Method for monitoring failure of bottom loading and unloading swivel and early warning system

Abstract

The application relates to the field of electrical measurement and testing, in particular to a bottom loading and unloading crane fault monitoring method and early warning system, which comprises the following steps: acquiring and preprocessing current, voltage and vibration sequences of a servo motor to obtain corresponding IMF components and energy spectrum; in combination with historical normal operation data and to-be-monitored data, the importance of the IMF components is evaluated, and the time domain abnormality degree is obtained based on the standard deviation of the sliding window variance and the DTW distance; odd harmonic information is extracted from the energy spectrum, and the comprehensive abnormality degree is obtained in combination with the time domain abnormality degree; the fault degree is obtained through weighted integration and input into a control system to judge the fault. The application comprehensively evaluates the servo motor operation state by comprehensively analyzing the energy spectrum difference of the IMF components, the first-order difference sequence difference and the time-frequency domain abnormality degree, avoids the loss of key fault information caused by the elimination of high-noise components in the traditional method and the limitation of single time domain feature monitoring, and significantly improves the accuracy and sensitivity of fault monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electrical measurement and testing. In particular, it relates to a method for monitoring and early warning system for bottom loading and unloading crane. BACKGROUND

[0002] Bottom loading and unloading crane is a device for loading and unloading oil and gas, liquefied natural gas (LNG), chemicals and other media, widely used in wharf, tank station and railway loading and unloading site. This device drives the crane through servo motor for precise positioning and control of material flow, to ensure the safety and efficiency of the loading and unloading process. The running state of the servo motor is directly related to the accuracy and safety of the loading and unloading, once a fault occurs, it may cause serious consequences.

[0003] The normal operation of the servo motor is essential for the safe and efficient operation of the bottom loading and unloading crane. The failure of the servo motor, such as winding short circuit, bearing damage, encoder failure, etc., may cause material leakage, equipment downtime and even explosion and other serious consequences. Therefore, effective fault monitoring of the servo motor, timely detection and handling of potential faults, is of great significance to ensure the safe operation of the entire loading and unloading system.

[0004] In the existing servo motor fault monitoring technology, signal decomposition method is usually used to eliminate components with high noise content. However, this method may cause key fault information to be eliminated or ignored. In addition, relying on single time domain feature for fault monitoring cannot effectively monitor the slight abnormalities when the fault occurs. This may cause fault monitoring not timely or misjudgment, and thus cause material leakage, equipment downtime and even explosion and other serious consequences. SUMMARY

[0005] In order to solve the problem that the existing servo motor fault monitoring technology often eliminates high noise components through signal decomposition, which may cause key fault information to be lost, and single time domain feature monitoring cannot capture slight abnormalities, which may cause monitoring not timely or misjudgment, the present application provides solutions in the following aspects.

[0006] In a first aspect, a failure monitoring method of a bottom loading and unloading swivel includes: obtaining a running feature sequence of a servo motor and preprocessing to obtain corresponding IMF components and energy spectrum; wherein the running feature sequence includes: a current sequence, a voltage sequence, and a vibration sequence; for a plurality of historical normal running processes and a to-be-monitored running process, according to the energy difference in the energy spectrum of each IMF component sequence and the feature sequence and the difference of the first-order difference sequence, the importance of the IMF components of each running feature is evaluated, the time domain abnormality degree is obtained based on the standard deviation of the sliding window variance and the DTW distance; the frequency and energy information of the odd harmonics are extracted from the energy spectrum, combined with the time domain abnormality degree, according to the frequency energy change between the odd harmonics and the energy change of the odd harmonics, the comprehensive abnormality degree is obtained, the importance of the IMF components and the comprehensive abnormality degree are weighted and integrated based on the importance of the IMF components and the comprehensive abnormality degree, to obtain the failure degree of the servo motor, the failure degree is input to the control system of the servo motor, and it is judged whether there is a failure, and the servo motor failure monitoring is completed.

[0007] By comprehensively analyzing the IMF components and energy spectrum of the current, voltage and vibration and other running feature sequences of the servo motor, the importance of each IMF component and the time domain and frequency domain abnormality degree can be accurately evaluated, and then the failure degree of the servo motor can be accurately determined and timely warning is given. The problem of loss of key fault information caused by direct elimination of high noise components in the traditional technology is effectively avoided, and the limitation of relying on only a single time domain feature for fault monitoring is overcome, the accuracy, sensitivity and reliability of fault monitoring are significantly improved, and a strong guarantee is provided for the safe operation of the servo motor.

[0008] Preferably, the calculation method of the importance of the IMF components of each running feature includes:

[0009] Taking any running feature as a target feature, the ratio between the average value of all frequency energies in the energy spectrum of the IMF components in the target feature and the average value of all energies in the energy spectrum of the feature sequence of the target feature is calculated to obtain an energy ratio value;

[0010] The data difference and waveform change rate difference of the IMF components and the target feature at each time point are calculated and multiplied and summed to obtain the similarity degree of the corresponding sequence of the target feature, the similarity degree is exponentially mapped using a negative exponential function, and the product of the mapping result and the energy ratio value is taken as the importance of each IMF component of the target feature.

[0011] Preferably, the time domain abnormality degree includes:

[0012] The reciprocal of the frequency with the maximum average energy value in all feature sequence energy spectrum is selected as the length of the sliding window; each IMF component is divided by the sliding window, the variance of the sequence in each window is calculated, the standard deviation based on the sequence variance of all windows in the IMF component is calculated, and the difference between the standard deviation in the historical normal operation process is summed up; the product of the difference sum, the standard deviation and the dynamic time warping distance is taken as the time domain abnormality degree of each IMF component.

[0013] Preferably, the calculation method of the comprehensive abnormality degree comprises:

[0014] The energy change of the odd harmonic of each IMF component and the distribution difference of the frequency energy between the odd harmonics are calculated, and the distribution difference is smoothed by using an exponential function to obtain a frequency domain abnormality degree; and the sum of the frequency domain abnormality degree and the time domain abnormality degree is taken as the comprehensive abnormality degree.

[0015] Preferably, the calculation method of the fault degree comprises:

[0016] The product of the importance degree and the comprehensive abnormality degree of each IMF component in each feature sequence is calculated respectively, the product results of all IMF components of all feature sequences are added up, and the sum is divided by the product of the number of feature sequences and the number of IMF components to obtain the fault degree of the servo motor.

[0017] Preferably, the step of judging whether there is a fault comprises:

[0018] In response to the fault degree being less than or equal to a preset fault threshold, the servo motor is in a normal operation state; otherwise, the servo motor is in a fault state, and a fault warning is triggered.

[0019] Preferably, the preprocessing step comprises:

[0020] The wavelet transform is used on the operation feature sequence to remove high-frequency noise, the denoised data is normalized, the normalized data is segmented into a plurality of time sequence segments, the empirical mode decomposition algorithm is applied to the time sequence segments, and the time sequence segments are decomposed into a plurality of IMF components; the Fourier transform is performed on each IMF component to convert from the time domain to the frequency domain, and the corresponding frequency spectrum is obtained; and the energy spectrum of each IMF component is calculated to identify the main frequency component and the energy change thereof in the signal.

[0021] In the second aspect, a fault warning system of a bottom loading and unloading crane pipe comprises a processor and a memory, and the memory stores computer program instructions; when the computer program instructions are executed by the processor, the fault monitoring method of the bottom loading and unloading crane pipe is realized.

[0022] The present application has the following effects:

[0023] 1、The application can more comprehensively evaluate the running state of the servo motor by comprehensively considering the energy spectrum difference of the IMF component, the first-order difference sequence difference, the time domain abnormality degree and the frequency domain abnormality degree. The loss of key fault information caused by directly removing high-noise components in the traditional method is avoided, and the limitation of relying on only a single time domain feature for fault monitoring is also overcome; through the combination of time domain and frequency domain multi-feature analysis, the fault signal can be more accurately identified, reducing misjudgment and missed detection, thereby significantly improving the accuracy of fault monitoring.

[0024] 2、The application can capture the slight abnormality when the fault occurs by analyzing the energy change of the odd harmonic and the frequency energy distribution difference between the odd harmonics. It is particularly suitable for servo motors of bottom loading and unloading cranes with high start-stop frequency and high noise interference. Through the combination of the standard deviation of the sliding window variance and the DTW distance, periodic waveform distortion in the time domain can be effectively detected, even in the case of high noise interference, the fault signal can be found in time, thereby improving the sensitivity of fault monitoring and ensuring that the fault can be detected in the early stage to avoid further deterioration of the fault. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 is a method flowchart of steps S1-S3 in the fault monitoring method of the bottom loading and unloading crane according to the embodiment of the application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, not all the embodiments of the application.

[0027] Referring to Figure 1 The fault monitoring method of the bottom loading and unloading crane includes steps S1-S3, and specifically as follows:

[0028] S1: Obtain the running feature sequence of the servo motor and pre-process it to obtain the corresponding IMF component and energy spectrum; wherein the running feature sequence includes: current sequence, voltage sequence and vibration sequence.

[0029] It should be noted that in the fault monitoring of the servo motor of the bottom loading and unloading crane, the servo motor may have multiple faults, including but not limited to winding short circuit, bearing damage, encoder failure, etc. When a fault occurs, abnormal characteristics will usually appear in the current, voltage and vibration signals.

[0030] Specifically, the historical normal operation process of multiple servo motors and the data acquisition of a servo motor to be monitored. The current sequence of the servo motor is collected through the current sensor arranged at the input end of the motor power supply; the voltage sequence of the servo motor is collected through the voltage sensor arranged at the output end of the frequency converter; and the vibration sequence of the servo motor is collected through the accelerometer arranged at the bearing shell of the motor.

[0031] The acquired current, voltage and vibration sequence data are subjected to wavelet transform to remove high-frequency noise, the denoised data are subjected to normalization processing, the normalized data are segmented into a plurality of time sequence segments, the time sequence segments are subjected to empirical mode decomposition algorithm to be decomposed into a plurality of IMF (Intrinsic Mode Functions) components, each IMF component is subjected to Fourier transform to convert from time domain to frequency domain to acquire the corresponding energy spectrum, the energy distribution in the frequency domain, i.e. the energy spectrum, can be obtained by Fourier transform of the operation characteristic sequence and the components, so as to identify the main frequency components and the energy change in the signal. These IMF components have the same length as the original sequence and can effectively reflect the different frequency components and characteristic information in the original signal, thereby providing a basis for subsequent fault feature extraction and analysis.

[0032] Specifically, taking the current sequence as an example, a current sequence can obtain a plurality of IMF components after EMD (Empirical Mode Decomposition). The original current sequence is a time sequence representing the current values measured at different times, and each IMF component is also a time sequence, and the length of each IMF component is the same as that of the original current sequence.

[0033] S2: According to the energy difference in the energy spectrum of each IMF component sequence and the characteristic sequence and the difference of the first-order difference sequence, the importance of each operation characteristic IMF component is evaluated, and the time domain abnormality degree is acquired based on the standard deviation of the sliding window variance and the DTW distance.

[0034] In the energy spectrum, an arbitrary one is selected as a target characteristic for subsequent fault monitoring analysis. The characteristic sequence and the energy spectrum of the IMF component of the servo motor under different operating states are analyzed, thereby providing basic data for fault monitoring.

[0035] In the fault monitoring of the servo motor of the bottom loading and unloading crane, due to the complex coordinated motion of the equipment, the generation of the fault signal is often accompanied by a large amount of noise. The traditional fault monitoring method usually directly eliminates the high frequency components containing noise, which may ignore the fault information contained in the high frequency components, thereby causing the fault monitoring to be not timely. On the contrary, if these components are not eliminated, their noise content may be mistaken for a fault signal, thereby causing fault misjudgment. In view of this, it is crucial to analyze the importance of each IMF component for improving the accuracy and timeliness of fault monitoring. The specific steps are as follows:

[0036] The steps for obtaining the importance of each IMF component of the target feature include:

[0037] First, select any running feature as the target feature. Then, calculate the average of all frequency energies in the energy spectrum of each IMF component in the target feature, and compare it with the average of all energies in the energy spectrum of the feature sequence of the target feature, so as to obtain the energy ratio. This ratio reflects the importance of the IMF component relative to the original feature sequence at the energy level. Subsequently, the data difference and the waveform change rate difference between each IMF component and the target feature at each time point are calculated, and the sum of the product of the two is obtained, that is, the similarity of the corresponding sequence of the target feature. The smaller the similarity, the more similar the IMF component and the target feature in numerical value and waveform change, and the more abundant the fault information they contain. The mapping result is multiplied by the energy ratio using a negative exponential function, and finally the importance of each IMF component of the target feature is obtained. The energy contribution and signal similarity are considered comprehensively, which can more accurately evaluate the importance of each IMF component in fault monitoring.

[0038] Specifically, the importance satisfies the following relationship:

[0039] ;

[0040] In the formula, denotes the importance of the i th IMF component of the target feature in the running process to be detected, denotes the average of all frequency energies in the energy spectrum of the i th IMF component, denotes the average of all frequency energies in the energy spectrum of the feature sequence of the target feature, denotes the length of the feature sequence, denotes the i th element of the i th IMF component, denotes the i th element of the feature sequence, denotes the i th element of the feature sequence, denotes the i th element of the feature sequence, denotes the i th element of the feature sequence, denotes the i th element of the feature sequence, denotes the i th element of the feature sequence, denotes the i th element of the feature sequence, the first element of the first-order difference sequence of the IMF component, the first element of the first-order difference sequence of the IMF component, the first element of the first-order difference sequence of the characteristic sequence, the first element of the first-order difference sequence of the characteristic sequence, represents an exponential function with base natural number

[0041] That is, measures the numerical difference between the IMF component and the characteristic sequence of the target feature at the first element, measures the waveform change rate difference between the IMF component and the characteristic sequence of the target feature at the first element.Both the amplitude information and the dynamic change information of the signal are considered. The amplitude directly reflects the strength of the signal. In fault monitoring, changes in amplitude may indicate changes in system state. For example, a sudden increase or decrease in the amplitude of current or voltage may indicate an abnormal situation in the system. Amplitude differences can help identify signal components that are significantly different from normal operating conditions in terms of amplitude, thereby improving the sensitivity of fault detection. The waveform change of the signal (i.e., the first-order difference) reflects the dynamic characteristics of the signal. In many practical applications, the dynamic changes of the signal can better reflect the running state of the system than the static amplitude. For example, the frequency change of a vibration signal may be more indicative of mechanical failure than the amplitude change. Waveform change differences can help identify signal components that are significantly different from normal operating conditions in terms of dynamic characteristics, thereby improving the accuracy of fault diagnosis. By analyzing both the numerical difference and the waveform change difference, the importance of signal components can be more comprehensively evaluated, thereby improving the sensitivity, accuracy, and robustness of fault monitoring. This comprehensive analysis method is very effective in practical applications, and can better extract fault features and reduce false positives and missed detections.

[0042] When evaluating the importance of each IMF component, not only the correlation between the component and the original signal is considered, but also the energy information contained in the component is comprehensively considered, so that the contribution of each component to fault identification can be more accurately judged. Specifically, for those IMF components that are not similar to the original signal but have high energy, the invention will not simply eliminate them, but will give them appropriate importance, thereby avoiding missing key fault information due to direct elimination and ensuring the timeliness of fault monitoring. At the same time, for IMF components that are similar to the original signal but have low energy, the invention will give them a smaller weight to reduce the misleading of noise to fault monitoring and effectively reduce the risk of fault misjudgment. This comprehensive evaluation strategy significantly improves the accuracy and reliability of fault monitoring.

[0043] When evaluating the importance of each IMF component, not only the correlation between the component and the original signal is considered, but also the energy information contained in the component is comprehensively considered, so that the contribution of each component to fault identification can be more accurately judged. Specifically, for those IMF components that are not similar to the original signal but have high energy, the invention will not simply eliminate them, but will give them appropriate importance, thereby avoiding missing key fault information due to direct elimination and ensuring the timeliness of fault monitoring. At the same time, for IMF components that are similar to the original signal but have low energy, the invention will give them a smaller weight to reduce the misleading of noise to fault monitoring and effectively reduce the risk of fault misjudgment. This comprehensive evaluation strategy significantly improves the accuracy and reliability of fault monitoring.

[0044] ​When the servo motor is in normal operation, the time series corresponding to the current, voltage and vibration features have significant periodicity. In the time domain, these operation feature sequences present periodic waveforms with relatively small fluctuations. However, when noise exists, although it will cause changes in the fluctuations of the sequence, it will not destroy its inherent periodicity. On the contrary, when a fault signal appears, due to the impact of a specific frequency and the enhancement of harmonics, the periodic waveform in the time domain will be distorted. Therefore, it is crucial to analyze whether the periodicity of the feature sequence is destroyed and whether the waveform remains similar, and the specific steps are as follows:

[0045] The way to obtain the time domain abnormality degree of each IMF component includes:

[0046] First, the length of the sliding window is determined. Specifically, by analyzing the energy spectrum of all feature sequences, the frequency with the maximum average energy value is selected, and the reciprocal of the frequency is used to determine the length of the sliding window. This method can ensure that the window length matches the main frequency component of the signal, thereby effectively capturing the periodic characteristics of the signal. Next, for multiple historical normal operation processes and a to-be-monitored operation process, based on the sliding window length described above, all IMF components are divided into sliding windows. In each sliding window, the variance of the sequence is calculated. Variance is an important indicator of the degree of signal fluctuation. By calculating the variance of each window, the change of the signal in the local time can be understood. Then, based on the sequence variance of all windows in each IMF component, the standard deviation is calculated. The standard deviation further quantifies the distribution of the variance, reflecting the fluctuation characteristics of the signal in the entire time sequence. The calculated standard deviation is compared with the corresponding standard deviation in the historical normal operation process, the difference between them is calculated, and the sum of these differences is calculated. The sum of the differences reflects the deviation degree between the current IMF component and the historical normal state. Finally, the product of the sum of the differences, the standard deviation of the current IMF component and the DTW (Dynamic Time Warping) distance is defined as the time domain abnormality degree of the IMF component. The DTW distance is used to measure the similarity between the current IMF component and the historical normal component in the time sequence. By combining the standard deviation and the DTW distance, the time domain abnormality degree of the IMF component can be more comprehensively evaluated, thereby providing a more accurate basis for fault monitoring.

[0047] Specifically, the time domain abnormality degree satisfies the following relationship:

[0048] ;

[0049] In the formula, denotes the time domain abnormality degree of the i-th IMF component, denotes the time domain abnormality degree of the i-th IMF component, denotes the time domain abnormality degree of the i-th IMF component, The standard deviation of the sliding window variance of each IMF component This indicates the number of normal historical processes. Indicates the first The first historical normal operation process The standard deviation of the sliding window variance of each IMF component Indicates the process to be monitored and the first The first historical normal operation process The average dynamic time bending distance between the IMF components.

[0050] The variance within a sliding window reflects the trend of IMF components within that window. When the variance values ​​of multiple windows are similar, i.e., the standard deviation of the variance is small, this indicates that the IMF components have strong periodicity. This is because a smaller standard deviation means that the degree of signal fluctuation is relatively consistent across different windows, thus reflecting the periodic characteristics of the signal.

[0051] By calculating the difference between the standard deviation of the sliding window variance of the current IMF component and the corresponding standard deviation during historical normal operation, it can be determined whether the periodic waveform of the IMF component has been distorted. The larger the difference, the higher the degree of waveform distortion, and thus the greater the probability of anomalies. The standard deviation of the sliding window variance mainly measures the difference in local periodicity, that is, the change in the periodic characteristics of the signal within a local time range.

[0052] To more comprehensively assess the differences between IMF components, the DTW distance is also introduced. The DTW distance measures the global, overall difference between two IMF components, rather than just local periodic variations. By combining the standard deviation of the sliding window variance with the DTW distance, anomalies in IMF components can be identified and assessed more accurately, thereby improving the accuracy and reliability of fault monitoring.

[0053] S3: Extract the frequency and energy information of odd harmonics from the energy spectrum, combine it with the time domain anomaly degree, obtain the comprehensive anomaly degree based on the frequency and energy changes between odd harmonics and the energy changes of odd harmonics, perform weighted integration based on the importance of IMF components and the comprehensive anomaly degree to obtain the fault degree of the servo motor, input the fault degree into the servo motor control system, and determine whether a fault exists, thus completing the servo motor fault monitoring.

[0054] During the operation of the bottom loading arm, the servo motor starts and stops at a high frequency, resulting in significant noise interference in the operating environment. When a fault first occurs, its anomaly in the time domain is relatively weak and difficult to detect effectively. In the frequency domain, the fault mainly manifests as an increase in odd harmonic energy, exhibiting multiple peaks in the energy spectrum. However, the presence of noise makes these peaks less pronounced, further increasing the difficulty of fault detection. Therefore, a combined frequency domain fault detection approach is adopted, with the specific steps as follows:

[0055] The methods for obtaining the overall anomaly level of each IMF component include:

[0056] First, the time-domain anomaly degree of each component is calculated based on the standard deviation of the sliding window variance and the DTW distance, used to measure the anomalousness of signal fluctuations and morphological changes in the time domain. Next, the energy variations of the odd harmonics of each IMF component and the differences in frequency energy distribution among the odd harmonics are analyzed. Specifically, the difference between the energy of each odd harmonic of the current IMF component and the corresponding odd harmonic energy during historical normal operation is calculated, considering the average difference in frequency energy among these odd harmonics. These differences are smoothed using an exponential function to avoid values ​​that are too large or too small, thus obtaining the frequency-domain anomaly degree. Finally, the frequency-domain anomaly degree is added to the time-domain anomaly degree to obtain the comprehensive anomaly degree. This approach considers not only anomalies in the time domain but also characteristic changes in the frequency domain, thus providing a more comprehensive and accurate evaluation index for fault monitoring.

[0057] Specifically, the overall anomaly level satisfies the following relationship:

[0058] ;

[0059] In the formula, Indicates the first The overall anomaly degree of each IMF component Indicates the first The degree of temporal anomaly of each IMF component, This indicates the number of normal historical processes. Indicates the number of odd harmonics. Indicates the first step in the monitoring process. The first IMF component The energy value of an odd harmonic Indicates the first The first historical normal operation process The first IMF component The energy value of an odd harmonic Indicates the first step in the monitoring process. The first IMF component an average of all frequency energy between the odd harmonic and the previous odd harmonic of the IMF component, represents the first an average of all frequency energy between the odd harmonic and the previous odd harmonic of the IMF component, represents the first an average of all frequency energy between the odd harmonic and the previous odd harmonic of the IMF component, represents an exponential function with base of natural number represents an exponential function with base of natural number

[0060] When detecting the fault of the servo motor of the bottom loading and unloading swivel joint, by analyzing the odd harmonic energy value of the IMF component, it can be judged whether there is an abnormality. Specifically, if the odd harmonic energy value of the IMF component is significantly higher than the energy value in normal operation, it indicates that the probability of abnormality is relatively large. However, since the servo motor will generate wideband noise during operation, such noise will cause the overall energy of the energy spectrum to increase in the wideband. Therefore, the change of the odd harmonic energy value alone for fault judgment may be disturbed by noise, resulting in misjudgment. In order to avoid the fault misjudgment caused by the influence of noise, the abnormality degree of the odd harmonic needs to be adjusted appropriately, so that the abnormality probability is correspondingly reduced; the exponential function is introduced to avoid the numerator being too small, since the influence of noise is small, it does not mean that the abnormality probability is infinite, through the adjustment of the exponential function, the abnormality probability can be more reasonably reflected, thereby improving the accuracy and reliability of fault detection.

[0061] The acquisition method of the fault degree includes:

[0062] First, for each IMF component in each feature sequence, the product of its importance and comprehensive abnormality degree is calculated. The multiplication operation aims to combine the importance of each IMF component with the abnormal situation, so as to more accurately reflect its contribution in fault monitoring. Subsequently, the product results of all IMF components in all feature sequences are accumulated and summed up to obtain an overall abnormality degree index. Finally, the sum result is divided by the product of the number of feature sequences and the number of IMF components to realize normalization processing and obtain the final servo motor fault degree. This process not only considers the individual characteristics of each IMF component, but also ensures fair comparison between different feature sequences and IMF components through normalization processing, thereby providing a quantitative and reliable evaluation index for fault monitoring of the servo motor.

[0063] Not only the energy change of the odd harmonic itself is investigated, but also the frequency energy distribution change between the odd harmonics is analyzed in depth, which can more accurately evaluate the degree of noise influence on the IMF component. Further combining the energy change of the odd harmonic, the frequency domain abnormality is comprehensively evaluated, which effectively distinguishes the energy change caused by noise and the energy change caused by fault, thereby significantly improving the accuracy and reliability of the frequency domain abnormality evaluation.

[0064] By combining the frequency domain anomaly evaluation result with the time domain anomaly evaluation result, dual-mode anomaly detection is realized. This dual-mode detection method can fully utilize the advantages of time domain and frequency domain information, overcome the shortcomings of single-mode detection method, and thus more comprehensively and accurately monitor and diagnose the faults of the servo motor.

[0065] Specifically, the fault degree satisfies the following relationship:

[0066] ;

[0067] In the formula, represents the fault degree of the servo motor, represents the number of features, represents the number of EMD decomposed IMF components, represents the normalized importance degree of the i-th IMF component of the i-th feature, represents the normalized importance degree of the i-th IMF component of the i-th feature, represents the normalized importance degree of the i-th IMF component of the i-th feature, represents the normalized importance degree of the i-th IMF component of the i-th feature.

[0068] In response to the fault degree being less than or equal to a preset fault threshold, the servo motor is in a normal operating state, otherwise, the servo motor is in a fault state, triggering a fault warning.

[0069] The application also provides a fault warning system for a bottom loading and unloading crane pipe. The system includes a processor, a memory, and a warning module. The fault degree is input to the warning module, and it is determined whether there is a fault. The memory stores computer program instructions, which, when executed by the processor, implement the fault monitoring method for the bottom loading and unloading crane pipe according to the first aspect of the application. The system also includes a communication bus and a communication interface, as well as other components known to those skilled in the art. Their settings and functions are known in the art, and thus will not be described here.

[0070] It should be noted that, for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are all within the scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.​​

Claims

1. A method of failure monitoring of a bottom loading and unloading swivel, characterized in that, The method comprises the following steps: Obtain the operation characteristic sequence of the servo motor and pre-process it to obtain the corresponding IMF component and energy spectrum; wherein, the operation characteristic sequence comprises: current sequence, voltage sequence and vibration sequence; For a plurality of historical normal operation processes and a to-be-monitored operation process, according to the energy difference in the energy spectrum of each IMF component sequence and characteristic sequence and the difference in the first-order difference sequence, the importance of the IMF component of each operation characteristic is evaluated, the time domain abnormality degree is obtained based on the standard deviation of the sliding window variance and the DTW distance; From the energy spectrum, the frequency and energy information of the odd harmonics are extracted, combined with the time domain abnormality degree, the comprehensive abnormality degree is obtained according to the frequency energy change between the odd harmonics and the energy change of the odd harmonics, the importance of the IMF component and the comprehensive abnormality degree are weighted and integrated to obtain the fault degree of the servo motor, and the servo motor fault monitoring is completed; Wherein, the importance includes: taking any operation characteristic as a target characteristic, calculating the ratio between the average value of all frequency energies in the energy spectrum of the IMF component in the target characteristic and the average value of all energies in the energy spectrum of the characteristic sequence of the target characteristic, to obtain the energy ratio; The data difference and waveform change rate difference of the IMF component and the target characteristic at each time point are calculated and multiplied and summed to obtain the similarity degree of the corresponding sequence of the target characteristic, the similarity degree is exponentially mapped using a negative exponential function, and the product of the mapping result and the energy ratio is taken as the importance of each IMF component of the target characteristic; The time domain abnormality degree comprises: The reciprocal of the frequency with the maximum average energy value in the energy spectrum of all characteristic sequences is selected as the length of the sliding window; each IMF component is divided into sliding windows, the variance of the sequence in each window is calculated, and the standard deviation is calculated based on the sequence variance of all windows in the IMF component, and the difference between the standard deviation and the standard deviation in the historical normal operation process is summed, and the product of the difference sum, the standard deviation and the dynamic time warping distance is taken as the time domain abnormality degree of each IMF component; The calculation method of the comprehensive abnormality degree comprises: The energy change of the odd harmonics of each IMF component and the distribution difference of the frequency energy between the odd harmonics are calculated, and the distribution difference is smoothed using an exponential function to obtain the frequency domain abnormality degree, and the sum of the frequency domain abnormality degree and the time domain abnormality degree is taken as the comprehensive abnormality degree.

2. The method of fault monitoring of a bottom loading and unloading hose according to claim 1, characterized in that, The calculation method of the fault degree comprises: The product of the importance of each IMF component in each characteristic sequence and the comprehensive abnormality degree is calculated, and the product results of all IMF components of all characteristic sequences are accumulated and summed, and the sum is divided by the product of the number of characteristic sequences and the number of IMF components to obtain the fault degree of the servo motor.

3. The method of fault monitoring of a bottom loading and unloading hose according to claim 1, characterized in that, If the fault degree is less than or equal to a preset fault threshold, the servo motor is in a normal operation state, otherwise, the servo motor is in a fault state, and a fault warning is triggered.

4. The method of fault monitoring of a bottom loading and unloading hose according to claim 1, characterized in that, The pre-processing step comprises: The running characteristic sequence is subjected to wavelet transform to remove high frequency noise, the denoised data is subjected to normalization processing, the normalized data is segmented into several time sequence segments, the time sequence segments are subjected to empirical mode decomposition algorithm, are decomposed into several IMF components, Fourier transform is conducted on each IMF component to convert from time domain to frequency domain, corresponding frequency spectrum is obtained, and energy spectrum of each IMF component is calculated to facilitate identification of main frequency components and energy changes thereof in the signal.

5. A failure warning system for a bottom loading and unloading swivel, characterized in that The method comprises the following steps: A processor, a memory and a pre-warning module, the fault degree is input to the pre-warning module, and it is judged whether there is a fault, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the bottom loading and unloading crane pipe fault monitoring method according to any one of claims 1-4 is realized.

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