reports;

[0051] The power management module 104 is used to support fast charging and low power mode for charging the device.

[0052] The handheld motor data acquisition device proposed in this embodiment solves a number of technical problems existing in traditional equipment through the coordinated operation of the sensor unit 101, the data processing unit 102, the human-computer interaction module 103 and the power management module 104.

[0053] Sensor unit 101 employs a timestamp synchronization algorithm to synchronize multiple key parameters such as motor current, vibration, and temperature collected by various sensors, avoiding time asynchrony issues caused by sensor delays and reducing errors. For example, in motor vibration monitoring scenarios, synchronously acquiring vibration frequency and current data allows for more accurate identification of vibration problems caused by abnormal loads. This unit breaks through the limitations of existing handheld devices in acquiring single parameters, providing a data foundation for multi-dimensional analysis of motor operating status.

[0054] The data processing unit 102 utilizes a built-in chip and embedded processor to perform real-time filtering on the acquired data to remove noise interference; it converts the time-domain signal into a frequency-domain signal through FFT analysis, extracts key features, and generates a diagnostic report based on preset parameter thresholds. For example, in diagnosing motor bearing faults, FFT analysis of the vibration signal spectrum can quickly identify abnormal bearing vibration frequencies, promptly detect potential faults, achieve intelligent data processing, avoid reliance on external computers, and improve diagnostic efficiency.

[0055] The human-machine interface module 103 is equipped with a touchscreen that supports real-time waveform display and operation command input. It intuitively presents the motor's operating data waveforms, and operators can quickly input commands via the touchscreen to start data acquisition or obtain diagnostic reports. This interaction method simplifies the operation process and allows on-site personnel to quickly grasp the motor's operating status.

[0056] The power management module 104 supports fast charging and low power mode. Fast charging mode can fully charge the device in a short time, while low power mode extends the device's battery life and meets the needs of long-term on-site testing.

[0057] This handheld motor data acquisition device effectively solves the problems of poor flexibility, limited functionality, inconvenient transmission, and low processing efficiency of traditional equipment, providing an efficient and accurate solution for motor operation status monitoring and fault diagnosis.

[0058] In another embodiment provided in this disclosure, the sensor unit 101 includes at least one of the following: a near field communication (NFC) module, a vibration sensor, a temperature sensor, a noise sensor, and a non-invasive current clamp probe.

[0059] In this embodiment, the sensor unit 101 serves as the core component of the handheld motor data acquisition device for acquiring motor operation data. Through the collaborative work of various types of sensors, it achieves comprehensive monitoring of the motor's operating status. Its components, including a near-field communication (NFC) module, vibration sensor, temperature sensor, noise sensor, and non-invasive current clamp probe, each perform unique functions, collectively supporting the synchronous acquisition of multiple parameters.

[0060] The Near Field Communication (NFC) module supports contactless data transmission, enabling rapid reading of motor ID information, historical operating data, and other data, avoiding the tediousness and errors associated with manual input. For example, when testing motors in batches, simply placing the NFC module near the motor tag allows for the rapid acquisition of basic information such as the motor model and rated parameters, facilitating subsequent testing and analysis.

[0061] Vibration sensors monitor parameters such as vibration amplitude and frequency during motor operation to determine whether there are mechanical faults in the motor. Faults such as worn motor bearings or rotor imbalance can cause abnormal vibrations. After capturing these vibration signals, the vibration sensors provide analysis data to the data processing unit 102.

[0062] Temperature sensors monitor the temperature of critical motor components in real time, such as windings and bearings. Problems like motor overload and poor heat dissipation can cause abnormal temperature increases. Timely temperature data collection by the sensors can effectively prevent motor malfunctions caused by overheating.

[0063] Noise sensors collect noise signals generated during motor operation. Different types of faults will produce noise of specific frequencies or intensities. By analyzing the noise signals, the operating status of the motor can be determined.

[0064] Non-invasive current clamp probes can measure motor operating current without disconnecting the circuit, providing convenient and quick access to current parameters and data for analyzing motor load, energy consumption, and other conditions.

[0065] The sensor unit 101 uses a combination of various sensors to achieve comprehensive acquisition of multi-dimensional data on motor operation. Combined with a timestamp synchronization algorithm, it ensures the synchronization and accuracy of the data, laying a solid foundation for the subsequent data processing unit 102 to perform real-time filtering, FFT analysis and fault diagnosis. This significantly improves the comprehensiveness and accuracy of the handheld motor data acquisition device in monitoring the motor's operating status.

[0066] In yet another embodiment provided in this disclosure, such as Figure 2 As shown, the device also includes: a wireless transmission module 105, which supports Wi-Fi, Bluetooth and 4G multi-mode communication, and can upload data to the cloud platform and / or local server and receive cloud diagnostic reports issued by the cloud platform and / or local server;

[0067] The human-computer interaction module 103 is also used to display cloud diagnostic reports.

[0068] In this embodiment of the disclosure, the handheld motor data acquisition device also includes a wireless transmission module 105, which further enhances the device's intelligence and remote collaboration capabilities.

[0069] The wireless transmission module 105 supports multi-mode communication including Wi-Fi, Bluetooth, and 4G, enabling flexible adaptation to different network environments. In scenarios with stable Wi-Fi networks, such as factory workshops, collected motor data can be uploaded to a local server at high speed via Wi-Fi, facilitating centralized management and analysis within the enterprise. In outdoor or mobile monitoring scenarios, the 4G communication function ensures stable data upload to the cloud platform, enabling remote data storage and analysis. Simultaneously, this module can also receive cloud diagnostic reports from the cloud platform or local server, breaking the limitations of traditional equipment that relies on local data analysis.

[0070] The human-machine interface module 103 not only has real-time waveform display and operation command input functions, but also supports cloud diagnostic report display. When the wireless transmission module 105 receives the cloud diagnostic report, the human-machine interface module 103 presents the report intuitively through the touch screen, including information such as motor fault type, fault location, and maintenance suggestions, making it convenient for on-site personnel to quickly obtain comprehensive diagnostic results without the need for additional equipment to view the report.

[0071] In summary, the wireless transmission module 105 of the handheld motor data acquisition device provided in this disclosure frees the device from the constraints of wired transmission and the limitations of local data analysis, enabling cloud storage, remote diagnosis, and real-time feedback. Multi-mode communication ensures the stability and flexibility of data transmission, and the human-machine interface module 103 provides an intuitive display of cloud diagnostic reports, enhancing the remote collaboration capabilities and diagnostic efficiency of the motor data acquisition device and meeting the demands of modern industry for intelligent and remote equipment monitoring.

[0072] In another embodiment provided in this disclosure, the device employs a magnetic housing.

[0073] In this embodiment, the handheld motor data acquisition device uses a magnetic housing, enabling it to be attached. A magnetic housing refers to a housing material in which magnetic components are integrated. In industrial motor testing scenarios, operators often need both hands for complex operations. In such cases, the device can be attached to the metal casing of the motor or other metal structures, avoiding interference from handheld operation and preventing accidental drops and damage. For example, when testing motors at heights or in confined spaces, attaching the device to a nearby metal frame frees up the operator's hands for delicate operations such as sensor connection, while simultaneously allowing them to view the collected data and diagnostic results.

[0074] In one possible implementation, the magnetic component of the magnetic housing can be a permanent magnet or an electromagnet. Permanent magnets have a simple structure, require no additional power supply, and provide a continuous and stable attraction force; electromagnets, on the other hand, can control the on / off state of the attraction force by controlling the flow of current, facilitating flexible adjustment of the device's position. Furthermore, the housing can incorporate an anti-slip texture design to further enhance stability in the magnetically attached state, while ensuring comfortable grip when held.

[0075] In summary, the handheld motor data acquisition device with a magnetic housing design disclosed herein offers improved flexibility and convenience compared to traditional motor data acquisition devices, solving the problem of inconvenient device placement during on-site testing. It is particularly suitable for complex and variable industrial environments. The magnetic housing further enhances the operational efficiency and safety of the entire device during on-site operations, strengthening its adaptability and reliability in practical applications and providing more comprehensive protection for motor data acquisition.

[0076] In another embodiment provided in this disclosure, the non-invasive current clamp probe has a foldable structure.

[0077] In this embodiment of the present disclosure, the non-invasive current clamp probe is a key component for acquiring motor current parameters in the sensor unit 101 of the handheld motor data acquisition device. Its foldable structure design further optimizes the practicality and portability of the device, and works effectively with other functional modules of the device to improve the overall performance.

[0078] In this embodiment, the non-invasive current clamp probe of the sensor unit 101 adopts a foldable structure, meaning that the opening and closing components of the probe can be folded and stored through a specific connection structure. In actual use, the clamp probe is unfolded and clamped onto the motor circuitry, allowing for rapid measurement of the operating current without disconnecting the circuit, thus supporting the acquisition of data such as motor load and energy consumption. After testing, the probe can be folded up, significantly reducing its space occupation. For example, when testing multiple motors on-site, where personnel need to frequently move, the folded probe is easy to store in a tool bag, reducing carrying burden. When testing in confined equipment spaces, the folded probe is also easier to carry and operate, avoiding the problem of difficulty in accessing the testing circuitry due to its large size.

[0079] In one possible implementation, the foldable structure can be achieved through mechanical connections such as hinges and snap-fits. The hinges provide flexible rotational connections, while the snap-fits serve to fix the probe in its unfolded or folded state, ensuring stability during measurement and compactness when stored.

[0080] In summary, the non-invasive current clamp probe with a foldable structure in the handheld motor data acquisition device provided in this disclosure effectively solves the problems of large size and inconvenience of traditional probes, greatly improving the portability and flexibility of on-site operation. This makes the entire handheld motor data acquisition device not only meet the needs of efficient data acquisition and analysis, but also more adaptable to diverse industrial testing scenarios. Whether in open areas or confined spaces, motor testing operations can complete data acquisition tasks more conveniently, improving testing efficiency and user experience.

[0081] Example 1

[0082] Provides a possible implementation structure for a handheld motor data acquisition device, such as Figure 3 As shown in this embodiment, the handheld motor data acquisition device is powered by a rechargeable lithium battery and is conveniently operated by hand using a handle. It integrates a non-invasive current waveform probe via a soft connection to collect data such as motor current. On the sensor side, multiple sensors, including vibration, temperature, and noise sensors, are arranged. It has a built-in NFC module and a magnetic casing, enabling multi-dimensional capture of motor operating data and adapting to different detection scenarios. The interactive interface side features a touchscreen and power-on / power-off buttons, providing simple operation and facilitating quick on-site startup, control, and viewing of collected data. The device side integrates a wireless transmission module 105 and a data processing unit. The collected motor data can be wirelessly transmitted after processing, achieving integrated handheld detection of acquisition, processing, and transmission, meeting the portable and efficient data acquisition needs in motor maintenance.

[0083] Based on the same disclosed concept, this disclosure also provides a control method for a handheld motor data acquisition device. Since the principle of the problem solved by this control method is similar to that of the aforementioned handheld motor data acquisition device, the implementation of this control method can refer to the implementation of the aforementioned device, and the repeated parts will not be described again.

[0084] With the above Figure 1 Correspondingly, this disclosure also provides a control method for a handheld motor data acquisition device, such as... Figure 4 As shown, it includes:

[0085] S401. Identify the motor number through the near field communication (NFC) module and retrieve the corresponding detection parameter threshold based on the motor number;

[0086] S402, The sensor unit synchronously collects motor operation data for a preset duration and displays waveforms and spectrum diagrams in real time through the human-machine interaction module;

[0087] S403. Generate a diagnostic report based on the detection parameter threshold, and prompt the risk level through the human-computer interaction module based on the diagnostic report;

[0088] S404. Upload the motor operation data to the cloud platform, receive the cloud diagnostic report generated by the cloud platform, and display it through the human-computer interaction module.

[0089] In this embodiment of the disclosure, based on the hardware functions of the handheld motor data acquisition device, a matching control method is provided to further standardize the device's operation process and data processing logic.

[0090] First, the motor serial number is identified using the Near Field Communication (NFC) module, and the corresponding detection parameter thresholds are retrieved based on the motor serial number. The NFC module reads the motor serial number in a contactless manner. For example, in a motor manufacturing workshop or equipment maintenance scenario, simply bringing the device close to the NFC tag of the motor can quickly obtain the motor serial number. Then, the standard operating parameter thresholds corresponding to that motor model can be retrieved from the local database or the cloud, providing a reference for subsequent diagnosis and avoiding the tediousness and errors of manual parameter input.

[0091] Next, the sensor unit synchronously collects motor operation data for a preset duration and displays the waveforms and spectrum in real time through the human-machine interface module. Vibration, temperature, and current sensors in the sensor unit synchronously collect multi-parameter data within a preset time period, such as continuously collecting motor operation data for 30 seconds. The human-machine interface module presents the data waveforms and spectrum in real time, allowing operators to intuitively observe the motor's operating trend, for example, promptly identifying abnormal frequency components through the vibration data spectrum.

[0092] Then, a diagnostic report is generated based on the detection parameter thresholds, and the risk level is indicated through the human-computer interaction module based on the diagnostic report. The data processing unit compares and analyzes the collected data with the thresholds to determine the motor's operating status and generate a diagnostic report.

[0093] Finally, the motor operation data is uploaded to the cloud platform, and the cloud diagnostic report generated by the cloud platform is received and displayed through the human-machine interaction module. The wireless transmission module uploads the data to the cloud platform, performs in-depth analysis with the help of the powerful computing power of the cloud, generates a cloud diagnostic report, and then sends it back to the device for display through the human-machine interaction module.

[0094] In summary, this control method automates and intelligentizes the motor testing process through NFC rapid identification, simultaneous acquisition and real-time display of multiple parameters, threshold diagnosis and risk alerts, and cloud-based collaborative diagnosis. It not only improves data acquisition and analysis efficiency but also ensures diagnostic accuracy through dual local and cloud-based diagnosis, providing a complete and efficient solution for motor operating status monitoring and fault early warning.

[0095] In another embodiment provided in this disclosure, the sensor unit includes at least one of the following: a near-field communication (NFC) module, a vibration sensor, a temperature sensor, a noise sensor, and a non-invasive current clamp probe;

[0096] The above step S402, where the sensor unit synchronously collects motor operating data for a preset duration, can be implemented as follows:

[0097] At least two sensors in the sensor unit collect motor operation data for a preset duration and use a timestamp synchronization algorithm to reduce the time delay error between the at least two sensors.

[0098] In this embodiment, the sensor unit includes various types such as a near-field communication (NFC) module, a vibration sensor, a temperature sensor, a noise sensor, and a non-invasive current clamp probe. The NFC module can quickly read information such as the motor number and parameters in a contactless manner. For example, in motor equipment management, simply bringing the NFC tag close to the motor can obtain basic motor data, facilitating subsequent detection and analysis.

[0099] During data acquisition, at least two sensors in the sensor unit collect motor operation data for a preset duration, and a timestamp synchronization algorithm is used to reduce time delay errors between sensors. For example, when simultaneously monitoring motor current, vibration, and temperature, each sensor collects data within a preset 1-minute period. The timestamp synchronization algorithm adds a precise timestamp to each data point, ensuring consistency in the time dimension of data collected by different sensors. This avoids analytical biases caused by differences in collection time, enabling subsequent data processing units to perform more accurate FFT analysis, feature extraction, and fault diagnosis based on synchronized data.

[0100] In summary, the multi-sensor configuration and timestamp synchronization acquisition mechanism provided in this disclosure enable sensor units to achieve multi-dimensional and high-precision monitoring of motor operating status. It breaks through the limitations of traditional single-parameter acquisition, reduces data errors through a timestamp synchronization algorithm, provides a reliable data foundation for comprehensive evaluation of motor operating status, significantly improves the accuracy and effectiveness of handheld motor data acquisition devices, and meets the needs for efficient and accurate motor detection in industrial scenarios.

[0101] In another embodiment provided in this disclosure, the motor operating data includes at least one of the following: vibration frequency, noise level, temperature, and current;

[0102] In step S403 above, generating a diagnostic report based on the detection parameter threshold includes:

[0103] The motor operating data is compared with the corresponding detection parameter thresholds.

[0104] If the vibration frequency is outside the range of the corresponding vibration threshold, a diagnostic report is generated indicating vibration abnormality; and / or

[0105] If the noise value is outside the range of the corresponding noise threshold, a diagnostic report is generated indicating noise abnormality; and / or

[0106] If the temperature is outside the corresponding temperature threshold range, a diagnostic report is generated indicating a temperature abnormality; and / or

[0107] If the current is outside the range of the corresponding current threshold, a diagnostic report is generated as "current abnormality".

[0108] After the handheld motor data acquisition device completes the acquisition of motor operation data, the key step is to evaluate and diagnose the motor's operating status based on the acquired data. The data processing unit generates a diagnostic report by comparing and analyzing the motor operation data with detection parameter thresholds.

[0109] In this embodiment, the motor operating data includes key parameters such as vibration frequency, noise level, temperature, and current. Vibration frequency reflects the stability of the motor's mechanical structure; for example, bearing wear or rotor imbalance can cause abnormal vibration frequency. Noise level reflects the acoustic characteristics of the motor during operation; abnormal noise may indicate loosening or friction of internal components. Temperature parameter is used to monitor the heating status of key parts of the motor; overheating is often a sign of overload, poor heat dissipation, or other problems. Current reflects the motor's load and energy consumption status; abnormal current fluctuations may indicate circuit faults or load changes.

[0110] The process of generating diagnostic reports based on detection parameter thresholds first compares the collected motor operating data with the corresponding detection parameter thresholds. Taking vibration frequency as an example, if the vibration frequency threshold range for a certain model of motor is 10-30Hz during normal operation, and the actual collected vibration frequency exceeds this range, the data processing unit generates a diagnostic report indicating vibration abnormality. Regarding noise levels, if the noise threshold range for normal motor operation is 50-70 dB, and the measured noise value is outside this range, it is diagnosed as noise abnormality. The same applies to temperature and current; when the temperature exceeds the preset maximum temperature threshold, or the current exceeds the rated current threshold range, diagnostic reports for temperature abnormality and current abnormality are generated respectively. The diagnostic results for each parameter can be presented individually or analyzed comprehensively. For example, when an abnormal current is accompanied by an increase in temperature, it can more accurately pinpoint motor overload or heat dissipation faults.

[0111] In summary, by quantitatively comparing motor operating data with thresholds, standardized and automated diagnosis of motor faults can be achieved. Compared to manual judgment based on experience, the diagnostic process is more objective and efficient, quickly identifying anomalies in motor vibration, noise, temperature, current, etc., and promptly discovering potential fault hazards. Furthermore, the intuitive display through the human-machine interface module provides a comprehensive, accurate, and efficient solution for monitoring motor operating status, effectively improving the scientific rigor and reliability of motor maintenance management.

[0112] Based on the same disclosed concept, this disclosure also provides a motor fault diagnosis system for handheld motor data acquisition devices. The system utilizes the collaboration between the handheld motor data acquisition device and a cloud platform to diagnose motor faults. The implementation of the control method can be found in the implementation of the aforementioned device, and repeated details will not be described again.

[0113] This disclosure also provides a motor fault diagnosis system, such as... Figure 5As shown, it includes: a handheld motor data acquisition device 501 and a cloud platform 502 provided in any embodiment of this disclosure;

[0114] The handheld motor data acquisition device 501 is used to collect motor operation data within a preset time period; it uses the installed embedded processor to generate a diagnostic report based on the detection parameter threshold, and uses the human-machine interaction module to indicate the risk level based on the diagnostic report;

[0115] The handheld motor data acquisition device 501 is also used to send motor operation data to the cloud platform 502, and receive cloud diagnostic reports issued by the cloud platform 502 and display them through the human-computer interaction module;

[0116] The cloud platform 502 is used to input motor operation data into a pre-trained fault diagnosis model to obtain a cloud diagnosis report.

[0117] With the increasing demand for intelligent monitoring of industrial equipment, simple motor data acquisition and local diagnosis are no longer sufficient to meet the requirements for accurate fault prediction under complex operating conditions. The motor fault diagnosis system provided in this embodiment achieves a dual diagnostic system of local real-time detection and cloud-based intelligent analysis through the collaborative operation of a handheld motor data acquisition device 501 and a cloud platform 502, effectively improving the accuracy and efficiency of motor fault diagnosis.

[0118] In this embodiment, the handheld motor data acquisition device 501 serves as the front-end detection device of the system, undertaking data acquisition and preliminary diagnostic functions. Within a preset time period, its sensor unit synchronously collects operating data such as motor vibration frequency, noise level, temperature, and current, for example, continuously collecting multi-dimensional data for up to 2 minutes. The embedded processor within the device compares the collected data with detection parameter thresholds, generates a diagnostic report, and uses a human-machine interaction module to indicate the risk level through color coding, icon warnings, etc. For example, when the motor current is detected to exceed the rated threshold, the device immediately displays a high-risk warning box on the screen to help on-site personnel quickly identify the problem. Simultaneously, the device wirelessly transmits the collected motor operating data to the cloud platform 502 and receives the cloud diagnostic report from the cloud platform 502, which is then visualized in the human-machine interaction module.

[0119] The cloud platform 502, acting as the system's intelligent analysis hub, receives motor operation data from the handheld device and inputs it into a pre-trained fault diagnosis model. This model, trained on a large amount of historical fault data, possesses powerful pattern recognition and fault prediction capabilities. For example, by inputting abnormal vibration frequency and current fluctuation data of a motor, the fault diagnosis model can analyze and determine potential faults such as bearing wear based on historical similar data characteristics, and then generate a cloud diagnostic report containing fault type, risk level, and maintenance recommendations, which is then sent to the handheld device.

[0120] The motor fault diagnosis system disclosed herein achieves complementary advantages of rapid local response and in-depth cloud analysis through an edge-cloud combined architecture. The local device meets the needs of real-time on-site diagnosis, avoiding missed detection of basic faults; the cloud platform 502, through intelligent analysis of fault diagnosis models, solves complex fault diagnosis challenges. This dual diagnostic mechanism not only improves the accuracy of motor fault diagnosis but also provides a scientific basis for equipment maintenance decisions, effectively reducing equipment downtime risks and enhancing the reliability and intelligence level of industrial production.

[0121] A possible implementation method for constructing, training, and validating the pre-trained fault diagnosis model is provided, with the following steps:

[0122] Data preparation

[0123] We collected a large amount of motor operating data covering different operating conditions, models, and service years, including parameters such as vibration frequency, noise level, temperature, and current, and labeled the corresponding anomaly types (such as bearing wear, winding short circuit, rotor imbalance, etc.). The data was divided into a training set (approximately 70%), a validation set (approximately 15%), and a test set (approximately 15%). The training set was used for model parameter learning, the validation set was used to adjust hyperparameters and prevent overfitting, and the test set was used to evaluate the final model performance.

[0124] Model building

[0125] Choosing a suitable neural network architecture, such as using an LSTM network, can effectively process the sequential information of motor operation data that changes over time. Then, setting parameters such as the number of network layers, the number of neurons, and the activation function (such as ReLU) allows for the construction of an initial fault diagnosis model.

[0126] Model training

[0127] The training set data is input into the constructed fault diagnosis model. Based on the input motor operating data, the model calculates the predicted anomaly type through forward propagation. The prediction result is compared with the true labels in the training set, and the prediction error is calculated using a loss function (such as cross-entropy loss). The error is then propagated from the output layer to the input layer using a backpropagation algorithm, updating the weights and bias parameters in the network to reduce the loss function value. This process of forward propagation, loss calculation, backpropagation, and parameter updates is repeated until the loss function converges or a preset number of training iterations are reached.

[0128] Model Validation

[0129] The model is evaluated during training using validation set data. The validation set is input into the model to obtain prediction results and calculate metrics such as loss and accuracy on the validation set. Based on the validation results, model hyperparameters, such as learning rate and number of network layers, are adjusted to avoid overfitting, where the model performs well on the training set but degrades on the validation set. Hyperparameters are repeatedly adjusted and validated until the model achieves optimal performance on the validation set.

[0130] Model testing

[0131] Input the test set data into the validated and optimized model, and calculate the model's accuracy, recall, F1 score, and other metrics on the test set to comprehensively evaluate the model's generalization ability. If the test results meet expectations, it indicates that the model has good performance and can be deployed to the 502 cloud platform for motor fault diagnosis; if the results are not ideal, the data needs to be re-examined, the model architecture or training strategy adjusted, and the above training, validation, and testing steps repeated until the model meets the requirements.

[0132] Example 2

[0133] An algorithm flow for fault diagnosis via a cloud platform is provided, such as... Figure 6 As shown, it includes:

[0134] Data Input Layer: As the starting point of the process, the original parameter data of the motor, as well as historical and real-time multi-dimensional monitoring data (temperature, vibration, current, noise, etc.) are input to provide basic materials for subsequent analysis.

[0135] Model processing unit: preprocesses the input data, including data standardization (unifying data format and units), data extraction (filtering key information), data augmentation (expanding / optimizing data), and data annotation (labeling for model training), making the data more suitable for the model.

[0136] Core algorithm architecture: It uses multi-mode fusion analysis (integrating multiple analysis modes), historical curve comparison (finding patterns with past data), and setting a matching mechanism (establishing fault matching rules) to mine fault-related features in the data.

[0137] Model optimization architecture: By using transfer learning (optimizing the model by leveraging existing knowledge), balancing strategies (handling the problem of data imbalance), and multi-task learning (simultaneously conducting multi-task training to improve the model's generalization), the core algorithm is optimized to improve diagnostic accuracy.

[0138] Output layer: The end point of the process outputs the predicted results of motor problems, visualizes the diagnostic basis, and can also perform preset processing based on the results to complete the fault diagnosis closed loop.

[0139] In another embodiment provided in this disclosure, the cloud platform 502 is also used to analyze the received motor operation data based on the motor number, obtain the degree of degradation of the motor, and adjust the detection parameter threshold based on the degree of degradation of the motor.

[0140] The degree of motor degradation does not exceed the preset value.

[0141] In this embodiment, the cloud platform 502 not only undertakes the task of intelligent diagnosis, but its in-depth analysis function of motor operating data further optimizes the adaptability and accuracy of local diagnosis. It assesses the degree of degradation by analyzing operating data based on the motor number, and adjusts the detection parameter thresholds used to generate the diagnostic report locally accordingly.

[0142] The cloud platform 502 analyzes received motor operating data based on the motor's serial number. Each motor has a unique serial number. The cloud platform 502 accesses the motor's historical data, integrating and analyzing trends in operating data such as vibration frequency, noise level, temperature, and current at different times. For example, if a motor's vibration frequency data shows a gradual upward trend over three months, the platform compares this historical data with standard data for the same model and uses an algorithm to calculate the degree of degradation. When the degree of degradation is within a reasonable range (not exceeding a preset value), the cloud platform 502 adjusts the corresponding detection parameter thresholds for the motor based on the degradation situation. If the motor's bearings experience slight wear due to long-term operation, causing the vibration frequency to increase compared to its initial state, the cloud platform 502 appropriately raises the normal threshold range for vibration frequency, making the diagnostic criteria more closely match the actual operating state of the motor and avoiding misjudgments due to rigid thresholds.

[0143] In one possible implementation, data change trends can be analyzed using statistical models, or the threshold adjustment strategy can be continuously optimized using machine learning algorithms. For example, a time series analysis model can be used to predict motor performance change trends and make preventative adjustments to the threshold in advance; or a reinforcement learning algorithm can be used to dynamically optimize the accuracy of threshold adjustment based on actual diagnostic feedback, which is not limited here.

[0144] In summary, the fault diagnosis system provided in this disclosure allows the cloud platform 502 to flexibly adjust local fault diagnosis standards based on the actual operating conditions and degradation trends of individual motors, overcoming the limitations of traditional fixed-threshold diagnosis in adapting to dynamic changes in motor performance. By dynamically adjusting the thresholds, the diagnostic system can reduce false alarms and missed alarms caused by unreasonable thresholds. Combining the real-time data acquisition and local preliminary diagnosis of the handheld motor data acquisition device 501 with the fault diagnosis model of the cloud platform 502, a more intelligent and adaptive motor fault diagnosis system is formed, significantly improving the scientific rigor and reliability of motor operating status monitoring.

[0145] Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments of this disclosure can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0146] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes in the drawings are not necessarily essential for implementing this disclosure.

[0147] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0148] The sequence numbers of the embodiments disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0149] Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, this disclosure is also intended to include such modifications and variations.

Claims

1. A handheld motor data acquisition device, characterized in that, include: Sensor unit, data processing unit, human-computer interaction module and power management module; The sensor unit is used to achieve synchronous acquisition of multiple parameters through a timestamp synchronization algorithm; The data processing unit is used to perform real-time data filtering, FFT analysis and feature extraction using built-in chips and embedded processors, and to generate diagnostic reports based on detection parameter thresholds. The human-computer interaction module is used to provide a touchscreen that supports real-time waveform display and operation command input, and to display the diagnostic report; The power management module supports fast charging and low-power modes for charging the device.

2. The apparatus as claimed in claim 1, characterized in that, The sensor unit includes at least one of the following: a near-field communication (NFC) module, a vibration sensor, a temperature sensor, a noise sensor, and a non-invasive current clamp probe.

3. The apparatus as described in claim 1, characterized in that, The device further includes: a wireless transmission module for supporting Wi-Fi, Bluetooth and 4G multi-mode communication, which can upload data to a cloud platform and / or a local server and receive cloud diagnostic reports issued by the cloud platform and / or the local server; The human-computer interaction module is also used to display the cloud diagnostic report.

4. The apparatus as claimed in claim 1, characterized in that, The device uses a magnetically attached housing.

5. The apparatus as described in claim 2, characterized in that, The non-invasive current clamp probe has a foldable structure.

6. A control method for a handheld motor data acquisition device, characterized in that, include: The motor number is identified by the near field communication (NFC) module, and the corresponding detection parameter threshold is retrieved based on the motor number. The sensor unit synchronously collects motor operation data for a preset duration and displays waveforms and spectrum diagrams in real time through the human-machine interaction module. A diagnostic report is generated based on the detection parameter thresholds, and the risk level is indicated through the human-computer interaction module based on the diagnostic report; The motor operation data is uploaded to the cloud platform, and the cloud diagnostic report generated by the cloud platform is received and displayed through the human-computer interaction module.

7. The method as described in claim 6, characterized in that, The sensor unit includes at least one of the following: a near-field communication (NFC) module, a vibration sensor, a temperature sensor, a noise sensor, and a non-invasive current clamp probe; The sensor unit synchronously collects motor operation data for a preset duration, including: At least two sensors in the sensor unit collect motor operation data for a preset duration and use a timestamp synchronization algorithm to reduce the time delay error between the at least two sensors.

8. The method as described in claim 6, characterized in that, The motor operating data includes at least one of the following: vibration frequency, noise level, temperature, and current; The generation of a diagnostic report based on detection parameter thresholds includes: The motor operating data are compared with the corresponding detection parameter thresholds. If the vibration frequency is not within the range of the corresponding vibration threshold, a diagnostic report is generated indicating vibration abnormality; and / or If the noise value is outside the range of the corresponding noise threshold, a diagnostic report is generated indicating noise abnormality; and / or If the temperature is outside the corresponding temperature threshold range, a diagnostic report is generated indicating a temperature abnormality; and / or If the current is outside the range of the corresponding current threshold, a diagnostic report is generated as "current abnormality".

9. A motor fault diagnosis system, characterized in that, include: The handheld motor data acquisition device and cloud platform as described in claim 1; The handheld motor data acquisition device is used to collect motor operation data within a preset time period; The installed embedded processor generates a diagnostic report based on detection parameter thresholds, and the risk level is indicated through the human-computer interaction module based on the diagnostic report; The handheld motor data acquisition device is also used to send the motor operation data to the cloud platform, receive the cloud diagnostic report issued by the cloud platform, and display it through the human-computer interaction module. The cloud platform is used to input the motor operation data into a pre-trained fault diagnosis model to obtain a cloud diagnosis report.

10. The system as described in claim 9, characterized in that, The cloud platform is also used to analyze the received motor operation data based on the motor number, obtain the degree of motor degradation, and adjust the detection parameter threshold based on the degree of motor degradation. The degree of degradation of the motor does not exceed a preset value.

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