A smart detection system for electric motors

Abstract

This invention relates to the field of acoustic wave detection technology, and more particularly to an intelligent detection system for motors, comprising: a data acquisition module, an anomaly detection module, a type detection module, a risk assessment module, an adjustment module, and an alarm module. This invention achieves accurate detection of the motor's operating status through acoustic wave analysis combined with multiple real-time monitoring parameters. The system can collect the motor's sound pressure level, vibration acceleration, winding current, bearing temperature, speed, and torque in real time, and quickly determine anomalies based on preset thresholds. When an anomaly is detected, the system further analyzes the wear type by combining current, temperature, and torque information, and assesses the severity of risk based on speed and vibration acceleration, thereby providing refined risk management and effectively solving the problems of low fault judgment accuracy and slow response speed caused by data transmission delays and environmental interference.

Description

Technical Field

[0001] This invention relates to the field of motor testing technology, and in particular to an intelligent testing system for motors. Background Technology

[0002] In modern manufacturing, the assembly and testing of motors are crucial steps in ensuring product quality. After assembly, motors must undergo rigorous performance testing to ensure they meet design standards and operate stably. However, traditional testing methods often rely on manual operation, which is time-consuming and susceptible to human error, especially in detecting details such as bearing wear. Bearing wear is a common fault affecting the normal operation of motors, and traditional testing methods often fail to detect even minor bearing wear in a timely manner, leading to abnormal vibrations and noise in the motor. Furthermore, the accuracy and efficiency of testing equipment can become a bottleneck, impacting overall production efficiency. Therefore, improving the automation level of testing, increasing the accuracy of bearing wear detection, and reducing human error have become significant challenges for production lines.

[0003] Patent document CN111965259A discloses a fault detection and inspection system based on sound waves. This system includes a fault detection system and an inspection robot. The fault detection system includes a sound wave acquisition terminal for individually acquiring sound waves from individual motors within a motor group. The sound wave acquisition terminal includes a sound wave detection sensor and a wireless communication module. The sound wave detection sensor acquires sound wave data generated by the vibration of the corresponding motor; one motor corresponds to multiple sound wave detection sensors located at different positions on the motor. The wireless transmission module transmits the sound wave data from the corresponding motor. A cloud server receives the sound wave data transmitted by the wireless communication module and determines whether the corresponding motor is in a faulty state. The inspection robot includes a control mechanism, which includes a wireless communication module. The wireless communication module receives fault information from the cloud server for the corresponding motor and moves to the location of the faulty motor to place a positioning device.

[0004] Therefore, the aforementioned acoustic wave-based fault detection and inspection system has the following problems: Since the system transmits acoustic wave data to the cloud server via a wireless communication module for judgment, there is a data transmission delay, affecting the real-time performance of fault judgment, especially in emergency situations requiring rapid response, making it impossible to handle problems promptly; the system relies on the coordinated operation of multiple acoustic wave detection sensors and wireless communication modules, and if any one of these devices malfunctions, the entire detection system will fail or its performance will degrade; the inspection robot needs to reach the location of the faulty motor based on the fault information sent by the cloud server, a process limited by the robot's movement speed and positioning accuracy, resulting in low inspection efficiency, especially when there are many motors or the faults are widely distributed. Summary of the Invention

[0005] Therefore, the present invention provides an intelligent detection system for motors, which overcomes the problems of low fault judgment accuracy and slow response speed caused by data transmission delay and environmental interference in the prior art by combining real-time acoustic data and operating condition data and dynamic threshold adjustment.

[0006] To achieve the above objectives, the present invention provides an intelligent detection system for motors, comprising:

[0007] The data acquisition module is used to collect real-time sound pressure level, real-time vibration acceleration, real-time winding current value, real-time speed, real-time bearing temperature and real-time torque of the motor on the production line during the operation of the testing station;

[0008] An anomaly determination module, which is connected to the data acquisition module, is used to determine whether the motor has an anomaly based on the real-time sound pressure level and the preset sound pressure level threshold, and form an anomaly determination result.

[0009] The type determination module is connected to the data acquisition module and the anomaly determination module respectively, and is used to determine the wear type based on the anomaly determination result, the real-time winding current value, the real-time bearing temperature and the real-time torque.

[0010] A risk determination module, which is connected to the data acquisition module and the type determination module respectively, is used to determine the severity risk level based on the wear type, the real-time rotational speed and the real-time vibration acceleration;

[0011] An adjustment module, which is connected to the data acquisition module and the risk determination module respectively, is used to adjust the preset sound pressure level threshold according to the severe risk level, the real-time sound pressure level and the real-time vibration acceleration within a preset adjustment time after the severe risk level is determined, thereby forming an adjusted sound pressure level threshold.

[0012] An alarm module, connected to the risk determination module, is used to issue an alarm for the severity risk level determined based on the adjusted sound pressure level threshold.

[0013] Furthermore, the anomaly detection module includes:

[0014] The sound pressure level comparison unit is used to compare the real-time sound pressure level with the preset sound pressure level threshold to form a sound pressure level comparison result;

[0015] An anomaly determination unit, connected to the sound pressure level comparison unit, is used to determine that the motor is abnormal when the sound pressure level comparison result is that the real-time sound pressure level is greater than the preset sound pressure level threshold, and to form an anomaly determination result.

[0016] Furthermore, the type determination module includes:

[0017] The current fluctuation calculation unit is used to calculate the standard deviation of the real-time winding current value within a preset judgment time when the real-time bearing temperature is greater than the preset standard temperature, and to form the current fluctuation value.

[0018] A torque fluctuation calculation unit is used to calculate the standard deviation of the real-time torque within the preset judgment time when the real-time bearing temperature is greater than the preset standard temperature, and to form a torque fluctuation value.

[0019] A type determination unit is connected to the current fluctuation calculation unit and the torque fluctuation calculation unit respectively, and is used to determine the wear type based on the current fluctuation value and the torque fluctuation value.

[0020] Furthermore, the type determination unit includes:

[0021] A normalization subunit is used to normalize the current fluctuation value to form a normalized current fluctuation value, and to normalize the torque fluctuation value to form a normalized torque fluctuation value.

[0022] A consistency calculation subunit, which is connected to the normalization subunit, is used to calculate the correlation coefficient between the normalized current fluctuation value and the normalized torque fluctuation value to form a fluctuation consistency.

[0023] The type determination subunit is connected to the consistency calculation subunit and is used to determine the type of the abnormality as bearing wear when the fluctuation consistency is less than the preset standard consistency, thus forming a wear type.

[0024] Furthermore, the risk determination module includes:

[0025] The rotational speed change rate calculation unit is used to calculate the real-time rotational speed change rate at a preset time interval when a wear type is formed, and to form the rotational speed change rate.

[0026] An acceleration change rate calculation unit is used to calculate the rate of change of the real-time vibration acceleration at a time interval of the preset determined duration when a wear type is formed, and to form an acceleration change rate.

[0027] A risk determination unit, connected to the acceleration fluctuation calculation unit, is used to determine the severity risk level based on the rotational speed change rate and the acceleration change rate.

[0028] Furthermore, the risk determination unit includes:

[0029] The variation deviation calculation subunit is used to calculate the relative deviation between the rotational speed change rate and the acceleration change rate to form the variation deviation;

[0030] A deviation fluctuation calculation subunit, which is connected to the change deviation calculation subunit, is used to calculate the standard deviation of all changes within a preset deviation fluctuation time when the change deviation is greater than a preset change deviation threshold, and form a deviation fluctuation value.

[0031] A risk determination subunit, which is connected to the deviation fluctuation calculation subunit, is used to determine the risk level of the anomaly as a severe risk level when the deviation fluctuation value is greater than a preset deviation fluctuation threshold.

[0032] Furthermore, the adjustment module includes:

[0033] A timestamp recording unit is used to record the timestamp when the severity risk level is determined, forming several timestamps;

[0034] A distribution degree calculation unit, which is connected to the timestamp recording unit, is used to calculate the level distribution degree based on the timestamp;

[0035] A synchronization calculation unit is used to calculate the change in synchronization degree based on the real-time sound pressure level and the real-time vibration acceleration.

[0036] An adjustment unit is connected to the distribution degree calculation unit and the synchronization degree calculation unit respectively, and is used to adjust the preset sound pressure level threshold according to the level distribution degree and the change synchronization degree to form an adjusted sound pressure level threshold.

[0037] Furthermore, the distribution degree calculation unit includes:

[0038] The duration calculation subunit is used to calculate the time interval between any two adjacent timestamps when the number of timestamps is greater than the number of preset intervals, forming several interval durations;

[0039] The distribution degree calculation subunit, which is connected to the duration calculation subunit, is used to calculate the standard deviation of all the interval durations to form the grade distribution degree.

[0040] Furthermore, the synchronization calculation unit includes:

[0041] The sound pressure fluctuation calculation subunit is adjusted to calculate the standard deviation of the real-time sound pressure level and form the adjusted sound pressure fluctuation value.

[0042] The vibration fluctuation calculation subunit is adjusted to calculate the standard deviation of the real-time vibration acceleration and form the adjusted vibration fluctuation value.

[0043] A fluctuation normalization subunit is connected to the adjusted sound pressure fluctuation calculation subunit and the adjusted vibration fluctuation calculation subunit, respectively, to normalize the adjusted sound pressure fluctuation value to form a sound pressure normalized fluctuation value, and to normalize the vibration fluctuation value to form a vibration normalized fluctuation value.

[0044] The synchronization calculation subunit, which is connected to the fluctuation normalization subunit, is used to calculate the relative deviation between the sound pressure normalized fluctuation value and the vibration normalized fluctuation value, thereby forming the change synchronization degree.

[0045] Furthermore, the adjustment unit includes:

[0046] The distribution comparison subunit is used to compare the level distribution degree with the preset standard distribution degree to form a distribution comparison result;

[0047] A synchronous comparison subunit, which is connected to the distribution comparison subunit, is used to compare the change synchronization degree and the preset standard synchronization degree when the distribution comparison result is that the level distribution degree is greater than the preset standard distribution degree, to form a synchronous comparison result;

[0048] An adjustment subunit, connected to the synchronization comparison subunit, is used to increase the preset sound pressure level threshold based on the relative deviation between the preset standard synchronization degree and the changed synchronization degree and a preset adjustment coefficient when the synchronization comparison result is that the changed synchronization degree is less than the preset standard synchronization degree, thereby forming an adjusted sound pressure level threshold.

[0049] Compared with existing technologies, the beneficial effects of this invention are that it achieves accurate detection of motor operating status by combining acoustic wave analysis with multiple real-time monitoring parameters. The system can collect the motor's sound pressure level, vibration acceleration, winding current, bearing temperature, speed, and torque in real time, and quickly determine anomalies based on preset thresholds. When an anomaly is detected, the system further analyzes the wear type by combining current, temperature, and torque information, and assesses the severity risk level based on speed and vibration acceleration, thereby providing refined risk management. The system also has an adaptive adjustment function, which can dynamically optimize the sound pressure level threshold based on the severity risk level, sound pressure level, and vibration acceleration within a specific time period to adapt to different working conditions, improve the accuracy and robustness of anomaly detection, and effectively solve the problems of low fault judgment accuracy and slow response speed caused by data transmission delay and environmental interference.

[0050] Furthermore, the ability to rapidly detect motor anomalies based on real-time sound pressure level changes avoids reliance on complex historical data analysis, improving the immediacy of detection. Simultaneously, the judgment method based on preset sound pressure level thresholds ensures the system's high efficiency and stability, helping to detect motor malfunctions at an early stage, thereby reducing the risk of equipment damage and improving production line reliability and maintenance efficiency.

[0051] Furthermore, by accurately capturing fluctuations in motor winding current and torque when bearing temperature is abnormal, and combining this with data characteristics to determine the wear type, targeted fault diagnosis can be achieved. Compared with single parameter monitoring, this improves the accuracy and reliability of the judgment, effectively identifies abnormal bearing wear, provides data support for predictive maintenance, reduces unplanned equipment downtime, and improves the stability and operating efficiency of the production line.

[0052] Furthermore, by normalizing the current and torque fluctuation values, the influence of dimensional differences can be eliminated, making different data comparable. Consistency calculation ensures the synchronization of their fluctuations; a low consistency indicates that wear has caused current and torque fluctuations to become asynchronous, thus improving the detection accuracy of bearing wear faults. This can effectively improve the system's early warning capability for motor wear, avoiding more serious faults caused by wear.

[0053] Furthermore, by monitoring and calculating the rate of change of rotational speed and vibration acceleration, the operational risks of the motor can be effectively assessed, potential faults or abnormalities can be identified early, production interruptions caused by equipment failures can be avoided, the accuracy and timeliness of early warnings can be improved, the safety and stability of motor operation can be ensured, and maintenance costs and downtime can be reduced.

[0054] Furthermore, through multi-level deviation calculation and fluctuation analysis, the diagnostic accuracy of potential motor faults has been improved. In particular, when judging motor abnormalities, it does not rely on a single parameter change, but comprehensively considers the fluctuation of speed and vibration acceleration, which can effectively identify hidden faults that may be ignored.

[0055] Furthermore, by comprehensively considering changes in time distribution, parameter synchronization, and risk level, the system achieves the function of dynamically adjusting the sound pressure level threshold, ensuring that the risk assessment criteria can be corrected in a timely manner according to changes in actual working conditions. The adaptive adjustment mechanism improves the system's ability to respond to sudden risks, reduces the false alarm rate, and improves the accuracy of motor fault prediction and the overall reliability of the system.

[0056] Furthermore, by analyzing the fluctuations in timestamp intervals, the stability and regularity of the motor during changes in risk level can be accurately assessed. A high standard deviation indicates significant level fluctuations, suggesting abnormal risks, while a low standard deviation shows the stability of level changes, helping the system accurately determine risk change trends and improve the accuracy and response speed of risk assessment.

[0057] Furthermore, by calculating the synchronization degree, it is possible to identify whether the changes in sound pressure and vibration are coordinated under certain operating conditions, thereby more accurately judging the operating status of the motor, timely detecting potential faults, and ensuring the stability and safety of production.

[0058] Furthermore, by effectively adjusting the sound pressure level threshold based on real-time operating data and preset standards, the sensitivity and accuracy of anomaly detection are improved. By comprehensively considering changes in level distribution and synchronization, the system can be adjusted to better adapt to the actual operating state of the motor, which helps to detect potential faults in advance, avoid accidents, and ensure the smooth operation of the production line. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the intelligent detection system for motors in this embodiment;

[0060] Figure 2 This is a logic diagram for the anomaly determination unit in this embodiment to determine the presence of an anomaly;

[0061] Figure 3 This is a logic diagram for determining the wear type in the type determination subunit of this embodiment;

[0062] Figure 4 This is a logic diagram for determining the severity level of the risk determination subunit in this embodiment. Detailed Implementation

[0063] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0064] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0065] Please see Figure 1 As shown, it is a schematic diagram of the intelligent detection system for motors in this embodiment;

[0066] This embodiment provides an intelligent detection system for motors, including:

[0067] The data acquisition module is used to collect real-time sound pressure level, real-time vibration acceleration, real-time winding current value, real-time speed, real-time bearing temperature and real-time torque of the motor on the production line during the operation of the testing station;

[0068] An anomaly determination module, which is connected to the data acquisition module, is used to determine whether the motor has an anomaly based on the real-time sound pressure level and the preset sound pressure level threshold, and form an anomaly determination result.

[0069] The type determination module is connected to the data acquisition module and the anomaly determination module respectively, and is used to determine the wear type based on the anomaly determination result, the real-time winding current value, the real-time bearing temperature and the real-time torque.

[0070] A risk determination module, which is connected to the data acquisition module and the type determination module respectively, is used to determine the severity risk level based on the wear type, the real-time rotational speed and the real-time vibration acceleration;

[0071] An adjustment module, which is connected to the data acquisition module and the risk determination module respectively, is used to adjust the preset sound pressure level threshold according to the severe risk level, the real-time sound pressure level and the real-time vibration acceleration within a preset adjustment time after the severe risk level is determined, thereby forming an adjusted sound pressure level threshold.

[0072] An alarm module, connected to the risk determination module, is used to issue an alarm for the severity risk level determined based on the adjusted sound pressure level threshold.

[0073] The data acquisition module monitors the motor's operating status in real time using multiple high-precision sensors installed at the testing station. Specifically, sound pressure level is acquired by a distributed microphone array, and environmental noise is filtered out using digital signal processing (DSP) technology to ensure measurement accuracy; vibration acceleration is detected by a triaxial accelerometer, and data quality is optimized through signal amplification and filtering circuits; winding current is measured by a Hall current sensor, and a digital signal is acquired through analog-to-digital conversion (ADC); rotational speed is acquired by a photoelectric encoder, and precise speed is obtained through frequency analysis; bearing temperature is detected by an infrared temperature sensor, and temperature fluctuation interference is eliminated using a data filtering algorithm; and torque is measured by a dynamic torque sensor, and data stability is optimized through signal amplification and filtering techniques.

[0074] The preset sound pressure level threshold is a benchmark value used to judge abnormal motor operation. It depends on the motor model, power rating, operating environment and industry standards, and is usually set between 60dB and 90dB. In this embodiment, it is set to 75dB, which helps to reduce false judgments while ensuring detection sensitivity and improving the reliability of the detection system.

[0075] The preset adjustment time is the time window during which the system dynamically adjusts the sound pressure level threshold after detecting an anomaly. It depends on the motor operating status, the speed of fault development, and the production line cycle time. It is usually set between 5s and 30s. In this embodiment, it is set to 10s so that the system can quickly adapt to different working conditions, ensure the accuracy of anomaly detection, and reduce the interference of environmental noise.

[0076] Bearing wear refers to structural damage or performance degradation of motor bearings during testing and operation due to factors such as friction, fatigue, insufficient lubrication, or the intrusion of impurities. In severe cases, bearings may experience spalling, pitting, or seizing, affecting the smooth operation of the motor and even causing mechanical failure.

[0077] The data acquisition module acquires key parameters of the motor in real time, including sound pressure level, vibration acceleration, winding current, speed, bearing temperature, and torque. The anomaly detection module determines whether there is an anomaly in the motor based on the sound pressure level threshold and generates an anomaly detection result. The type detection module analyzes the wear type based on current, temperature, and torque information. The risk assessment module further evaluates the severity of the risk based on the wear type, speed, and vibration acceleration. Subsequently, the adjustment module dynamically adjusts the sound pressure level threshold according to the risk level and changes in sound pressure level and vibration acceleration to optimize detection accuracy. Finally, the alarm module provides an alert for high-risk situations based on the adjusted threshold, enabling timely maintenance measures to be taken.

[0078] By combining acoustic wave analysis with multiple real-time monitoring parameters, the system achieves accurate detection of motor operating status. It can collect the motor's sound pressure level, vibration acceleration, winding current, bearing temperature, speed, and torque in real time, and quickly determine anomalies based on preset thresholds. When an anomaly is detected, the system further analyzes the wear type by combining current, temperature, and torque information, and assesses the severity of risk based on speed and vibration acceleration, thereby providing refined risk management. The system also has an adaptive adjustment function, which can dynamically optimize the sound pressure level threshold based on the severity of risk, sound pressure level, and vibration acceleration within a specific time period to adapt to different operating conditions, improve the accuracy and robustness of anomaly detection, and effectively solve the problems of low fault judgment accuracy and slow response speed caused by data transmission delay and environmental interference.

[0079] Please continue reading. Figure 2 As shown, it is the logic diagram for the anomaly determination unit in this embodiment to determine the existence of an anomaly;

[0080] The anomaly detection module includes:

[0081] The sound pressure level comparison unit is used to compare the real-time sound pressure level with the preset sound pressure level threshold to form a sound pressure level comparison result;

[0082] An anomaly determination unit, connected to the sound pressure level comparison unit, is used to determine that the motor is abnormal when the sound pressure level comparison result is that the real-time sound pressure level is greater than the preset sound pressure level threshold, and to form an anomaly determination result.

[0083] The anomaly detection module first compares the real-time sound pressure level with a preset sound pressure level threshold using a sound pressure level comparison unit to obtain the sound pressure level comparison result. When the real-time sound pressure level exceeds the preset sound pressure level threshold, the anomaly detection unit determines that the motor is abnormal and outputs the anomaly detection result, providing a basis for subsequent wear type identification and risk assessment.

[0084] Real-time sound pressure level changes enable rapid detection of motor anomalies, avoiding reliance on complex historical data analysis and improving the immediacy of detection. Simultaneously, the judgment method based on preset sound pressure level thresholds ensures the system's high efficiency and stability, helping to detect motor malfunctions at an early stage, thereby reducing the risk of equipment damage and improving production line reliability and maintenance efficiency.

[0085] Specifically, the type determination module includes:

[0086] The current fluctuation calculation unit is used to calculate the standard deviation of the real-time winding current value within a preset judgment time when the real-time bearing temperature is greater than the preset standard temperature, and to form the current fluctuation value.

[0087] A torque fluctuation calculation unit is used to calculate the standard deviation of the real-time torque within the preset judgment time when the real-time bearing temperature is greater than the preset standard temperature, and to form a torque fluctuation value.

[0088] A type determination unit is connected to the current fluctuation calculation unit and the torque fluctuation calculation unit respectively, and is used to determine the wear type based on the current fluctuation value and the torque fluctuation value.

[0089] The preset standard temperature is the maximum safe temperature that the bearing should maintain when the motor is working normally. It is set according to the type of motor and the environment in which it is used, and is usually set between 70°C and 100°C. In this embodiment, it is set to 85°C, which can ensure that the motor bearing is within the normal operating temperature range and avoid premature wear or failure of the bearing due to excessive temperature.

[0090] The preset judgment duration is the time period used to calculate the current and torque fluctuation values. It is set according to the operating cycle and fault characteristics of the equipment, and is usually set between 10 and 30 seconds. In this embodiment, it is set to 20 seconds, which helps to smooth out sudden short-term fluctuations, ensure that the data reflects the long-term stability of the motor, and avoid misjudgment.

[0091] The type determination module first detects whether the real-time bearing temperature exceeds the preset standard temperature. When the temperature rises abnormally, the current fluctuation calculation unit calculates the standard deviation of the winding current value within a preset determination period to obtain the current fluctuation value. Simultaneously, the torque fluctuation calculation unit calculates the standard deviation of the real-time torque within the same time period to obtain the torque fluctuation value. Subsequently, the type determination unit comprehensively analyzes the motor's operating status based on the characteristics of the current fluctuation value and the torque fluctuation value to accurately determine the wear type, providing a basis for subsequent risk assessment and maintenance decisions.

[0092] By accurately capturing fluctuations in motor winding current and torque when bearing temperature is abnormal, and combining this data with characteristics to determine the type of wear, targeted fault diagnosis can be achieved. Compared with single parameter monitoring, this improves the accuracy and reliability of the diagnosis, effectively identifies abnormal bearing wear, provides data support for predictive maintenance, reduces unplanned equipment downtime, and improves the stability and operating efficiency of the production line.

[0093] Please continue reading. Figure 3 As shown, this is the logic diagram for determining the wear type in the type determination subunit of this embodiment;

[0094] The type determination unit includes:

[0095] A normalization subunit is used to normalize the current fluctuation value to form a normalized current fluctuation value, and to normalize the torque fluctuation value to form a normalized torque fluctuation value.

[0096] A consistency calculation subunit, which is connected to the normalization subunit, is used to calculate the correlation coefficient between the normalized current fluctuation value and the normalized torque fluctuation value to form a fluctuation consistency.

[0097] The type determination subunit is connected to the consistency calculation subunit and is used to determine the type of the abnormality as bearing wear when the fluctuation consistency is less than the preset standard consistency, thus forming a wear type.

[0098] The preset standard consistency is a threshold used to judge the correlation between current fluctuation value and torque fluctuation value. It depends on the working characteristics of the equipment and the actual operating environment. It is usually set between 0.7 and 0.9 to ensure that normal and abnormal conditions can be distinguished. In this embodiment, it is set to 0.8, which helps to avoid too many false alarms caused by small fluctuations while ensuring high sensitivity, thereby improving the stability and reliability of the system.

[0099] First, the current and torque fluctuation values ​​are normalized by the normalization subunit to form normalized current and torque fluctuation values. Then, the consistency calculation subunit calculates the correlation coefficient between these two normalized fluctuation values ​​to obtain the fluctuation consistency degree. Finally, the type determination subunit compares the calculated fluctuation consistency degree with a preset standard consistency degree. If the consistency degree is lower than the standard, the motor abnormality is determined to be of the bearing wear type.

[0100] By normalizing the current and torque fluctuation values, the influence of dimensional differences can be eliminated, making different data comparable. Consistency calculation ensures the synchronization of their fluctuations; a low consistency indicates that wear has caused current and torque fluctuations to become asynchronous, thus improving the detection accuracy of bearing wear faults. This can effectively improve the system's early warning capability for motor wear, avoiding more serious faults caused by wear.

[0101] Specifically, the risk determination module includes:

[0102] The rotational speed change rate calculation unit is used to calculate the real-time rotational speed change rate at a preset time interval when a wear type is formed, and to form the rotational speed change rate.

[0103] An acceleration change rate calculation unit is used to calculate the rate of change of the real-time vibration acceleration at a time interval of the preset determined duration when a wear type is formed, and to form an acceleration change rate.

[0104] A risk determination unit, connected to the acceleration fluctuation calculation unit, is used to determine the severity risk level based on the rotational speed change rate and the acceleration change rate.

[0105] The preset time interval refers to the time interval used to calculate the rate of change of rotational speed and the rate of change of acceleration. Its setting depends on the operating characteristics of the motor and the fault prediction requirements. It is usually set between 1 second and 10 seconds. In this embodiment, it is set to 5 seconds, which helps to monitor the dynamic changes of the motor in real time. It can capture short-term fluctuations of the motor without missing important risk signals due to the time being too short or too long.

[0106] The risk assessment module calculates the rates of change of real-time rotational speed and vibration acceleration over a preset time period using a speed change rate calculation unit and an acceleration change rate calculation unit, respectively. These rates of change reflect the instability or abnormal fluctuations of the motor during operation, helping to assess the risk level of the equipment. Based on the calculated speed and acceleration change rates, the risk assessment unit further determines whether the motor is at a severe risk level.

[0107] By monitoring and calculating the rate of change of rotational speed and vibration acceleration, the operational risks of the motor can be effectively assessed, potential faults or abnormalities can be identified early, production interruptions caused by equipment failure can be avoided, the accuracy and timeliness of early warning can be improved, the safety and stability of motor operation can be ensured, and maintenance costs and downtime can be reduced.

[0108] Please continue reading. Figure 4 As shown, this is the logic diagram for determining the severity level of the risk in the risk determination subunit of this embodiment;

[0109] The risk determination unit includes:

[0110] The variation deviation calculation subunit is used to calculate the relative deviation between the rotational speed change rate and the acceleration change rate to form the variation deviation;

[0111] A deviation fluctuation calculation subunit, which is connected to the change deviation calculation subunit, is used to calculate the standard deviation of all changes within a preset deviation fluctuation time when the change deviation is greater than a preset change deviation threshold, and form a deviation fluctuation value.

[0112] A risk determination subunit, which is connected to the deviation fluctuation calculation subunit, is used to determine the risk level of the anomaly as a severe risk level when the deviation fluctuation value is greater than a preset deviation fluctuation threshold.

[0113] The preset deviation threshold is a standard used to determine whether the relative deviation of the rate of change of speed and the rate of change of acceleration exceeds the normal range. It depends on the operating characteristics of the motor, the changes in operating conditions, and the design specifications of the equipment. It is usually set between 0.05 and 0.1. In this embodiment, it is set to 0.07, which can ensure that key operating deviations are captured, prevent abnormalities from being judged too early or too late, help to detect motor problems in a timely manner and reduce false alarms.

[0114] The preset deviation fluctuation duration refers to the time interval selected when calculating the deviation fluctuation value. It is set according to the working cycle of the motor and the fault development trend, and is usually set between 1 minute and 5 minutes. In this embodiment, it is set to 3 minutes, which can balance capturing enough fluctuation data, while avoiding misjudgment caused by fluctuations that are too short, and ensuring the stability and accuracy of risk assessment.

[0115] The preset deviation fluctuation threshold is a standard used to determine whether the fluctuation value exceeds the normal range. It depends on the normal operating fluctuation range of the equipment and is usually set between 0.05 and 0.2. In this embodiment, it is set to 0.1, which can reasonably avoid false alarms caused by environmental or instantaneous fluctuations, and effectively identify potential risk fluctuations, thereby making accurate risk assessments and warnings.

[0116] The risk assessment unit evaluates the stability of motor operation by calculating the relative deviation between the rate of change of rotational speed and the rate of change of vibration acceleration. If the deviation exceeds a preset threshold, the system further calculates the standard deviation of all deviations within a preset time period, i.e., the deviation fluctuation value. If the deviation fluctuation value exceeds the preset threshold, the system ultimately determines the motor's abnormal risk level to be a severe risk level. Through this series of calculations and judgments, the risk level of the motor can be accurately assessed.

[0117] By using multi-level deviation calculation and fluctuation analysis, the diagnostic accuracy of potential motor faults has been improved. In particular, when judging motor abnormalities, it does not rely on a single parameter change, but comprehensively considers the fluctuation of speed and vibration acceleration, which can effectively identify hidden faults that may be overlooked.

[0118] Specifically, the adjustment module includes:

[0119] A timestamp recording unit is used to record the timestamp when the severity risk level is determined, forming several timestamps;

[0120] A distribution degree calculation unit, which is connected to the timestamp recording unit, is used to calculate the level distribution degree based on the timestamp;

[0121] A synchronization calculation unit is used to calculate the change in synchronization degree based on the real-time sound pressure level and the real-time vibration acceleration.

[0122] An adjustment unit is connected to the distribution degree calculation unit and the synchronization degree calculation unit respectively, and is used to adjust the preset sound pressure level threshold according to the level distribution degree and the change synchronization degree to form an adjusted sound pressure level threshold.

[0123] The timestamp recording unit records the time information when the severe risk level is determined, forming multiple timestamp data. The distribution calculation unit calculates the level distribution degree based on these timestamps to reflect the changing pattern of the risk level. The synchronization calculation unit calculates the change synchronization degree based on the real-time changes in sound pressure level and vibration acceleration, reflecting the consistency of these two parameters. Finally, the adjustment unit combines the level distribution degree and synchronization degree to adjust the preset sound pressure level threshold, thereby obtaining a new adjusted sound pressure level threshold for more accurate assessment of motor risk.

[0124] By comprehensively considering changes in time distribution, parameter synchronization, and risk level, the system achieves dynamic adjustment of sound pressure level thresholds, ensuring that risk assessment standards can be corrected in a timely manner according to changes in actual working conditions. The adaptive adjustment mechanism improves the system's response capability to sudden risks, reduces false alarm rate, and enhances the accuracy of motor fault prediction and the overall reliability of the system.

[0125] Specifically, the distribution degree calculation unit includes:

[0126] The duration calculation subunit is used to calculate the time interval between any two adjacent timestamps when the number of timestamps is greater than the number of preset intervals, forming several interval durations;

[0127] The distribution degree calculation subunit, which is connected to the duration calculation subunit, is used to calculate the standard deviation of all the interval durations to form the grade distribution degree.

[0128] The preset interval number refers to the minimum number used to determine whether to calculate the timestamp interval in the distribution degree calculation. It depends on the system's data collection frequency, the accuracy requirements of anomaly detection, and the length of the time period. It is usually set between 10 and 30. In this embodiment, it is set to 20 to ensure the representativeness of the data and the stability of the calculation.

[0129] First, the duration calculation subunit determines when the number of timestamps exceeds the preset interval number, then calculates the time interval between any two adjacent timestamps, thus obtaining multiple interval duration data. Next, the distribution calculation subunit processes all these interval duration data, calculates their standard deviation, and thus obtains the rank distribution degree, reflecting the degree of fluctuation of the time interval.

[0130] By analyzing the fluctuations in timestamp intervals, the stability and regularity of motors during changes in risk level can be accurately assessed. A high standard deviation indicates significant level fluctuations, suggesting abnormal risk, while a low standard deviation shows the stability of level changes, helping the system accurately determine risk trends and improve the accuracy and response speed of risk assessment.

[0131] Specifically, the synchronization calculation unit includes:

[0132] The sound pressure fluctuation calculation subunit is adjusted to calculate the standard deviation of the real-time sound pressure level and form the adjusted sound pressure fluctuation value.

[0133] The vibration fluctuation calculation subunit is adjusted to calculate the standard deviation of the real-time vibration acceleration and form the adjusted vibration fluctuation value.

[0134] A fluctuation normalization subunit is connected to the adjusted sound pressure fluctuation calculation subunit and the adjusted vibration fluctuation calculation subunit, respectively, to normalize the adjusted sound pressure fluctuation value to form a sound pressure normalized fluctuation value, and to normalize the vibration fluctuation value to form a vibration normalized fluctuation value.

[0135] The synchronization calculation subunit, which is connected to the fluctuation normalization subunit, is used to calculate the relative deviation between the sound pressure normalized fluctuation value and the vibration normalized fluctuation value, thereby forming the change synchronization degree.

[0136] First, the standard deviation of the real-time sound pressure level calculated by the sound pressure fluctuation calculation subunit is adjusted to obtain the adjusted sound pressure fluctuation value. Then, the standard deviation of the real-time vibration acceleration calculated by the vibration fluctuation calculation subunit is adjusted to obtain the adjusted vibration fluctuation value. Next, the fluctuation normalization subunit normalizes these two fluctuation values ​​to form the normalized sound pressure fluctuation value and the normalized vibration fluctuation value, respectively. Finally, the synchronization calculation subunit calculates the relative deviation between these two normalized fluctuation values ​​to obtain the change synchronization degree.

[0137] By calculating the synchronization degree, it is possible to identify whether the changes in sound pressure and vibration are coordinated under certain operating conditions, thereby more accurately judging the operating status of the motor, timely detecting potential faults, and ensuring the stability and safety of production.

[0138] Specifically, the adjustment unit includes:

[0139] The distribution comparison subunit is used to compare the level distribution degree with the preset standard distribution degree to form a distribution comparison result;

[0140] A synchronous comparison subunit, which is connected to the distribution comparison subunit, is used to compare the change synchronization degree and the preset standard synchronization degree when the distribution comparison result is that the level distribution degree is greater than the preset standard distribution degree, to form a synchronous comparison result;

[0141] An adjustment subunit, connected to the synchronization comparison subunit, is used to increase the preset sound pressure level threshold based on the relative deviation between the preset standard synchronization degree and the changed synchronization degree and a preset adjustment coefficient when the synchronization comparison result is that the changed synchronization degree is less than the preset standard synchronization degree, thereby forming an adjusted sound pressure level threshold. The relative deviation between the preset standard synchronization degree and the changed synchronization degree is positively correlated with the adjusted sound pressure level threshold.

[0142] The preset standard distribution degree refers to a fixed standard value used for comparison calculation. It is usually set based on the historical data of the equipment operation or the expected performance. It is usually set between 0 and 1. In this embodiment, it is set to 0.5, which helps to accurately determine whether there are abnormal fluctuations during operation.

[0143] The preset standard synchronization degree is a fixed reference value used to compare the change in synchronization degree. It reflects the synchronization between real-time sound pressure level and vibration acceleration. It is usually set according to the normal operating characteristics of the equipment to ensure that the synchronization change does not exceed a certain threshold during system operation. It is usually set between 0.2 and 0.5. In this embodiment, it is set to 0.3, which helps to determine whether abnormal synchronization changes have occurred during the operation of the equipment.

[0144] The preset adjustment coefficient is a constant used to adjust the sound pressure level threshold. It reflects the degree of influence of different synchronization changes on the sound pressure level threshold adjustment. It is set according to the equipment's operating environment, load conditions, and expected safety margin. It is usually set between 0.1 and 0.3. In this embodiment, it is set to 0.2, which helps to ensure that the adjusted threshold does not deviate excessively from the actual needs, while also improving the system's response speed to anomalies.

[0145] First, the distribution comparison subunit compares the calculated level distribution degree with the preset standard distribution degree to obtain the distribution comparison result. Then, when the level distribution degree is greater than the preset standard distribution degree, the synchronization comparison subunit further compares the change synchronization degree with the preset standard synchronization degree to form a synchronization comparison result. Finally, when the change synchronization degree is less than the preset standard synchronization degree, the adjustment subunit increases the preset sound pressure level threshold based on the relative deviation between the standard synchronization degree and the change synchronization degree and the preset adjustment coefficient to form the adjusted sound pressure level threshold.

[0146] By effectively adjusting the sound pressure level threshold based on real-time operating data and preset standards, the sensitivity and accuracy of anomaly detection are improved. By comprehensively considering changes in level distribution and synchronization, the system can be adjusted to better adapt to the actual operating state of the motor, which helps to detect potential faults in advance, avoid accidents, and ensure the smooth operation of the production line.

[0147] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An intelligent detection system for an electric machine, characterized in that, The application relates to a motor fault diagnosis system. The system comprises: a data acquisition module for acquiring real-time sound pressure level, real-time vibration acceleration, real-time winding current value, real-time rotating speed, real-time bearing temperature and real-time torque of a motor on a production line during operation at a detection station; an abnormality determination module connected with the data acquisition module for determining existence of abnormality of the motor according to the real-time sound pressure level and a preset sound pressure level threshold to form an abnormality determination result; a type determination module connected with the data acquisition module and the abnormality determination module for determining a wear type according to the abnormality determination result, the real-time winding current value, the real-time bearing temperature and the real-time torque; a risk determination module connected with the data acquisition module and the type determination module for determining a serious risk level according to the wear type, the real-time rotating speed and the real-time vibration acceleration; an adjustment module connected with the data acquisition module and the risk determination module for adjusting the preset sound pressure level threshold according to the serious risk level, the real-time sound pressure level and the real-time vibration acceleration within a preset adjustment time length after the serious risk level is determined to form an adjusted sound pressure level threshold; an alarm module connected with the risk determination module for issuing an alarm for the serious risk level determined based on the adjusted sound pressure level threshold; the abnormality determination module comprises: a sound pressure level comparison unit for comparing the real-time sound pressure level with the preset sound pressure level threshold to form a sound pressure level comparison result; an abnormality determination unit connected with the sound pressure level comparison unit for determining existence of abnormality of the motor when the sound pressure level comparison result is that the real-time sound pressure level is greater than the preset sound pressure level threshold to form an abnormality determination result; the type determination module comprises: a current fluctuation calculation unit for calculating a standard deviation of the real-time winding current value within a preset determination time length when the real-time bearing temperature is greater than a preset standard temperature to form a current fluctuation value; a torque fluctuation calculation unit for calculating a standard deviation of the real-time torque within the preset determination time length when the real-time bearing temperature is greater than the preset standard temperature to form a torque fluctuation value; 2. The intelligent detection system for electric machines according to claim 1, characterized in that, a type determination unit connected with the current fluctuation calculation unit and the torque fluctuation calculation unit for determining a wear type according to the current fluctuation value and the torque fluctuation value. the type determination unit comprises: a normalization subunit for normalizing the current fluctuation value to form a normalized current fluctuation value and normalizing the torque fluctuation value to form a normalized torque fluctuation value; a consistency calculation subunit connected with the normalization subunit for calculating a correlation coefficient of the normalized current fluctuation value and the normalized torque fluctuation value to form a fluctuation consistency; 3. The intelligent detection system for an electric machine of claim 2, wherein, a type determination subunit connected with the consistency calculation subunit for determining that the type of the abnormality is bearing wear when the fluctuation consistency is less than a preset standard consistency to form a wear type. the risk determination module comprises: a rotating speed change rate calculation unit for calculating a change rate of the real-time rotating speed within a preset determination time length when the wear type is formed to form a rotating speed change rate. The acceleration change rate calculation unit is configured to calculate a change rate of the real-time vibration acceleration with a time interval of the preset determined duration when forming the wear type, and form an acceleration change rate. The risk determination unit is connected with the acceleration change rate calculation unit and configured to determine the serious risk level according to the rotation speed change rate and the acceleration change rate.

4. The intelligent detection system for electric machines according to claim 3, characterized in that, The risk determination unit includes: The change deviation calculation sub-unit is configured to calculate a relative deviation of the rotation speed change rate and the acceleration change rate, and form a change deviation. The deviation fluctuation calculation sub-unit is connected with the change deviation calculation sub-unit and configured to calculate a standard deviation of all the change deviations within a preset deviation fluctuation duration when the change deviation is greater than a preset change deviation threshold, and form a deviation fluctuation value. The risk determination sub-unit is connected with the deviation fluctuation calculation sub-unit and configured to determine the risk level of the abnormality as a serious risk level when the deviation fluctuation value is greater than a preset deviation fluctuation threshold.

5. The intelligent detection system for an electric machine of claim 4, wherein, The adjustment module includes: The timestamp recording unit is configured to record timestamps when the serious risk level is determined, and form a plurality of timestamps. The distribution degree calculation unit is connected with the timestamp recording unit and configured to calculate a level distribution degree according to the timestamps. The synchronization degree calculation unit is configured to calculate a change synchronization degree according to the real-time sound pressure level and the real-time vibration acceleration. The adjustment unit is connected with the distribution degree calculation unit and the synchronization degree calculation unit respectively, and configured to adjust the preset sound pressure level threshold according to the level distribution degree and the change synchronization degree, and form an adjusted sound pressure level threshold.

6. The intelligent detection system for an electric machine of claim 5, wherein, The distribution degree calculation unit includes: The duration calculation sub-unit is configured to calculate a time interval of any two adjacent timestamps when the number of the timestamps is greater than a preset interval number, and form a plurality of interval durations. The distribution degree calculation sub-unit is connected with the duration calculation sub-unit and configured to calculate a standard deviation of all the interval durations, and form a level distribution degree.

7. The intelligent detection system for an electric machine of claim 6, wherein, The synchronization degree calculation unit includes: The adjusted sound pressure fluctuation calculation sub-unit is configured to calculate a standard deviation of the real-time sound pressure level, and form an adjusted sound pressure fluctuation value. The adjusted vibration fluctuation calculation sub-unit is configured to calculate a standard deviation of the real-time vibration acceleration, and form an adjusted vibration fluctuation value. The fluctuation normalization sub-unit is connected with the adjusted sound pressure fluctuation calculation sub-unit and the adjusted vibration fluctuation calculation sub-unit respectively, and configured to normalize the adjusted sound pressure fluctuation value, and form a sound pressure normalized fluctuation value, and normalize the vibration fluctuation value, and form a vibration normalized fluctuation value. The synchronization degree calculation sub-unit is connected with the fluctuation normalization sub-unit and configured to calculate a relative deviation of the sound pressure normalized fluctuation value and the vibration normalized fluctuation value, and form a change synchronization degree.

8. The intelligent detection system for an electric machine of claim 7, wherein, The adjustment unit includes: The distribution comparison sub-unit is configured to compare the level distribution degree with a preset standard distribution degree, and form a distribution comparison result. The synchronization comparison sub-unit is connected with the distribution comparison sub-unit and configured to compare the change synchronization degree with a preset standard synchronization degree when the distribution comparison result is that the level distribution degree is greater than the preset standard distribution degree, and form a synchronization comparison result. An adjusting subunit, connected with the synchronous comparison subunit, configured to increase the preset sound pressure level threshold to form an adjusted sound pressure level threshold according to a preset adjusting coefficient and a relative deviation between the preset standard synchronization degree and the change synchronization degree when the synchronous comparison result is that the change synchronization degree is less than the preset standard synchronization degree.

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