Intelligent detection system for motor

Abstract

The invention relates to the technical field of sound wave detection, in particular to an intelligent detection system for a motor, which comprises a data acquisition module, an abnormality judgment module, a type judgment module, a risk determination module, an adjustment module and an alarm module. According to the invention, through combination of sound wave analysis and various real-time monitoring parameters, accurate detection of the operation state of the motor is realized, the system can acquire the sound pressure level, the vibration acceleration, the winding current, the bearing temperature, the rotating speed and the torque of the motor in real time, and rapidly determine abnormity based on a preset threshold value, and after abnormity is detected, the system is started. The system further combines current, temperature and torque information to analyze the abrasion type, and evaluates the serious risk level according to the rotating speed and the vibration acceleration, so that refined risk management is provided, and the problems of low fault judgment precision and slow response speed caused by data transmission delay and environmental interference are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor detection, and in particular to an intelligent detection system for a motor. Background Art

[0002] In modern manufacturing, the assembly and testing of motors are critical steps in ensuring product quality. After the motor is assembled, it must undergo rigorous performance testing to ensure that it meets design standards and can operate stably. However, traditional testing methods often rely on manual operations, which is time-consuming and easily affected by human factors, especially when detecting minor issues such as bearing wear. Bearing wear is one of the common faults that affect the normal operation of motors. Traditional testing methods often have difficulty in detecting subtle bearing wear in a timely manner, resulting in abnormal vibration, noise and other problems in the motor. In addition, the accuracy and efficiency of the testing equipment may become a bottleneck, affecting overall production efficiency. Therefore, how to improve the level of test automation, improve the accuracy of bearing wear detection, and reduce human errors has become a major challenge facing the production line.

[0003] Patent document with publication number CN111965259A discloses a fault detection and inspection system based on sound waves, which includes: a fault detection system and an inspection robot; wherein the fault detection system includes: a sound wave acquisition terminal, which is used to collect sound waves from motors in a motor group separately; the sound wave acquisition terminal includes a sound wave detection sensor and a wireless communication module, the sound wave detection sensor is used to obtain sound wave data generated by the vibration of the corresponding motor, one motor corresponds to multiple sound wave detection sensors, and the multiple sound wave detection sensors are at different positions on the motor, and the wireless transmission module is used to send the sound wave data of the corresponding motor; a cloud server, the cloud server can receive the sound wave data transmitted by the wireless communication module and determine whether the corresponding motor is in a fault state; wherein the inspection robot includes a control mechanism, the control mechanism includes a wireless communication module, and the wireless communication module is used to receive the fault information of the corresponding motor from the cloud server and move to the position of the faulty motor to place the positioning device.

[0004] It can be seen that the fault detection and inspection system based on sound waves has the following problems: since the system transmits sound wave data to the cloud server for judgment through the wireless communication module, there is a data transmission delay, which affects the real-time nature of fault judgment, especially in emergency situations that require a quick response, and the problem cannot be handled in time; the system relies on the cooperation of multiple sound wave detection sensors and wireless communication modules. If any of the devices fails, it will cause the failure of the entire detection system or performance degradation; the inspection robot needs to reach the location of the faulty motor according to the fault information sent by the cloud server. This process will be limited by the robot's moving 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] To this end, the present invention provides an intelligent detection system for motors, which is used to overcome the problems of low fault judgment accuracy and slow response speed caused by data transmission delays and environmental interference in the prior art by combining real-time acoustic wave data and operating condition data and dynamic threshold adjustment.

[0006] To achieve the above object, the present invention provides an intelligent detection system for a motor, comprising:

[0007] The data acquisition module is used to collect the 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 operation at the inspection station;

[0008] an abnormality determination module connected to the data acquisition module, configured to determine whether the motor has an abnormality based on the real-time sound pressure level and a preset sound pressure level threshold, and form an abnormality determination result;

[0009] a type determination module, connected to the data acquisition module and the abnormality determination module, respectively, for determining the wear type according to the abnormality determination result, the real-time winding current value, the real-time bearing temperature, and the real-time torque;

[0010] a risk determination module, connected to the data acquisition module and the type determination module respectively, for determining a serious risk level according to the wear type, the real-time rotational speed, and the real-time vibration acceleration;

[0011] an adjustment module, connected to the data acquisition module and the risk determination module, respectively, for adjusting 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 period after determining the severe risk level, to form an adjusted sound pressure level threshold;

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

[0013] Furthermore, the abnormality determination module includes:

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

[0015] The abnormality determination unit is connected to the sound pressure level comparison unit and is used to determine that the motor has an abnormality when the sound pressure level comparison result shows that the real-time sound pressure level is greater than the preset sound pressure level threshold, thereby forming an abnormality determination result.

[0016] Furthermore, the type determination module includes:

[0017] a current fluctuation calculation unit, configured to calculate a standard deviation of the real-time winding current value within a preset determination time period when the real-time bearing temperature is greater than a preset standard temperature, to form a current fluctuation value;

[0018] a torque fluctuation calculation unit, configured to calculate a standard deviation of the real-time torque within the preset determination time period when the real-time bearing temperature is greater than the preset standard temperature, 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 according to the current fluctuation value and the torque fluctuation value.

[0020] Furthermore, the type determination unit includes:

[0021] a normalizing subunit, configured to normalize the current fluctuation value to form a current normalized fluctuation value, and to normalize the torque fluctuation value to form a torque normalized fluctuation value;

[0022] a consistency calculation subunit, connected to the normalization subunit, for calculating a 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 that the type of the abnormality is bearing wear when the fluctuation consistency is less than a preset standard consistency, thereby forming a wear type.

[0024] Furthermore, the risk determination module includes:

[0025] a rotational speed change rate calculation unit, configured to calculate a change rate of the real-time rotational speed at a time interval of a predetermined time length when forming a wear type, to form a rotational speed change rate;

[0026] an acceleration change rate calculation unit, configured to calculate a change rate of the real-time vibration acceleration at a time interval equal to the preset determined time length when forming a wear type, to form an acceleration change rate;

[0027] A risk determination unit is connected to the acceleration fluctuation calculation unit and is used to determine the severe risk level according to the rotational speed change rate and the acceleration change rate.

[0028] Furthermore, the risk determination unit includes:

[0029] a change deviation calculation subunit, configured to calculate a relative deviation between the rotational speed change rate and the acceleration change rate to form a change deviation;

[0030] a deviation fluctuation calculation subunit, connected to the change deviation calculation subunit, for calculating the standard deviation of all change deviations within a preset deviation fluctuation time period to form a deviation fluctuation value when the change deviation is greater than a preset change deviation threshold;

[0031] The risk determination subunit is connected to the deviation fluctuation calculation subunit and is used to determine that the risk level of the abnormality is a serious 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, for recording a timestamp when the serious risk level is determined, to form a plurality of timestamps;

[0034] a distribution degree calculation unit connected to the timestamp recording unit and configured to calculate the level distribution degree according to the timestamp;

[0035] a synchronization degree calculation unit, configured to calculate a change synchronization degree according to 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] a duration calculation subunit, configured to calculate the time interval between any two adjacent timestamps to form a plurality of interval durations when the number of the timestamps is greater than a preset number of intervals;

[0039] The distribution degree calculation subunit is connected to the duration calculation subunit and is used to calculate the standard deviation of all the interval durations to form a level distribution degree.

[0040] Furthermore, the synchronization calculation unit includes:

[0041] an adjusted sound pressure fluctuation calculation subunit, configured to calculate a standard deviation of the real-time sound pressure level to form an adjusted sound pressure fluctuation value;

[0042] an adjusted vibration fluctuation calculation subunit, configured to calculate a standard deviation of the real-time vibration acceleration to form an adjusted vibration fluctuation value;

[0043] a fluctuation normalization subunit, connected to the adjusted sound pressure fluctuation calculation subunit and the adjusted vibration fluctuation calculation subunit, respectively, for normalizing the adjusted sound pressure fluctuation value to form a sound pressure normalized fluctuation value, and normalizing the vibration fluctuation value to form a vibration normalized fluctuation value;

[0044] The synchronization degree calculation subunit is connected to the fluctuation normalization subunit and is used to calculate the relative deviation between the normalized sound pressure fluctuation value and the normalized vibration fluctuation value to form the variation synchronization degree.

[0045] Furthermore, the adjustment unit includes:

[0046] a distribution comparison subunit, for comparing the grade distribution degree with a preset standard distribution degree to form a distribution comparison result;

[0047] a synchronization comparison subunit connected to the distribution comparison subunit, for comparing the change synchronization degree with the preset standard synchronization degree to form a synchronization comparison result when the distribution comparison result shows that the grade distribution degree is greater than the preset standard distribution degree;

[0048] An adjustment subunit is connected to the synchronization comparison subunit and is used to increase the preset sound pressure level threshold according to the relative deviation between the preset standard synchronization degree and the change synchronization degree and the preset adjustment coefficient to form an adjusted sound pressure level threshold when the synchronization comparison result shows that the change synchronization degree is less than the preset standard synchronization degree.

[0049] Compared with the existing technology, the beneficial effect of the present invention is that it realizes accurate detection of the motor 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 the abnormality based on the preset threshold. When the abnormality is detected, the system further analyzes the wear type based on the current, temperature and torque information, and evaluates the severity risk level based on the 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 according to 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 abnormality detection, and effectively solve the problems of low fault judgment accuracy and slow response speed due to data transmission delays and environmental interference.

[0050] Furthermore, rapid detection of motor anomalies based on real-time sound pressure level changes avoids reliance on complex historical data analysis and improves the immediacy of detection. Furthermore, the determination method based on preset sound pressure level thresholds ensures system efficiency and stability, facilitating early detection of motor anomalies, thereby reducing the risk of equipment damage and improving production line reliability and maintenance efficiency.

[0051] Furthermore, by accurately capturing the fluctuations in motor winding current and torque when the bearing temperature is abnormal, and combining data characteristics to determine the type of wear, targeted fault diagnosis can be achieved. Compared with single parameter monitoring, the accuracy and reliability of the judgment are improved, and abnormal bearing wear can be effectively identified. This 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 fluctuations, the impact of dimensional differences can be eliminated, making different data comparable. The consistency calculation ensures the synchronization of the two fluctuations. A low consistency indicates that wear has caused the current and torque fluctuations to become out of sync, thereby improving the detection accuracy of bearing wear faults. This can effectively enhance the system's ability to provide early warning of motor wear and prevent more serious failures caused by wear.

[0053] Furthermore, by monitoring and calculating the speed and vibration acceleration change rate, the operating risks of the motor can be effectively assessed, potential faults or abnormal conditions can be identified early, and production interruptions caused by equipment failure can be avoided. This improves the accuracy and timeliness of early warnings, ensures the safety and stability of motor operation, and reduces maintenance costs and downtime.

[0054] Furthermore, through multi-level deviation calculation and fluctuation analysis, the diagnostic accuracy of potential motor faults is improved. In particular, when judging motor abnormalities, it not only relies on single parameter changes, but also comprehensively considers the fluctuations of speed and vibration acceleration, which can effectively identify hidden faults that may be overlooked.

[0055] Furthermore, by comprehensively considering the time distribution, parameter synchronization and changes in risk levels, the function of dynamically adjusting the sound pressure level threshold is realized, ensuring that the risk assessment standard can be revised in time according to changes in actual working conditions. The adaptive adjustment mechanism improves the system's response ability 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 fluctuations in timestamp intervals, we can accurately assess the stability and regularity of a motor's risk level changes. High standard deviations indicate significant level fluctuations, signaling abnormal risk, while low standard deviations indicate stable level changes. This helps the system accurately determine risk trends and improves the accuracy and response speed of risk assessments.

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

[0058] Furthermore, by effectively adjusting the sound pressure level threshold according to 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 adjustment system can better adapt to the actual operating status of the motor, helping to detect potential faults in advance, avoid accidents, and ensure the smooth operation of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Schematic diagram of the intelligent detection system for a motor according to this embodiment;

[0060] Figure 2 This is a logic diagram for determining the presence of an abnormality by the abnormality determination unit of this embodiment;

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

[0062] Figure 4 This is a decision logic diagram for the risk determination subunit of this embodiment to determine the severity risk level. DETAILED DESCRIPTION

[0063] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0064] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0065] See also Figure 1 As shown, it is a schematic diagram of the intelligent detection system for motors according to this embodiment;

[0066] This embodiment provides an intelligent detection system for a motor, comprising:

[0067] The data acquisition module is used to collect the 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 operation at the inspection station;

[0068] an abnormality determination module connected to the data acquisition module, configured to determine whether the motor has an abnormality based on the real-time sound pressure level and a preset sound pressure level threshold, and form an abnormality determination result;

[0069] a type determination module, connected to the data acquisition module and the abnormality determination module, respectively, for determining the wear type according to the abnormality determination result, the real-time winding current value, the real-time bearing temperature, and the real-time torque;

[0070] a risk determination module, connected to the data acquisition module and the type determination module respectively, for determining a serious risk level according to the wear type, the real-time rotational speed, and the real-time vibration acceleration;

[0071] an adjustment module, connected to the data acquisition module and the risk determination module, respectively, for adjusting 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 period after determining the severe risk level, to form an adjusted sound pressure level threshold;

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

[0073] The data acquisition module uses multiple high-precision sensors installed at the inspection station to monitor the motor's operating status in real time. Sound pressure levels are collected by a distributed microphone array, with digital signal processing (DSP) filtering to ensure measurement accuracy. Vibration acceleration is detected by a three-axis accelerometer, with signal amplification and filtering circuits optimizing data quality. Winding current is measured by a Hall effect current sensor, with analog-to-digital conversion (ADC) converting the signal into a digital form. Speed ​​is acquired by a photoelectric encoder, with frequency analysis used to accurately determine speed. Bearing temperature is detected by an infrared temperature sensor, with data filtering algorithms used to eliminate temperature fluctuations. Torque is measured by a dynamic torque sensor, with signal amplification and filtering optimizing data stability.

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

[0075] The preset adjustment time is the time window used by the system to dynamically adjust the sound pressure level threshold after detecting an anomaly. It depends on the motor operating status, the speed of fault development, and the rhythm of the production line. 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 judgment, and reduce interference from environmental noise.

[0076] Bearing wear refers to structural damage or performance degradation caused by friction, fatigue, insufficient lubrication, or impurity intrusion during motor operation. In severe cases, bearings may spall, pit, or seize, 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 during operation, including sound pressure level, vibration acceleration, winding current, speed, bearing temperature, and torque. The abnormality determination module determines whether the motor is abnormal based on the sound pressure level threshold and generates an abnormality determination result. The type determination module analyzes the wear type based on current, temperature, and torque information. The risk determination module further assesses 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 issues an alarm prompt for high-risk situations based on the adjusted threshold so that maintenance measures can be taken in a timely manner.

[0078] By combining acoustic wave analysis with a variety of real-time monitoring parameters, accurate detection of the motor's operating status can be achieved. 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 based on current, temperature and torque information, and assesses the severity level based on speed and vibration acceleration, thereby providing refined risk management. The system also has an adaptive adjustment function that can dynamically optimize the sound pressure level threshold based on the severity 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 due to data transmission delays and environmental interference.

[0079] Please continue reading Figure 2 As shown, it is a determination logic diagram of the abnormality determination unit of this embodiment for determining the presence of an abnormality;

[0080] The abnormality determination module includes:

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

[0082] The abnormality determination unit is connected to the sound pressure level comparison unit and is used to determine that the motor has an abnormality when the sound pressure level comparison result shows that the real-time sound pressure level is greater than the preset sound pressure level threshold, thereby forming an abnormality determination result.

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

[0084] Rapidly detecting motor anomalies based on real-time sound pressure level changes avoids reliance on complex historical data analysis and improves the immediacy of detection. Furthermore, the determination method based on preset sound pressure level thresholds ensures system efficiency and stability, helping to detect motor anomalies 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] a current fluctuation calculation unit, configured to calculate a standard deviation of the real-time winding current value within a preset determination time period when the real-time bearing temperature is greater than a preset standard temperature, to form a current fluctuation value;

[0087] a torque fluctuation calculation unit, configured to calculate a standard deviation of the real-time torque within the preset determination time period when the real-time bearing temperature is greater than the preset standard temperature, 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 according to the current fluctuation value and the torque fluctuation value.

[0089] The preset standard temperature is the maximum safe temperature that the motor bearings should maintain during normal operation. It is set according to the motor type and operating environment, 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 bearings are within the normal operating temperature range and avoid premature wear or failure of the bearings due to excessive temperature.

[0090] The preset judgment time 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. It is usually set to 10 seconds to 30 seconds. In this embodiment, it is set to 20 seconds. It 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 checks whether the real-time bearing temperature exceeds a preset standard temperature. If the temperature rises abnormally, the current fluctuation calculation unit calculates the standard deviation of the winding current values ​​within the preset determination time 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. The type determination unit then comprehensively analyzes the motor's operating status based on the characteristics of the current fluctuation values ​​and torque fluctuation values ​​to accurately determine the wear type, providing a basis for subsequent risk assessment and maintenance decisions.

[0092] By accurately capturing the fluctuations in motor winding current and torque when the bearing temperature is abnormal, and combining data features 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 judgment, can effectively identify abnormal bearing wear, provide data support for predictive maintenance, reduce unplanned equipment downtime, and improve the stability and operating efficiency of the production line.

[0093] Please continue reading Figure 3 As shown, it is a determination logic diagram of the wear type determined by the type determination subunit of this embodiment;

[0094] The type determination unit includes:

[0095] a normalizing subunit, configured to normalize the current fluctuation value to form a current normalized fluctuation value, and to normalize the torque fluctuation value to form a torque normalized fluctuation value;

[0096] a consistency calculation subunit, connected to the normalization subunit, for calculating a 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 that the type of the abnormality is bearing wear when the fluctuation consistency is less than a preset standard consistency, thereby forming a wear type.

[0098] The preset standard consistency is a threshold used to determine the correlation between the current fluctuation value and the 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 situations can be distinguished. In this embodiment, it is set to 0.8, which helps to ensure high sensitivity while avoiding excessive false alarms due to small fluctuations, thereby improving the stability and reliability of the system.

[0099] First, the normalization subunit normalizes the current and torque fluctuation values ​​to form normalized current and torque fluctuation values. The consistency calculation subunit then calculates the correlation coefficient between these two normalized fluctuation values ​​to determine the fluctuation consistency. Finally, the type determination subunit compares the calculated fluctuation consistency with a preset standard consistency. If the consistency falls below the standard, the motor abnormality is determined to be a bearing wear type.

[0100] Normalizing current and torque fluctuations eliminates the impact of dimensional differences, making different data comparable. Consistency calculation ensures the synchronization of these fluctuations. A low degree of consistency indicates that wear has caused current and torque fluctuations to become out of sync, thereby improving the accuracy of bearing wear fault detection. This can effectively enhance the system's ability to provide early warning of motor wear and prevent more serious failures caused by wear.

[0101] Specifically, the risk determination module includes:

[0102] a rotational speed change rate calculation unit, configured to calculate a change rate of the real-time rotational speed at a time interval of a predetermined time length when forming a wear type, to form a rotational speed change rate;

[0103] an acceleration change rate calculation unit, configured to calculate a change rate of the real-time vibration acceleration at a time interval equal to the preset determined time length when forming a wear type, to form an acceleration change rate;

[0104] A risk determination unit is connected to the acceleration fluctuation calculation unit and is used to determine the severe risk level according to the rotational speed change rate and the acceleration change rate.

[0105] The preset determination time length refers to the time interval used to calculate the speed change rate and acceleration change rate. Its setting is based on the operating characteristics of the motor and fault prediction requirements, and 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 the short-term fluctuations of the motor without missing important risk signals due to too short or too long time.

[0106] The risk determination module uses the speed change rate calculation unit and the acceleration change rate calculation unit to calculate the rate of change of the real-time speed and vibration acceleration, respectively, over a preset time period. These rates of change reflect any instability or abnormal fluctuations in the motor's operation, helping to assess the risk level of the equipment. Based on the calculated speed change rate and acceleration change rate, the risk determination unit further determines whether the motor is at a critical risk level.

[0107] By monitoring and calculating the speed and vibration acceleration change rate, the operating risk of the motor can be effectively assessed, potential faults or abnormal conditions can be identified early, and production interruptions caused by equipment failure can be avoided. This improves the accuracy and timeliness of early warnings, ensures the safety and stability of motor operation, and reduces maintenance costs and downtime.

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

[0109] The risk determination unit includes:

[0110] a change deviation calculation subunit, configured to calculate a relative deviation between the rotational speed change rate and the acceleration change rate to form a change deviation;

[0111] a deviation fluctuation calculation subunit, connected to the change deviation calculation subunit, for calculating the standard deviation of all change deviations within a preset deviation fluctuation time period to form a deviation fluctuation value when the change deviation is greater than a preset change deviation threshold;

[0112] The risk determination subunit is connected to the deviation fluctuation calculation subunit and is used to determine that the risk level of the abnormality is a serious risk level when the deviation fluctuation value is greater than a preset deviation fluctuation threshold.

[0113] The preset change deviation threshold is a standard used to determine whether the relative deviation of the speed change rate and the acceleration change rate exceeds the normal range. It depends on the operating characteristics of the motor, 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 to ensure that key operating deviations are captured, prevent abnormalities from being determined too early or too late, and help to detect motor problems in a timely manner and reduce false alarms.

[0114] The preset deviation fluctuation time 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. It is usually set between 1 minute and 5 minutes. In this embodiment, it is set to 3 minutes. It can balance the capture of sufficient fluctuation data while avoiding misjudgment caused by fluctuations of too short a time, thereby ensuring the stability and accuracy of risk judgment.

[0115] The preset deviation fluctuation threshold is a standard for judging 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. It can reasonably avoid false alarms caused by environmental or instantaneous fluctuations, and effectively identify potential risk fluctuations, thereby making accurate risk assessments and early warnings.

[0116] The risk determination unit assesses the stability of the motor's operation by calculating the relative deviation between the speed change rate and the vibration acceleration change rate. If the deviation exceeds a preset threshold, the system further calculates the standard deviation of all deviations within a preset time period, known as the deviation fluctuation value. If the deviation fluctuation value exceeds the preset threshold, the system ultimately determines the motor's abnormality risk level as severe. This series of calculations and judgments accurately assesses the motor's risk level.

[0117] Through multi-level deviation calculation and fluctuation analysis, the diagnostic accuracy of potential motor faults is improved. In particular, when judging motor abnormalities, it not only relies on single parameter changes, but also comprehensively considers the fluctuations 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, for recording a timestamp when the serious risk level is determined, to form a plurality of timestamps;

[0120] a distribution degree calculation unit connected to the timestamp recording unit and configured to calculate the level distribution degree according to the timestamp;

[0121] a synchronization degree calculation unit, configured to calculate a change synchronization degree according to 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 severity risk level is determined, generating multiple timestamp data. The distribution calculation unit calculates the level distribution based on these timestamps to reflect the changing pattern of the risk level. The synchronization calculation unit calculates the synchronization of changes based on the real-time changes in sound pressure level and vibration acceleration, reflecting the consistency of changes in these two parameters. Finally, the adjustment unit combines the level distribution and synchronization to adjust the preset sound pressure level threshold, thereby obtaining a new adjusted sound pressure level threshold for more accurate motor risk assessment.

[0124] By comprehensively considering changes in time distribution, parameter synchronization, and risk levels, the function of dynamically adjusting the sound pressure level threshold is realized, ensuring that the risk assessment standard can be revised in a timely manner according to changes in actual operating conditions. The adaptive adjustment mechanism improves the system's response capability to sudden risks, reduces the false alarm rate, and improves the accuracy of motor fault prediction and the overall reliability of the system.

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

[0126] a duration calculation subunit, configured to calculate the time interval between any two adjacent timestamps to form a plurality of interval durations when the number of the timestamps is greater than a preset number of intervals;

[0127] The distribution degree calculation subunit is connected to the duration calculation subunit and is used to calculate the standard deviation of all the interval durations to form a level distribution degree.

[0128] The preset number of intervals refers to the minimum number of timestamp intervals used to determine whether to calculate the timestamp interval in the distribution 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 a preset number of intervals. It then calculates the time interval between any two adjacent timestamps, generating multiple interval duration data. Next, the distribution calculation subunit processes all of this interval duration data and calculates its standard deviation to derive a graded distribution, reflecting the degree of fluctuation in the time intervals.

[0130] By analyzing fluctuations in timestamp intervals, we can accurately assess the stability and regularity of a motor's risk level changes. High standard deviations indicate significant level fluctuations, signaling abnormal risk, while low standard deviations indicate stable level changes. This helps the system accurately determine risk trends and improves the accuracy and response speed of risk assessments.

[0131] Specifically, the synchronization calculation unit includes:

[0132] an adjusted sound pressure fluctuation calculation subunit, configured to calculate a standard deviation of the real-time sound pressure level to form an adjusted sound pressure fluctuation value;

[0133] an adjusted vibration fluctuation calculation subunit, configured to calculate a standard deviation of the real-time vibration acceleration to form an adjusted vibration fluctuation value;

[0134] a fluctuation normalization subunit, connected to the adjusted sound pressure fluctuation calculation subunit and the adjusted vibration fluctuation calculation subunit, respectively, for normalizing the adjusted sound pressure fluctuation value to form a sound pressure normalized fluctuation value, and normalizing the vibration fluctuation value to form a vibration normalized fluctuation value;

[0135] The synchronization degree calculation subunit is connected to the fluctuation normalization subunit and is used to calculate the relative deviation between the normalized sound pressure fluctuation value and the normalized vibration fluctuation value to form the variation synchronization degree.

[0136] First, the Adjusted Sound Pressure Fluctuation Calculation subunit calculates the standard deviation of the real-time sound pressure level to obtain the Adjusted Sound Pressure Fluctuation Value. Then, the Adjusted Vibration Fluctuation Calculation subunit calculates the standard deviation of the real-time vibration acceleration 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 Synchronicity Calculation subunit calculates the relative deviation between these two normalized fluctuation values ​​to obtain the degree of synchronization.

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

[0138] Specifically, the adjustment unit includes:

[0139] a distribution comparison subunit, for comparing the grade distribution degree with a preset standard distribution degree to form a distribution comparison result;

[0140] a synchronization comparison subunit connected to the distribution comparison subunit, for comparing the change synchronization degree with the preset standard synchronization degree to form a synchronization comparison result when the distribution comparison result shows that the grade distribution degree is greater than the preset standard distribution degree;

[0141] an adjustment subunit connected to the synchronization comparison subunit and configured to, when the synchronization comparison result indicates that the change synchronization degree is less than the preset standard synchronization degree, increase the preset sound pressure level threshold according to the relative deviation between the preset standard synchronization degree and the change synchronization degree and a preset adjustment coefficient to form an adjusted sound pressure level threshold, wherein the relative deviation between the preset standard synchronization degree and the change 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 comparative calculations, which is usually set based on the historical data or expected performance of the equipment operation. 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 is a fixed reference value used when comparing the changing synchronization. It reflects the synchronization between the real-time sound pressure level and the 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 occur during equipment operation.

[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 actual requirements while improving the system's response speed to abnormalities.

[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 according to the relative deviation between the standard synchronization degree and the change synchronization degree and the preset adjustment coefficient to form an 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 adjustment system can better adapt to the actual operating status of the motor, helping to detect potential faults in advance, avoid accidents, and ensure the smooth operation of the production line.

[0147] Thus far, the technical solutions of the present invention have been described in conjunction with 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 may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. An intelligent detection system for a motor, characterized in that: include: The data acquisition module is used to collect the 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 operation at the inspection station; an abnormality determination module connected to the data acquisition module, configured to determine whether the motor has an abnormality based on the real-time sound pressure level and a preset sound pressure level threshold, and form an abnormality determination result; a type determination module, connected to the data acquisition module and the abnormality determination module, respectively, for determining the 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 to the data acquisition module and the type determination module respectively, for determining a serious risk level according to the wear type, the real-time rotational speed, and the real-time vibration acceleration; an adjustment module, connected to the data acquisition module and the risk determination module, respectively, for adjusting 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 period after determining the severe risk level, to form an adjusted sound pressure level threshold; An alarm module is connected to the risk determination module and is configured to issue an alarm for the severe risk level determined based on the adjusted sound pressure level threshold.

2. The intelligent detection system for a motor according to claim 1, characterized in that: The abnormality determination module includes: a sound pressure level comparison unit, configured to compare the real-time sound pressure level with the preset sound pressure level threshold to form a sound pressure level comparison result; The abnormality determination unit is connected to the sound pressure level comparison unit and is used to determine that the motor has an abnormality when the sound pressure level comparison result shows that the real-time sound pressure level is greater than the preset sound pressure level threshold, thereby forming an abnormality determination result.

3. The intelligent detection system for a motor according to claim 2, characterized in that: The type determination module includes: a current fluctuation calculation unit, configured to calculate a standard deviation of the real-time winding current value within a preset determination time period when the real-time bearing temperature is greater than a preset standard temperature, to form a current fluctuation value; a torque fluctuation calculation unit, configured to calculate a standard deviation of the real-time torque within the preset determination time period when the real-time bearing temperature is greater than the preset standard temperature, to form a torque fluctuation value; 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 according to the current fluctuation value and the torque fluctuation value.

4. The intelligent detection system for a motor according to claim 3, characterized in that: The type determination unit includes: a normalizing subunit, configured to normalize the current fluctuation value to form a current normalized fluctuation value, and to normalize the torque fluctuation value to form a torque normalized fluctuation value; a consistency calculation subunit, connected to the normalization subunit, for calculating a correlation coefficient between the normalized current fluctuation value and the normalized torque fluctuation value to form a fluctuation consistency; The type determination subunit is connected to the consistency calculation subunit and is used to determine that the type of the abnormality is bearing wear when the fluctuation consistency is less than a preset standard consistency, thereby forming a wear type.

5. The intelligent detection system for a motor according to claim 4, characterized in that: The risk determination module includes: a rotational speed change rate calculation unit, configured to calculate a change rate of the real-time rotational speed at a time interval of a predetermined time length when forming a wear type, to form a rotational speed change rate; an acceleration change rate calculation unit, configured to calculate a change rate of the real-time vibration acceleration at a time interval equal to the preset determined time length when forming a wear type, to form an acceleration change rate; A risk determination unit is connected to the acceleration fluctuation calculation unit and is used to determine the severe risk level according to the rotational speed change rate and the acceleration change rate.

6. The intelligent detection system for a motor according to claim 5, characterized in that: The risk determination unit includes: a change deviation calculation subunit, configured to calculate a relative deviation between the rotational speed change rate and the acceleration change rate to form a change deviation; a deviation fluctuation calculation subunit, connected to the change deviation calculation subunit, for calculating the standard deviation of all change deviations within a preset deviation fluctuation time period to form a deviation fluctuation value when the change deviation is greater than a preset change deviation threshold; The risk determination subunit is connected to the deviation fluctuation calculation subunit and is used to determine that the risk level of the abnormality is a serious risk level when the deviation fluctuation value is greater than a preset deviation fluctuation threshold.

7. The intelligent detection system for a motor according to claim 6, characterized in that: The adjustment module includes: a timestamp recording unit, for recording a timestamp when the serious risk level is determined, to form a plurality of timestamps; a distribution degree calculation unit connected to the timestamp recording unit and configured to calculate the level distribution degree according to the timestamp; a synchronization degree calculation unit, configured to calculate a change synchronization degree according to the real-time sound pressure level and the real-time vibration acceleration; 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.

8. The intelligent detection system for a motor according to claim 7, characterized in that: The distribution degree calculation unit includes: a duration calculation subunit, configured to calculate the time interval between any two adjacent timestamps to form a plurality of interval durations when the number of the timestamps is greater than a preset number of intervals; The distribution degree calculation subunit is connected to the duration calculation subunit and is used to calculate the standard deviation of all the interval durations to form a level distribution degree.

9. The intelligent detection system for a motor according to claim 8, characterized in that: The synchronization calculation unit includes: an adjusted sound pressure fluctuation calculation subunit, configured to calculate a standard deviation of the real-time sound pressure level to form an adjusted sound pressure fluctuation value; an adjusted vibration fluctuation calculation subunit, configured to calculate a standard deviation of the real-time vibration acceleration to form an adjusted vibration fluctuation value; a fluctuation normalization subunit, connected to the adjusted sound pressure fluctuation calculation subunit and the adjusted vibration fluctuation calculation subunit, respectively, for normalizing the adjusted sound pressure fluctuation value to form a sound pressure normalized fluctuation value, and normalizing the vibration fluctuation value to form a vibration normalized fluctuation value; The synchronization degree calculation subunit is connected to the fluctuation normalization subunit and is used to calculate the relative deviation between the normalized sound pressure fluctuation value and the normalized vibration fluctuation value to form the variation synchronization degree.

10. The intelligent detection system for a motor according to claim 9, characterized in that: The adjustment unit includes: a distribution comparison subunit, for comparing the grade distribution degree with a preset standard distribution degree to form a distribution comparison result; a synchronization comparison subunit connected to the distribution comparison subunit, for comparing the change synchronization degree with the preset standard synchronization degree to form a synchronization comparison result when the distribution comparison result shows that the grade distribution degree is greater than the preset standard distribution degree; An adjustment subunit is connected to the synchronization comparison subunit and is used to increase the preset sound pressure level threshold according to the relative deviation between the preset standard synchronization degree and the change synchronization degree and the preset adjustment coefficient to form an adjusted sound pressure level threshold when the synchronization comparison result shows that the change synchronization degree is less than the preset standard synchronization degree.

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