Shafting monitoring method and device, monitoring equipment and storage medium

Abstract

The invention relates to a shafting monitoring method and device, monitoring equipment and a storage medium, and belongs to the technical field of equipment monitoring, and the shafting monitoring method comprises the steps: obtaining a stress measurement value sequence, an environment temperature sequence and a rotating speed measurement value sequence of a target rotating machine in a target time period; correcting the stress measurement value sequence based on the environment temperature sequence to obtain a stress measurement value correction sequence; the rotating speed measurement value sequence is filtered based on a Kalman filtering algorithm, a rotating speed measurement value correction sequence is obtained, and the noise covariance of the Kalman filtering algorithm is determined based on the historical rotating speed measurement value of the target rotating machine under the normal working condition; and determining a correlation coefficient between the stress and the rotating speed of the target rotating machine in the target time period based on the stress measurement value correction sequence and the rotating speed measurement value correction sequence, and determining whether the target rotating machine is abnormal in the target time period based on the correlation coefficient. According to the invention, the accuracy of the equipment shafting monitoring result is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of equipment monitoring technology, and in particular to a shaft system monitoring method, device, monitoring equipment, and storage medium. Background Technology

[0002] Shaft system monitoring refers to the real-time or periodic detection, analysis, and evaluation of the operating status of the shaft system in rotating machinery (such as electric motors, generators, steam turbines, and ship propulsion systems) to ensure its safe, stable, and efficient operation. Shaft system monitoring typically involves measuring parameters such as vibration, temperature, displacement, speed, and torque, and combining this with data analysis techniques for fault diagnosis and predictive maintenance.

[0003] In existing technologies, shaft monitoring systems mainly rely on local adaptive algorithms or fixed rule configurations, which have many limitations. For example, sensor adaptive compensation (such as strain gauge temperature drift correction and Hall speed calculation) only depends on local preset rules; each sensor works independently and cannot be adjusted in conjunction with other sensors according to operating conditions. Due to these limitations, existing shaft monitoring systems result in errors in the final monitoring results, which in turn reduces the reliability of rotating machinery.

[0004] Therefore, improving the flexibility of shaft monitoring systems and enhancing the accuracy of monitoring results to ensure the reliability of rotating machinery has become an urgent technical problem to be solved. Summary of the Invention

[0005] In view of this, it is necessary to provide a shaft system monitoring method, device, monitoring equipment, and storage medium to solve the problem of insufficient accuracy of monitoring results in existing shaft system monitoring systems.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides a shaft system monitoring method, comprising: Obtain the stress measurement sequence, ambient temperature sequence, and rotational speed measurement sequence of the target rotating machinery within the target time period; The stress measurement value sequence is corrected based on the ambient temperature sequence to obtain a corrected stress measurement value sequence. The rotational speed measurement sequence is filtered using a Kalman filter algorithm to obtain a corrected rotational speed measurement sequence. The noise covariance of the Kalman filter algorithm is determined based on the historical rotational speed measurement values ​​of the target rotating machinery under normal operating conditions. Based on the stress measurement correction sequence and the rotational speed measurement correction sequence, the correlation coefficient between the stress and rotational speed of the target rotating machinery within the target time period is determined, and based on the correlation coefficient, it is determined whether the target rotating machinery has an abnormality within the target time period.

[0007] In one possible implementation, correcting the stress measurement sequence based on the ambient temperature sequence to obtain a corrected stress measurement sequence includes: Based on the ambient temperature and temperature compensation coefficient set of each sampling point, the corresponding temperature compensation coefficient is determined. The set of temperature compensation coefficients is determined based on the historical stress measurement value, the historical stress true value and the historical ambient temperature of the target rotating machinery. Based on the temperature compensation coefficient, the stress measurement value of each sampling point is corrected to obtain the stress measurement value correction sequence.

[0008] In one possible implementation, determining the corresponding temperature compensation coefficient based on the ambient temperature and the set of temperature compensation coefficients at each sampling time includes: The temperature compensation coefficient is determined based on the following formula:

[0009] in, Indicates ambient temperature The corresponding temperature compensation coefficient, ambient temperature Within the temperature range Inside, Indicates ambient temperature The corresponding temperature compensation coefficient, Indicates ambient temperature The corresponding temperature compensation coefficient.

[0010] In one possible implementation, the set of temperature compensation coefficients is determined by least squares fitting based on the historical stress measurements, historical stress values, and historical ambient temperatures of the target rotating machinery, and the noise covariance of the set of temperature compensation coefficients and the Kalman filter algorithm is determined within a cloud server.

[0011] In one possible implementation, determining the correlation coefficient between the stress and rotational speed of the target rotating machinery within a target time period based on the stress measurement correction sequence and the rotational speed measurement correction sequence includes: The correlation coefficient between the stress and rotational speed of the target rotating machinery during the target time period is determined based on the following formula:

[0012] in, This represents the correlation coefficient between stress and rotational speed. The number of sampling points. Indicating the first value in the stress measurement correction sequence The value of each sampling point, This represents the mean of all sampling points in the stress measurement correction sequence. Indicates the first value in the speed measurement correction sequence The value of each sampling point, This represents the mean of all sampling points in the corrected sequence of rotational speed measurements.

[0013] In one possible implementation, determining whether the target rotating machinery exhibits an anomaly within the target time period based on the correlation coefficient includes: If the correlation coefficient is greater than the correlation coefficient threshold, it is determined that the target rotating machinery has an anomaly during the target time period; If the correlation coefficient is less than or equal to the correlation coefficient threshold, it is determined that the target rotating machinery did not exhibit any abnormalities during the target time period.

[0014] In one possible implementation, the method further includes: If the rate of change of rotational speed between two adjacent sampling points is greater than the rate of change of rotational speed threshold, the data sampling frequency is increased. The rate of change of rotational speed between two adjacent sampling points is determined based on the ratio between the difference in rotational speed between the two adjacent sampling points and the sampling time difference.

[0015] On the other hand, the present invention also provides a shaft system monitoring device, comprising: The strain gauge sensor module is used to acquire and output the stress value of the target rotating machinery; Hall sensor module, used to acquire and output the rotational speed value of the target rotating machinery; Storage module, used to store programs; The processing module, coupled to the storage module, is used to execute the program stored in the memory to implement the steps in the shaft monitoring method described in any of the above implementations.

[0016] Secondly, the present invention also provides a monitoring device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the shaft monitoring method described in any of the above implementations.

[0017] Thirdly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps of the shaft monitoring method described in any of the above implementations.

[0018] The beneficial effects of this invention are as follows: The shaft system monitoring method, device, monitoring equipment, and storage medium provided by this invention correct the stress measurement value by ambient temperature, determine the noise covariance of the Kalman filter algorithm by the historical speed measurement value under normal operating conditions, and then correct the speed measurement value by the Kalman filter algorithm, thereby improving the accuracy of the monitoring data. Then, the anomaly judgment of the equipment is made by the correlation between stress value and speed, thereby improving the accuracy of the equipment shaft system monitoring results and ensuring the reliability of equipment operation. This invention effectively improves the accuracy of the equipment shaft system monitoring results. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of an embodiment of the shaft system monitoring method provided by the present invention; Figure 2 This is a schematic diagram of an embodiment of the shaft monitoring system provided by the present invention; Figure 3 A schematic diagram of an embodiment of the Hall sensor adaptive circuit provided by the present invention; Figure 4 A schematic diagram of an embodiment of the adaptive circuit for strain gauge sensors provided by the present invention; Figure 5 This is a schematic flowchart of an embodiment of the cross-sensor linkage and adaptive algorithm provided by the present invention; Figure 6 A schematic flowchart of an embodiment of the wireless dynamic configuration of multiple sensors provided by the present invention; Figure 7 A schematic flowchart of an embodiment of the cloud-based remote device firmware update process provided by the present invention; Figure 8 This is a schematic diagram of an embodiment of the shaft monitoring device provided by the present invention; Figure 9 A schematic diagram of an embodiment of the monitoring device provided by the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0022] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] This invention provides a shaft system monitoring method, device, monitoring equipment, and storage medium, which are described below.

[0025] Figure 1 This is a schematic flowchart of an embodiment of the shaft system monitoring method provided by the present invention, as shown below. Figure 1 As shown, this shaft system monitoring method can be applied to the monitoring of shaft system operating status in rotating machinery (such as electric motors, generators, steam turbines, and ship propulsion systems). The shaft system monitoring method includes: S101. Obtain the stress measurement sequence, ambient temperature sequence, and rotational speed measurement sequence of the target rotating machinery within the target time period.

[0026] It should be noted that, in order to achieve shaft system monitoring of the target rotating machinery, the stress measurement sequence, ambient temperature sequence, and rotational speed measurement sequence of the target rotating machinery can first be obtained within the target time period (e.g., within a set 1 hour). Typically, the data sampling frequency within the target time period can be set to 100Hz.

[0027] S102. Based on the ambient temperature sequence, the stress measurement value sequence is corrected to obtain the stress measurement value correction sequence.

[0028] It should be noted that since ambient temperature has a significant impact on stress measurement values, in order to improve the accuracy of monitoring results, the stress measurement values ​​can be corrected according to the ambient temperature to obtain stress values ​​that are closer to the true values.

[0029] S103. The rotational speed measurement sequence is filtered based on the Kalman filter algorithm to obtain a corrected rotational speed measurement sequence. The noise covariance of the Kalman filter algorithm is determined based on the historical rotational speed measurement values ​​of the target rotating machinery under normal operating conditions.

[0030] It should be noted that in order to remove noise from the speed measurement values, the noise covariance of the Kalman filter algorithm can be determined by the historical speed measurement values ​​of the target rotating machinery under normal operating conditions. Then, the speed measurement value sequence can be filtered by the Kalman filter algorithm to remove the noise and obtain a corrected speed measurement value sequence, thereby further improving the accuracy of the monitoring data.

[0031] S104. Based on the stress measurement correction sequence and the rotation speed measurement correction sequence, determine the correlation coefficient between the stress and rotation speed of the target rotating machinery within the target time period, and determine whether the target rotating machinery has an abnormality within the target time period based on the correlation coefficient.

[0032] It should be noted that after obtaining the corrected sequences of stress and rotational speed measurements, the correlation coefficient between stress and rotational speed of the target rotating machinery can be determined based on these two sequences. Under normal operating conditions, stress and rotational speed exhibit a moderate correlation, and the correlation coefficient remains within a stable range. When a bearing fails, the shaft system's operating state changes, and the correlation characteristics between stress and rotational speed change significantly, resulting in a significant increase in the correlation coefficient. Therefore, the correlation coefficient can be used to determine whether the target rotating machinery exhibits any abnormalities within a target time period, thereby enabling shaft system monitoring of the target rotating machinery.

[0033] In summary, the shaft monitoring method provided by this invention corrects stress measurements by adjusting ambient temperature, determines the noise covariance of the Kalman filter algorithm by using historical speed measurements under normal operating conditions, and then corrects the speed measurements using the Kalman filter algorithm to improve the accuracy of the monitoring data. Finally, it judges equipment anomalies by analyzing the correlation between stress values ​​and speed, thereby improving the accuracy of the equipment shaft monitoring results and ensuring the reliability of equipment operation. This invention effectively improves the accuracy of equipment shaft monitoring results.

[0034] In some embodiments of the present invention, the step of correcting the stress measurement value sequence based on the ambient temperature sequence to obtain a corrected stress measurement value sequence includes: Based on the ambient temperature and temperature compensation coefficient set of each sampling point, the corresponding temperature compensation coefficient is determined. The set of temperature compensation coefficients is determined based on the historical stress measurement value, the historical stress true value and the historical ambient temperature of the target rotating machinery. Based on the temperature compensation coefficient, the stress measurement value of each sampling point is corrected to obtain the stress measurement value correction sequence.

[0035] It should be noted that when correcting the stress measurement sequence based on the ambient temperature sequence, the corresponding temperature compensation coefficient can first be determined based on the ambient temperature and temperature compensation coefficient set for each sampling point. The temperature compensation coefficient set can be calculated based on the historical stress measurements, historical true stress values, and historical ambient temperatures of the target rotating machinery. Then, the stress measurement values ​​at each sampling point can be corrected based on the temperature compensation coefficients to obtain a corrected stress measurement sequence, thereby improving the accuracy of the monitoring data.

[0036] In some embodiments of the present invention, determining the corresponding temperature compensation coefficient based on the ambient temperature and temperature compensation coefficient set at each sampling time includes: The temperature compensation coefficient is determined based on the following formula:

[0037] in, Indicates ambient temperature The corresponding temperature compensation coefficient, ambient temperature Within the temperature range Inside, Indicates ambient temperature The corresponding temperature compensation coefficient, Indicates ambient temperature The corresponding temperature compensation coefficient.

[0038] It should be noted that when determining the corresponding temperature compensation coefficient based on the ambient temperature and temperature compensation coefficient set at each sampling time, the temperature compensation coefficient can be calculated using the above formula.

[0039] In some embodiments of the present invention, the set of temperature compensation coefficients is determined by least squares fitting based on the historical stress measurements, historical stress values ​​and historical ambient temperatures of the target rotating machinery, and the noise covariance of the set of temperature compensation coefficients and the Kalman filter algorithm is determined in a cloud server.

[0040] It should be noted that when determining the set of temperature compensation coefficients, the set of temperature compensation coefficients can be obtained by least squares fitting based on the historical stress measurements, historical stress values, and historical ambient temperatures of the target rotating machinery. In addition, the set of temperature compensation coefficients and the noise covariance of the Kalman filter algorithm can be determined in the cloud server, thereby reducing the data processing pressure on the on-site monitoring equipment and further improving the efficiency of the on-site equipment.

[0041] In some embodiments of the present invention, determining the correlation coefficient between the stress and rotational speed of the target rotating machinery within a target time period based on the stress measurement correction sequence and the rotational speed measurement correction sequence includes: The correlation coefficient between the stress and rotational speed of the target rotating machinery during the target time period is determined based on the following formula:

[0042] in, This represents the correlation coefficient between stress and rotational speed. The number of sampling points. Indicates the first value in the stress measurement correction sequence The value of each sampling point, This represents the mean of all sampling points in the stress measurement correction sequence. Indicates the first value in the speed measurement correction sequence The value of each sampling point, This represents the mean of all sampling points in the corrected sequence of rotational speed measurements.

[0043] It should be noted that the correlation coefficient between stress and rotational speed can be calculated using the above formula.

[0044] In some embodiments of the present invention, determining whether the target rotating machinery exhibits an anomaly within a target time period based on the correlation coefficient includes: If the correlation coefficient is greater than the correlation coefficient threshold, it is determined that the target rotating machinery has an anomaly during the target time period; If the correlation coefficient is less than or equal to the correlation coefficient threshold, it is determined that the target rotating machinery did not exhibit any abnormalities during the target time period.

[0045] It should be noted that when determining whether a target rotating machine exhibits an anomaly within a target time period based on the correlation coefficient, a correlation coefficient threshold (generally set to 0.6) can be used in conjunction. When the correlation coefficient is greater than the threshold, it can be determined that the target rotating machine exhibits an anomaly within the target time period; when the correlation coefficient is less than or equal to the threshold, it can be determined that the target rotating machine does not exhibit an anomaly within the target time period.

[0046] In some embodiments of the present invention, the method further includes: If the rate of change of rotational speed between two adjacent sampling points is greater than the rate of change of rotational speed threshold, the data sampling frequency is increased. The rate of change of rotational speed between two adjacent sampling points is determined based on the ratio between the difference in rotational speed between the two adjacent sampling points and the sampling time difference.

[0047] It should be noted that when the motor starts or stops, the speed will change significantly in a short period of time. In order to capture stress data under high-speed changing conditions more accurately, when the rate of change of speed between two adjacent sampling points is greater than the speed change rate threshold, the data sampling frequency can be increased to further improve the accuracy of the monitoring data.

[0048] Combination Figure 2 The hardware architecture of the shaft monitoring system provided by this invention consists of an STM32 main control chip equipped with a basic power supply module (using an LM7805 voltage regulator chip to convert the input voltage to +5V required by the STM32, and then using an AMS1117-3.3 to convert +5V to 3.3V to ensure stable power supply to all parts of the system), a crystal oscillator module (8MHz), a reset module, and a startup mode selection module (such as booting from internal FLASH, booting from system memory, etc.), which together maintain the normal operation of the system. Peripherals include a FLASH module (W25Q128), a strain gauge sensor module, and a Hall sensor module for real-time shaft monitoring. A Message Queuing Telemetry Transport (MQTT) protocol is established via a WIFI module (ESP8266) to achieve communication between the system devices and the cloud, realizing a complete wireless shaft monitoring system.

[0049] Combination Figure 3 The strain gauge monitoring provided by this invention employs a Wheatstone full-bridge circuit. H1, H2, H3, and H4 are connected to strain gauges respectively. When the shaft system is subjected to dynamic loads, the resistance of the strain gauges changes, generating a strain signal. The strain signal is amplified and output via an AD620 instrumentation amplifier, and the STM32 main control chip monitors the signal amplitude in real time through the digital-to-analog converter input port ADC1_IN0. The STM32 main control chip communicates with the MCP41010 digital potentiometer via the SPI protocol, achieving 256 levels of adjustable gain, with a gain range of [1, 1000]. This allows for flexible amplification of the weak signal generated by micro-strain, while limiting the gain of the strong signal generated by overload impacts, providing robust hardware support for the implementation of the strain gauge adaptive algorithm.

[0050] Combination Figure 4As can be seen, the Hall speed monitoring provided by the present invention uses a three-wire Hall sensor to collect and output speed signals. The speed signals are processed successively by the digital programmable gain instrumentation amplifier AD8253 and the programmable filter MAX2671. The STM32 main control chip monitors the cut-off frequency in real time through the analog-to-digital conversion input port ADC1_IN1. Among them, the STM32 main control chip can finely adjust the signal gain of AD8253 through different level combinations of the MCU_PA1 and MCU_PA2 pins (the specific adjustment logic is: 00 corresponds to a gain of 1, 01 corresponds to a gain of 10, 10 corresponds to a gain of 100, 11 corresponds to a gain of 1000), and sends specific instructions to MAX2671 through the MCU_PA3 and MCU_PA4 pins to achieve bandwidth switching, providing hardware support for implementing the Hall sensor adaptive algorithm.

[0051] Combined with Figure 5 As can be seen, the system completes autonomous feedback through real-time monitoring of data to achieve local compensation, and reports data to provide data support for the cloud. The cloud analyzes historical data to optimize the local compensation algorithm, thereby achieving local + cloud adaptive compensation. The system analyzes the monitored data in real time. When the speed or temperature reaches the set value, other sensors are adjusted through an interrupt to achieve cross-sensor linkage.

[0052] Combined with Figure 6 As can be seen, the cloud establishes two-way communication with the edge device (STM32 main control chip) through the MQTT protocol to achieve remote dynamic adjustment of sensor parameters. The cloud management platform generates parameter configuration instructions based on device type, working condition requirements, or historical data analysis results. The configuration instructions are transmitted to the WIFI module (ESP8266) at the device end through the MQTT protocol. The WIFI module forwards them to the STM32 main control chip through the serial port protocol. Among them, the transmitted content includes: each interval of the temperature compensation table , gain adjustment coefficient , Kalman filter noise covariance , speed change rate threshold , stress overrun threshold, correlation coefficient warning threshold, etc. The STM32 main control chip stores the received parameters in the FLASH module (W25Q128), updates the speed mutation trigger threshold adjusts the sampling rate switching logic, and updates the sensor working mode in real time. The strain gauge module executes the temperature compensation algorithm according to the new compensation table; the Hall module loads to the Kalman filter algorithm. After the configuration is completed, the device end sends back the parameter effective status to the cloud.

[0053] Combined with Figure 7This invention employs differential transmission technology based on the MQTT protocol to achieve secure and efficient remote upgrades of sensor firmware. Through a three-level verification mechanism and a dual-partition backup architecture, it significantly reduces data transmission volume and device resource consumption while ensuring a high upgrade success rate. When the cloud detects a security vulnerability or functional defect in the device, it proactively pushes an upgrade command to the device. The command includes the upgrade type (urgent / normal), the target version number, and a checksum. Every 24 hours, the device proactively requests a version comparison from the cloud, verifying the consistency between the local version and the latest version on the cloud using a Secure Hash Algorithm (SHA) -256. The cloud uses the Myers differential algorithm to generate an upgrade patch, transmitting only the differences between the old and new firmware. The firmware is divided into 4KB blocks, and a Cyclic Redundancy Check (CRC) 32 checksum is calculated for each block. Only the content of the changed blocks is transmitted; unchanged blocks reuse the local version through block indexing. MQTT messages are encrypted using Transport Layer Security (TLS) 1.3, and each data packet contains a Hash-based Message Authentication Code (HMAC) - SHA256 signature. After the firmware download is complete, the overall MD5 value is calculated and compared with the checksum sent from the cloud. Upon system startup after the upgrade, critical code segments are verified using a hardware CRC engine. The FLASH memory is divided into a main program area and a backup area. Before the upgrade, the contents of the main program area are completely copied to the backup area, and a backup flag is set. When the new firmware is first started, only the core modules are loaded for self-testing. If the self-test passes, the full-featured modules are loaded. If the self-test fails, the system automatically jumps to the backup area and reports an upgrade failure code.

[0054] This invention overcomes the static limitations of traditional shaft monitoring systems through an "edge-cloud" collaborative architecture, a multi-sensor linkage mechanism, and wireless dynamic configuration. Compared with existing technologies, this invention has the following technical advantages: 1. The adaptive compensation capability of the sensor is significantly improved. The strain gauge sensor achieves full-temperature range compensation [-20℃, 80℃] by combining piecewise linear interpolation algorithm with cloud-based least squares fitting. The stress measurement error caused by temperature is reduced by more than 60% compared with traditional local fixed compensation. The cloud-based compensation coefficient table is optimized based on historical data of similar equipment, overcoming the limitations of self-learning of single device parameters. The compensation coefficient update cycle is shortened from manual quarterly calibration to real-time dynamic optimization. The Hall sensor adopts a stress-coupled Kalman filter algorithm to jointly model the rotational speed and acceleration state vectors, and optimizes the noise covariance through cloud-based optimization. It reduces the speed fluctuation error from ±5RPM to within ±2RPM, effectively suppressing interference from scenarios such as motor start-up and shutdown, and sudden load changes.

[0055] 2. Breakthrough in multi-sensor collaborative monitoring and fault early warning capabilities: When the Hall sensor detects a sudden change in rotational speed, it automatically triggers the strain gauge sampling rate to increase to 500Hz and adjusts the gain, improving the high-frequency stress signal capture capability by 5 times, enabling real-time tracking of dynamic load changes in the shaft system. Based on stress-speed correlation coefficient analysis, the correlation is approximately 0.6 under normal operating conditions, but surges to over 0.9 during bearing failure, allowing for early identification of mechanical faults and significantly earlier warning times compared to traditional single-sensor monitoring. By integrating multi-dimensional data such as strain gauge stress and Hall speed, a correlation model of the shaft system's operating status is established, increasing the fault identification accuracy from 75% in traditional solutions to over 92%, while reducing the false alarm rate from 8% to below 2%, significantly improving the reliability of the monitoring system.

[0056] 3. Wireless dynamic configuration and remote maintenance: Sensor parameters (such as temperature compensation tables and filter thresholds) are sent to the cloud in real time via the MQTT protocol, significantly reducing on-site debugging time and greatly improving equipment deployment efficiency. Utilizing differential transmission technology (Myers algorithm) and a dual-partition backup architecture, firmware upgrade data transmission volume is reduced by approximately 70%, upgrade success rate increases from 85% to 99%, and automatic rollback is supported to avoid equipment downtime due to upgrade failures.

[0057] 4. Cloud-based collaboration and predictive maintenance: The cloud aggregates operational data from multiple devices to build a digital model of the shaft system, enabling global optimization of compensation coefficients, filtering parameters, etc., reducing deployment costs for similar equipment by more than 50%. Based on historical data time-series analysis, it can predict sensor performance degradation trends and trigger maintenance reminders in advance, transforming passive downtime maintenance into proactive preventative maintenance, reducing unplanned equipment downtime by 40%.

[0058] 5. Enhanced hardware compatibility and communication security: The strain gauge module controls the digital potentiometer (MCP41010) via a Serial Peripheral Interface (SPI) to achieve 256 levels of gain adjustment. The Hall effect sensor module adjusts the AD8253 gain (1 / 10 / 100 / 1000 times) through a combination of general-purpose input / output (GPIO) pins, adapting to sensors with different ranges and significantly improving hardware compatibility. The power supply module adopts a two-stage voltage regulation design (LM7805 + AMS1117-3.3) to ensure stable operation of the system within ±15% of the input voltage fluctuation range. TLS encryption and HMAC-SHA256 signature mechanisms are used to reduce the risk of communication data tampering.

[0059] To better implement the shaft system monitoring method in the embodiments of the present invention, based on the shaft system monitoring method, correspondingly, as follows: Figure 8As shown, this embodiment of the invention also provides a shaft system monitoring device, the shaft system monitoring device 800 including: The strain gauge sensor module 801 is used to acquire and output the stress value of the target rotating machinery; Hall sensor module 802 is used to acquire and output the rotational speed value of the target rotating machinery; Storage module 803 is used to store programs; The processing module 804, coupled to the storage module, is used to execute the program stored in the memory to implement the steps in the shaft monitoring method described in any of the above implementations.

[0060] The shaft system monitoring device 800 provided in the above embodiments can realize the technical solutions described in the above shaft system monitoring method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above shaft system monitoring method embodiments, and will not be repeated here.

[0061] like Figure 9 As shown, the present invention also provides a monitoring device 900. The monitoring device 900 includes a processor 901, a memory 902, and a display 903. Figure 9 Only some components of the monitoring device 900 are shown; however, it should be understood that implementation of all shown components is not required, and more or fewer components may be implemented instead.

[0062] In some embodiments, processor 901 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 902 or process data, such as the magnetic resonance image optimization method of the present invention.

[0063] In some embodiments, processor 901 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 901 may be local or remote. In some embodiments, processor 901 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0064] In some embodiments, memory 902 may be an internal storage unit of monitoring device 900, such as a hard disk or memory of monitoring device 900. In other embodiments, memory 902 may also be an external storage device of monitoring device 900, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on monitoring device 900.

[0065] Furthermore, the memory 902 may include both internal storage units of the monitoring device 900 and external storage devices. The memory 902 is used to store the application software and various types of data installed on the monitoring device 900.

[0066] In some embodiments, display 903 may be an LED display, a liquid crystal display, a touch-screen liquid crystal display, or an organic light-emitting diode (OLED) touchscreen, etc. Display 903 is used to display information from monitoring device 900 and to display a user interface for visualization. Components 901-903 of monitoring device 900 communicate with each other via a system bus.

[0067] In one embodiment, when the processor 901 executes the shaft monitoring program in the memory 902, the following steps can be implemented: Obtain the stress measurement sequence, ambient temperature sequence, and rotational speed measurement sequence of the target rotating machinery within the target time period; The stress measurement value sequence is corrected based on the ambient temperature sequence to obtain a corrected stress measurement value sequence. The rotational speed measurement sequence is filtered using a Kalman filter algorithm to obtain a corrected rotational speed measurement sequence. The noise covariance of the Kalman filter algorithm is determined based on the historical rotational speed measurement values ​​of the target rotating machinery under normal operating conditions. Based on the stress measurement correction sequence and the rotational speed measurement correction sequence, the correlation coefficient between the stress and rotational speed of the target rotating machinery within the target time period is determined, and based on the correlation coefficient, it is determined whether the target rotating machinery has an abnormality within the target time period.

[0068] It should be understood that when the processor 901 executes the shaft monitoring program in the memory 902, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0069] Furthermore, this embodiment of the invention does not specifically limit the type of monitoring device 900 mentioned. The monitoring device 900 can be a portable electronic device such as a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, or laptop computer. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the invention, the monitoring device 900 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0070] Accordingly, this application also provides a computer-readable storage medium for storing a computer-readable program or instruction. When the program or instruction is executed by a processor, it can implement the steps or functions of the shaft monitoring method provided in the above-described method embodiments.

[0071] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0072] The shaft system monitoring method, device, monitoring equipment, and storage medium provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A shaft system monitoring method, characterized in that, include: Obtain the stress measurement sequence, ambient temperature sequence, and rotational speed measurement sequence of the target rotating machinery within the target time period; The stress measurement value sequence is corrected based on the ambient temperature sequence to obtain a corrected stress measurement value sequence. The rotational speed measurement sequence is filtered using a Kalman filter algorithm to obtain a corrected rotational speed measurement sequence. The noise covariance of the Kalman filter algorithm is determined based on the historical rotational speed measurement values ​​of the target rotating machinery under normal operating conditions. Based on the stress measurement correction sequence and the rotational speed measurement correction sequence, the correlation coefficient between the stress and rotational speed of the target rotating machinery within the target time period is determined, and based on the correlation coefficient, it is determined whether the target rotating machinery has an abnormality within the target time period.

2. The shaft system monitoring method according to claim 1, characterized in that, The step of correcting the stress measurement value sequence based on the ambient temperature sequence to obtain a corrected stress measurement value sequence includes: Based on the ambient temperature and temperature compensation coefficient set of each sampling point, the corresponding temperature compensation coefficient is determined. The set of temperature compensation coefficients is determined based on the historical stress measurement value, the historical stress true value and the historical ambient temperature of the target rotating machinery. Based on the temperature compensation coefficient, the stress measurement value of each sampling point is corrected to obtain the stress measurement value correction sequence.

3. The shaft system monitoring method according to claim 2, characterized in that, The determination of the corresponding temperature compensation coefficient based on the ambient temperature and temperature compensation coefficient set at each sampling time includes: The temperature compensation coefficient is determined based on the following formula: in, Indicates ambient temperature The corresponding temperature compensation coefficient, ambient temperature Within the temperature range Inside, Indicates ambient temperature The corresponding temperature compensation coefficient, Indicates ambient temperature The corresponding temperature compensation coefficient.

4. The shaft system monitoring method according to claim 2, characterized in that, The set of temperature compensation coefficients is determined by least squares fitting based on the historical stress measurements, historical stress values, and historical ambient temperatures of the target rotating machinery, and the noise covariance of the set of temperature compensation coefficients and the Kalman filter algorithm is determined within a cloud server.

5. The shaft system monitoring method according to claim 1, characterized in that, The determination of the correlation coefficient between stress and rotational speed of the target rotating machinery within the target time period based on the stress measurement correction sequence and the rotational speed measurement correction sequence includes: The correlation coefficient between the stress and rotational speed of the target rotating machinery during the target time period is determined based on the following formula: in, This represents the correlation coefficient between stress and rotational speed. The number of sampling points. Indicates the first value in the stress measurement correction sequence The value of each sampling point, This represents the mean of all sampling points in the stress measurement correction sequence. Indicates the first value in the speed measurement correction sequence The value of each sampling point, This represents the mean of all sampling points in the corrected sequence of rotational speed measurements.

6. The shaft system monitoring method according to claim 1, characterized in that, The step of determining whether the target rotating machinery exhibits an anomaly within the target time period based on the correlation coefficient includes: If the correlation coefficient is greater than the correlation coefficient threshold, it is determined that the target rotating machinery has an anomaly during the target time period; If the correlation coefficient is less than or equal to the correlation coefficient threshold, it is determined that the target rotating machinery did not exhibit any abnormalities during the target time period.

7. The shaft system monitoring method according to any one of claims 1 to 6, characterized in that, The method further includes: If the rate of change of rotational speed between two adjacent sampling points is greater than the rate of change of rotational speed threshold, the data sampling frequency is increased. The rate of change of rotational speed between two adjacent sampling points is determined based on the ratio between the difference in rotational speed between the two adjacent sampling points and the sampling time difference.

8. A shaft system monitoring device, characterized in that, include: The strain gauge sensor module is used to acquire and output the stress value of the target rotating machinery; Hall sensor module, used to acquire and output the rotational speed value of the target rotating machinery; Storage module, used to store programs; A processing module, coupled to the storage module, is used to execute the program stored in the memory to implement the steps in the shaft monitoring method according to any one of claims 1 to 7.

9. A monitoring device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the shaft monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can perform the steps in the shaft monitoring method according to any one of claims 1 to 7.

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