Motor Vibration Signals Research Articles

Hilbert-Huang Transform and machine learning based electromechanical analysis of induction machine under power quality disturbances

Monitoring and predicting Power Quality (PQ) is crucial for quickly minimizing risk, protecting induction machines (IMs), and increasing productivity. This paper proposes an electromechanical analytical framework for IM working with various PQ disturbances based on machine learning (ML). This work collects the motor characteristics and PQ data, motor vibration signals, and other sensor data to identify the failures. The complicated electromechanical data is broken down into intrinsic mode functions (IMFs) using the Hilbert-Huang Transform (HHT), which reveals the modes present in the signals. This frequency-domain data is retrieved, paired with time-domain characteristics, and used as input features for ML algorithms. Support Vector Machines (SVMs) trained on labeled datasets in which features extracted from PQ disturbances are used to categorize the disturbances into several groups. The PQ disturbances caused by voltage sags, swells, harmonics, and transients can all be efficiently classified using SVM classifiers, allowing for real-time determination of the kind of disturbance influencing the IM. The accuracy for the SVM in the proposed scheme is 97.2 %. The SVM method is trained with PQ data and classification results using MATLAB classifier and Python software.

Research on diagnosis method of motor vibration signal based on MSCNN-LSTM

Abstract Vibration signal is often considered an important basis for diagnosing motor faults. However, the original vibration signal features a single time series that needs to be shorter. This paper introduces a fault diagnosis approach, MSCNN-LSTM, which integrates a multi-scale one-dimensional convolutional neural network with a long short-term memory network, reflecting the ongoing advancements in deep learning for fault diagnosis. Convolution kernels of varying sizes are accustomed to realizing information integration of various scales and broadening the dimensions of vibration signals. In addition, one-dimensional convolution networks effectively solve the problem of excessively long timing of original signals, and LSTM captures timing dependence for diagnosing motor faults. The experimental results indicate that MSCNN-LSTM has high accuracy in motor fault diagnosis based on vibration signals.

Sparse reservoir computing with vertically coupled vortex spin-torque oscillators for time series prediction

Spin torque nano-oscillators possessing fast nonlinear dynamics and short-term memory functions are potentially able to achieve energy-efficient neuromorphic computing. In this study, we introduce an activation-state controllable spin neuron unit composed of vertically coupled vortex spin torque oscillators and a V–I source circuit is proposed and used to build an energy-efficient sparse reservoir computing (RC) system to solve nonlinear dynamic system prediction task. Based on micromagnetic and electronic circuit simulation, the Mackey–Glass chaotic time series and the real motor vibration signal series can be predicted by the RC system with merely 20 and 100 spin neuron units, respectively. Further study shows that the proposed sparse reservoir system could reduce energy consumption without significantly compromising performance, and a minimal response from inactivated neurons is crucial for maintaining the system’s performance. The accuracy and signal processing speed show the potential of the proposed sparse RC system for high-performance and low-energy neuromorphic computing.

A fault diagnosis method for motor vibration signals incorporating Swin transformer with locally sensitive hash attention

Identification of motor vibration signals is one of the important tasks in motor fault diagnosis and predictive maintenance, and wavelet time–frequency diagram is a commonly used signal analysis method to extract the frequency and time characteristics of signals. In this paper, a method based on local sensitive hashing (LSH)-Swin transformer network is proposed for identifying the wavelet time–frequency diagrams of motor vibration signals to analyze the fault types. The traditional Swin transformer model converges slowly due to the smoothing of the attention distribution when dealing with data with sparse features, while the method proposed in this paper reduces the smoothing of the computed attention and enables the network to learn the key features better by introducing locally-sensitive hash attention in the network model, dividing the sequences in the input attention into multiple hash buckets, calculating the attention weights of only some of the vectors with a high degree of hash similarity, and by sampling discrete samples with the use of the Gumbel Softmax. The experimental results show that the method proposed in this paper has better recognition accuracy and higher computational efficiency compared with the traditional network when processing wavelet time–frequency maps of motor vibration signals, and its validation accuracy reaches 99.7%, the number of parameters also has a decrease of about 13%, and the training network to reach converged epochs is also faster. The method in this paper can provide an effective solution for the analysis and processing of motor vibration signals, and has certain application value in practical engineering.

Centrifugal Pump Cavitation Fault Diagnosis Based on Feature-Level Multi-Source Information Fusion

In nuclear power systems, centrifugal pumps often need to operate under extreme conditions. However, accurately determining the cavitation status of centrifugal pumps under such extreme conditions is challenging. To improve the recognition accuracy of the three statuses of non-cavitation, incipient cavitation, and severe cavitation while improving the anti-interference capability of the monitoring system, this study extracted cavitation features from centrifugal pumps’ motor current and vibration signals under three different operational conditions. It fused the features using feature-level multi-source information fusion (MSIF) based on the backpropagation neural network (BPNN) or support vector machine (SVM) to construct a cavitation status recognition model and analyzed the results to compare with those of recognition without information fusion. The results show that, compared with one signal source, MSIF can significantly improve the recognition accuracy of cavitation statuses. Combined current and pump casing axial monitoring based on the BPNN is the optimal scheme, with an overall recognition accuracy of 97.3% for all operational conditions, compared to 73.9% for the single current signal and 89.3% for the single casing axial vibration signal. These research results can guide the monitoring of cavitation statuses in practical engineering, as well as timely intervention at incipient cavitations to reduce structural damage to centrifugal pumps and prolong service life.

Research on Three Common Fault Diagnosis Methods for AC Asynchronous Motors Based on Deep Learning

<p>In response to the problem that traditional fault diagnosis methods mainly rely on manual search, this paper proposes an improved convolutional neural network based three item asynchronous motor fault diagnosis method. Taking the motor rotor bar fault as the research object, in the early stage of the fault, the characteristic signal is easily mixed with the motor fundamental frequency signal. Therefore, first, the current characteristics of the motor rotor bar fault are analyzed, and then the motor vibration signal is converted into a time-frequency map using wavelet analysis method. Then, based on the superpixel segmentation method, the image is generated into a superpixel block. Finally, the image information is input into an improved neural network, The improved neural network can adaptively extract fault features. The experimental results show that the method described in this article can improve the diagnostic ability for rotor bar breaking faults, and has a higher fault recognition accuracy compared to traditional methods.</p> <p> </p>

Research on Three-Phase Asynchronous Motor Fault Diagnosis Based on Multiscale Weibull Dispersion Entropy.

Three-phase asynchronous motors have a wide range of applications in the machinery industry and fault diagnosis aids in the healthy operation of a motor. In order to improve the accuracy and generalization of fault diagnosis in three-phase asynchronous motors, this paper proposes a three-phase asynchronous motor fault diagnosis method based on the combination of multiscale Weibull dispersive entropy (WB-MDE) and particle swarm optimization-support vector machine (PSO-SVM). Firstly, the Weibull distribution (WB) is used to linearize and smooth the vibration signals to obtain sharper information about the motor state. Secondly, the quantitative features of the regularity and orderliness of a given sequence are extracted using multiscale dispersion entropy (MDE). Then, a support vector machine (SVM) is used to construct a classifier, the parameters are optimized via the particle swarm optimization (PSO) algorithm, and the extracted feature vectors are fed into the optimized SVM model for classification and recognition. Finally, the accuracy and generalization of the model proposed in this paper are tested by adding raw data with Gaussian white noise with different signal-to-noise ratios and the CHIST-ERA SOON public dataset. This paper builds a three-phase asynchronous motor vibration signal experimental platform, through a piezoelectric acceleration sensor to discern the four states of the motor data, to verify the effectiveness of the proposed method. The accuracy of the collected data using the WB-MDE method proposed in this paper for feature extraction and the extracted features using the optimization of the PSO-SVM method for fault classification and identification is 100%. Additionally, the proposed model is tested for noise resistance and generalization. Finally, the superiority of the present method is verified through experiments as well as noise immunity and generalization tests.

Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion

Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The method involves capturing vibration signals from the motor across various operational states and frequencies using vibration sensors. Subsequently, the signals undergo transformation into frequency domain representations through fast Fourier transform. This includes normalizing and concatenating the amplitude frequency and phase frequency signals into comprehensive frequency domain information. Leveraging Gramian image-encoding attributes, cross-domain fusion of time-domain and frequency-domain data is achieved. Finally, the fused Gram angle field map is fed into the ConvMixer deep learning model, augmented by the ECA mechanism to facilitate precise motor fault identification. Experimental outcomes underscore the efficacy of cross-domain data fusion, showcasing improved pattern recognition and recognition rates for the models compared to traditional time-domain methods. Additionally, a comparative analysis of various deep learning models highlights the superior performance of the ECA-ConvMixer model. This study makes significant contributions by introducing a cross-domain data fusion method, merging time-domain and frequency-domain information to enhance motor vibration signal analysis. Additionally, the incorporation of the ECA-ConvMixer deep learning model, equipped with attention mechanisms, effectively captures critical features, thus serving as a robust tool for motor fault diagnosis. These innovations not only enhance diagnostic accuracy but also have broad applications in areas like autonomous vehicles and industry, leading to reduced maintenance expenses and enhanced equipment reliability.

Application of Hybrid Model between the Technique for Order of Preference by Similarity to Ideal Solution and Feature Extractions for Bearing Defect Classification

This paper describes a development that offers new opportunities for detecting faulty bearings. Prioritization is based on the technique for order of preference by similarity to the ideal solution (TOPSIS) for the most discriminative features in the faulty bearing dataset. The proposed model is divided into three steps: feature extraction, feature selection, and classification. In feature extraction, variational mode decomposition (VMD) and fast Fourier transform (FFT) are used to extract features from the measured signal of the test motors and use the symmetrical uncertainty (SU) value for calculation, reducing the redundancy of data. In terms of feature selection, the TOPSIS method is used instead of the traditional filtering method, which is applied to analysis and decision making, and important features are selected from seven filtering methods. Finally, in order to validate the classification ability of the proposed model, k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural networks (ANN) are used as independent classifiers. The effectiveness of the proposed model is evaluated by applying two bearing datasets, namely the current dataset of motor vibration signals and the dataset of bearing motors provided by Case Western Reserve University (CWRU). The results show that the comparison of the proposed model with other models shows the feasibility of this study.

Application of Frequency Domain Analysis Method in Vibration Analysis and Fault Diagnosis of Oil Transfer Pump Unit

The frequency domain analysis method was used to analyze the changes of the vertical spectrum of the non-driving end, the vertical spectrum of the driving end, the horizontal spectrum of the non-driving end and the horizontal spectrum of the driving end of the pump unit under different operating conditions. And the relationship between the vibration value of the motor and the temperature was summarized. The results show that the motor vibration signal components are mainly fundamental frequency and second harmonic, and there are third harmonic components; The motor vibration value increases with the increase of bearing bush temperature and winding temperature. The reason for the failure of the pump unit is that the motor heat is too large, which leads to the high temperature of the motor bearing bush and winding, and the rotor appears bending and expanding deformation, which leads to the increase of the vibration value of the motor. The results show that the frequency domain analysis method has a good application effect in vibration analysis and fault diagnosis of oil transfer pump unit.

A novel motor fault diagnosis method based on principal component analysis (PCA) with a discrete belief rule base (DBRB) system

Motor vibration signal data sets are characteristically random and nonlinear, and its features are difficult to extract for fault identification. To reduce the uncertainty of fault diagnosis, a method based on principal component analysis (PCA) and discrete belief rule base (DBRB) was developed for the first time. Initially, the vibration signal was first denoised using a wavelet threshold algorithm to eliminate interference. Second, overlapping signals were segmented into 15 time windows and a total of 13 typical time domain features and mathematical statistical features were extracted. Third, the dimensions of the features were reduced to three principal components by PCA and were taken as the antecedent attributes of the DBRB. However, the amount of information in each principal component is different, so the variance contribution rate was taken as an antecedent attribute weight to restore the original data characteristics. Fourth, a PCA-DBRB model was established, which effectively avoided the combinatorial explosion problem of rule base in the DBRB model. In addition, to obtain appropriate reference values, the k-means algorithm was introduced to take the cluster centers as reference values. The method was then validated by collecting typical fault data from motor bench experiments. The results demonstrated that compared with other traditional classifiers, this approach is more effective and superior in classification performance and more accurate in diagnosing faults from motor vibration data.

Gated Adaptive Hierarchical Attention Unit Neural Networks for the Life Prediction of Servo Motors

The remaining useful life (RUL) prediction of servo motors plays a greatly indispensable role in preventing abnormal and inefficient operation of servo drive system and even major safety accident. In this article, a novel variant of recurrent neural networks, called gated adaptive hierarchical attention unit (GAHAU), is constructed to efficiently predict the RULs of servo motors, which can not only pay attention to the outputs of reset and update gates but also adaptively divide the hierarchy of the sequence information in attention gate. The updating formulas of GAHAU are derived. With the raw vibration signals of servo motors, a quadratic function-based deep convolutional autoencoder is used for automatically generating the health indicator vector. Based on the constructed HI vector, the RULs of servo motors are accurately predicted by GAHAU. The experiments on servo motors demonstrate that the presented GAHAU has remarkable prediction performance especially for the long-term prediction. Compared to the existing typical RUL prediction methods, the proposed methodology has stronger predictive ability according to the comparative results.

CNN-Based Feature Fusion Motor Fault Diagnosis

Artificial intelligence fields have been using deep learning in recent years. Due to its powerful data mining capabilities, deep learning has a wide-ranging impact on the diagnosis of motor faults. A method for diagnosing motor faults based on the multi-feature fusion of convolutional neural network (CNN) is presented in this paper. As far as the method is concerned, CNN is used as the basic framework, and the CNN model has been improved. First, the collected vibration and current signals are preprocessed. Second, segmented multi-time window synchronous input is performed on the processed data. In addition, a multi-scale feature extraction process and time series fusion of vibration and current signals subject to synchronous input in the same time window can be performed, which ultimately enables the identification of motor faults with a high degree of accuracy. In order to verify the validity of the proposed fault diagnosis model, an experimental platform for fault simulation was built for the motor, and vibration and current signals of different motor states were collected and verified by experimentation. According to the results of the experiment, the method can effectively combine motor vibration and current signal fault features, and thus motor fault diagnosis can be improved. In comparison with a single signal input, a multi-signal input provides greater accuracy and stability. As compared to other multi-signal feature fusion methods, such a deep learning model is able to extract fault features in a more comprehensive manner, which helps to improve the accuracy of motor fault diagnosis.

Quantitative Analysis of Broken Rotor Bars in Cage Motor Based on Energy Characteristics of Vibration Signals.

The rotor, as the power output device of a cage motor, is subject to a type of invisible fault, BRB, during long-term use. The conventional motor vibration signal fault monitoring system only analyzes the rotor qualitatively for the fault of BRBs and cannot evaluate the fault degree of BRBs quantitatively. Moreover, the vibration signal used for monitoring has nonstationary and nonlinear characteristics. It is necessary to manually determine the time window and basis function when extracting the characteristics of the time-frequency domain. To address these problems, this paper proposes a method for quantitative analysis of BRBs based on CEEMD decomposition and weight transformation for feature extraction and then uses the AdaBoost to construct a classifier. The method applies CEEMD for adaptive decomposition while extracting IMFs' energy as the initial feature values, uses OOB for contribution evaluation of features to construct weight vectors, and performs a spatial transformation on the original feature values to expand the differences between the feature vectors. To verify the effectiveness and superiority of the method, vibration signals were collected from motors in four BRB states to produce rotor fault data sets in this paper. The experiment results show that the feature extraction method based on CEEMD decomposition and weight transformation can better extract the feature vectors from the vibration signals, and the constructed classifier can accurately perform quantitative analysis of BRB fault.

Prediction of Automobile Wiper Motor Noise Based on Support Vector Machine with Vibration Sensors.

Wiper motor noise has an important impact on vehicle comfort. Accurate prediction of wiper motor noise can obtain motor NVH performance in motor manufacturing or earlier stage and provide necessary support for NVH performance design of parts and vehicles. However, the prediction accuracy of wiper motor noise by the traditional CAE or testing method is low. Data-driven technology provides a new idea for wiper motor noise prediction with its advantages of high efficiency and high precision. This paper studies the wiper motor noise prediction algorithm based on the motor vibration signal, respectively, using the transmission path analysis theory and the support vector machine theory, and carries on the test verification and comparative analysis of the effect. The results show that the method based on support vector machine is more accurate in the prediction of wiper motor noise and has higher practical engineering value.

Fault Diagnosis of Motor Vibration Signals by Fusion of Spatiotemporal Features

This paper constructs a spatiotemporal feature fusion network (STNet) to enhance the influence of spatiotemporal features of signals on the diagnostic performance during motor fault diagnosis. The STNet consists of the spatial feature processing capability of convolutional neural networks (CNN) and the temporal feature processing capability of recurrent neural networks (RNN). It is used for fault diagnosis of motor vibration signals. The network uses dual-stream branching to extract the fault features of motor vibration signals by a convolutional neural network and gated recurrent unit (GRU) simultaneously. The features are also enhanced by using the attention mechanism. Then, the temporal and spatial features are fused and input into the softmax function for fault discrimination. After that, the fault diagnosis of motor vibration signals is completed. In addition, several sets of experimental evaluations are conducted. The experimental results show that the vibration signal processing method combined with spatiotemporal features can effectively improve the recognition accuracy of motor faults.

Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone

Induction motors are key pieces of equipment in today’s society, powering a variety of industrial drives and home appliances. The induction motor speed is often used to monitor the performance of all kinds of industrial drives. For example, in the industrial field, the motor speed is very often used to determine the efficiency and mechanical load of motors. In this work, a new simple, low-cost, and nonintrusive procedure is proposed for infield measurement of induction motors speed, which is based on the spectral analysis of the vibration signal of the motors. The motor vibration signal is first acquired using the accelerometers integrated into a basic phone. The acquired signal is then treated by a MATLAB-based algorithm, which can determine the motor speed by identifying the mechanical frequency of the rotor shaft from the harmonic content of the vibration signal. In this way, it is shown that the mechanical frequency corresponding to the speed of rotation of the motors can be acquired by means of the embedded accelerometers of a common smartphone, avoiding the acquisition and installation of external accelerometers. To the authors’ knowledge, this could be the first time that a smartphone has been proposed as a practical means of measuring the speed of a motor by analysing its vibration. Experimental results from an extensive set of tests, including the supply of the motor from a frequency converter, show that the speed can always be measured with a relative error of less than 0.15%.

인공신경망을 이용한 고장 심각도에 따른 무인기용 유성감속기의 고장진단 연구

Purpose: Gear teeth in the rotary geared actuator of the unmanned aerial vehicle, experience crack propagation because of its harsh operating conditions. To prevent the failure of catastrophic events, this study proposes a diagnostic approach for various gear crack levels based on the built-in and add-on sensor signal.<BR>Methods: A downsized planetary gearbox test rig was prepared, in which the motor position, current, and vibration signals were acquired for the normal and 4 different crack-induced states. Signals were filtered around the region of resonance frequencies by spectral kurtosis and the features for the health state were extracted. Then, feature selection was conducted based on the correlation with fault levels. Finally, the Artificial Neural Network (ANN) model was constructed to identify different fault sizes of the cracks, and K-fold validation was adopted to optimize the parameters of the ANN model.<BR>Results: Among the various signals, the vibration from the add-on sensor and a position from the built-in sensor exhibited high performance compared to the current signal. The features after band-pass filtering yielded a high correlation with fault severity.<BR>Conclusion: The proposed method successfully diagnosed different fault severities of gear cracks in the planetary gearbox by using both the built-in and add-on signals.

Prognostics of Induction Motor Shaft Based on Feature Importance and Least Square Support Vector Machine Regression

This paper aims to present a prognostic method for induction motor shafts that experience fatigue failure in the keyway area, using motor vibration signals. Preprocessing the data to eliminate noise in raw signals is done by decomposing the signal, using discrete wavelet transforms. Prognostic indicator candidates are obtained through the selection of features based on its importance, which involve the superposition of monotonicity and trendability parameters. The prognostics model is built based on the least squares support vector machine regression approach. Remaining useful life (RUL) estimates of motor shafts were performed by fitting the sum of two exponential functions to the regression results and extrapolating over time until the specified failure threshold hits. The results of the study show that the proposed method can work satisfactorily to estimate the RUL of motor shaft. The best prognostic indicator namely the RMS, can be used to predict the motor shaft RUL since 50% of the time step before the end of the motor shaft life is error bound within 20%.

Motor Fault Diagnosis Algorithm Based on Wavelet and Attention Mechanism

In order to improve the maintenance efficiency of the motor and realize the real‐time fault diagnosis function of the motor, a motor fault diagnosis algorithm based on wavelet and attention mechanism is proposed. Firstly, the motor vibration signal is decomposed by wavelet transform, and the high‐frequency signal is denoised to improve the signal‐to‐noise ratio. Secondly, the frequency band and time dimension after wavelet decomposition are taken as input data, the convolution neural network is used to fuse the frequency band features of data, and the bidirectional gated loop unit is used to fuse the time series features. Then, the attention mechanism is used to adaptively integrate the features of different time points. Finally, motor fault diagnosis and prediction are realized by classifier recognition. Experimental results show that, compared with the existing deep learning fault diagnosis model, this method has higher diagnosis accuracy and can accurately diagnose the running state of the motor.