Method For Motor Fault Diagnosis Research Articles

Motor Fault Diagnosis Based on Convolutional Block Attention Module-Xception Lightweight Neural Network.

Electric motors play a crucial role in self-driving vehicles. Therefore, fault diagnosis in motors is important for ensuring the safety and reliability of vehicles. In order to improve fault detection performance, this paper proposes a motor fault diagnosis method based on vibration signals. Firstly, the vibration signals of each operating state of the motor at different frequencies are measured with vibration sensors. Secondly, the characteristic of Gram image coding is used to realize the coding of time domain information, and the one-dimensional vibration signals are transformed into grayscale diagrams to highlight their features. Finally, the lightweight neural network Xception is chosen as the main tool, and the attention mechanism Convolutional Block Attention Module (CBAM) is introduced into the model to enforce the importance of the characteristic information of the motor faults and realize their accurate identification. Xception is a type of convolutional neural network; its lightweight design maintains excellent performance while significantly reducing the model's order of magnitude. Without affecting the computational complexity and accuracy of the network, the CBAM attention mechanism is added, and Gram's corner field is combined with the improved lightweight neural network. The experimental results show that this model achieves a better recognition effect and faster iteration speed compared with the traditional Convolutional Neural Network (CNN), ResNet, and Xception networks.

Research on rotor fault diagnosis technology of three-phase asynchronous motor based on NA-MEMD mutual information and SVM

Aiming at the problem of mode aliasing in the adaptive decomposition of nonlinear and non-stationary current signals generated by three-phase asynchronous motor faults, and the fault features contained in signals collected by a single sensor can not be accurately and comprehensively extracted and characterized when early rotor bar breakage and air gap eccentricity faults occur, A fault diagnosis method for three-phase asynchronous motor based on noise assisted multivariate empirical mode decomposition (NA-MEMD) and mutual information is proposed. Firstly, the NA-MEMD algorithm is used to decompose the three-phase stator current signal of the asynchronous motor to obtain multi-scale intrinsic mode functions (IMFs). Then, the correlation algorithm is used to screen the IMFs containing useful information. Then, the filtered IMF components are reconstructed into new signals and their features are extracted, Finally, support vector machines (SVM) are used to identify the rotor broken bars and air gap eccentric faults of the three-phase asynchronous motor. The experimental results show that the NA-MEMD method has a higher recognition rate than the traditional empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD) methods.

A novel fault diagnosis method for permanent magnet synchronous motor based on VS-Inception

The new energy vehicle industry has an increasing demand for safety, stability, and efficiency in the operation of essential equipment, particularly in the intelligent means of fault diagnosis of motor equipment, which is rising annually. For the past several years, there has been an increasing demand for accuracy and precision in troubleshooting fault diagnosis of permanent magnet synchronous motors. This paper presents a variable-scale Inception (VS-Inception) permanent magnet motor malfunction diagnostic method on the basis of R parameters. Firstly, the Time Series Generative Adversarial Network is utilized to expand the dataset samples of the collected vibration signals, resulting in many virtual signals with motor fault characteristics. Then, the variable parameter variable scale structure of the VS-Inception model is employed to carry out the fault diagnosis of permanent magnet synchronous motors on the expanded dataset. Finally, comparison experiments of VS-Inception with the existing state-of-the-art methods and with the original method are conducted to confirm the superiority even further and stability of the VS-Inception method in the field of permanent magnet synchronous motor malfunction diagnostic.

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.

Synchronous and fault-tolerance control for dual-motor steer-by-wire system of commercial vehicle

In order to meet the high safety requirement of high-level automatic steering mechanism for commercial vehicle, a kind of steer-by-wire (SBW) system with redundant motors is studied in this paper, and the structure principle and control strategy of the system are studied. Commercial vehicles have the characteristics of large steering resistance moment, which is accompanied by impact and vibration during steering, so the synchronous control of dual motors is the key and difficult problem. Based on the circulating ball steering gear for commercial vehicle, dual-motor synchronous control architecture is designed and the dual-motor global-fast-terminal-sliding-mode (nonsingular-GFTSM) synchronous control is formulated. Compared with the sliding-mode control, the simulation results show that the dual-motor actuator based on nonsingular-GFTSM can better realize the synchronous operation in angle, speed and torque. In addition, a real-time motor fault-diagnosis method based on sliding mode observer and a fault-tolerant control strategy based on smooth switching system are designed, which can achieve fast fault-isolation and smooth torque switching in case of actuator failure in dual-motor system. A Hardware-in-the-Loop (HIL) test bench is built to further verify the effectiveness of the synchronous and fault-tolerance control strategy for dual motors.

A Novel Motor Fault Diagnosis Method Based on Generative Adversarial Learning with Distribution Fusion of Discrete Working Conditions

Many kinds of electrical equipment are used in civil and building engineering. The motor is one of the main power components of this electrical equipment, which can provide stable power output. During the long-term use of motors, various motor faults may occur, which affects the normal use of electrical equipment and even causes accidents. It is significant to apply fault diagnosis for the motors at the construction site. Aiming at the problem that signal data of faulty motor lack diversity, this research designs a multi-layer perceptron Wasserstein generative adversarial network, which is used to enhance training data through distribution fusion. A discrete wavelet decomposition algorithm is employed to extract the low-frequency wavelet coefficients from the original motor current signals. These are used to train the multi-layer perceptron Wasserstein generative adversarial model. Then, the trained model is applied to generate fake current wavelet coefficients with the fused distribution. A motor fault classification model consisting of a feature extractor and pattern recognizer is built based on perceptron. The data augmentation experiment shows that the fake dataset has a larger distribution than the real dataset. The classification model trained on a real dataset, fake dataset and combined dataset achieves 21.5%, 87.2%, and 90.1% prediction accuracy on the unseen real data, respectively. The results indicate that the proposed data augmentation method can effectively generate fake data with the fused distribution. The motor fault classification model trained on a fake dataset has better generalization performance than that trained on a real dataset.

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.

Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square

This study proposes a broken rotor bar (BRB) fault diagnosis method for an induction motor using the sum of the weighted Fourier series coefficients squares of a complex current as a diagnosis signal. First, the sum of the squares of the Fourier series coefficients confirms the very narrow band-pass filter characteristics to derive a specific frequency component. This assists us in obtaining a BRB fault diagnosis signal that exists in a limited frequency range. Second, the magnitude of the Fourier series coefficients of the BRB fault signal is proportional to the slip frequency and load condition. A weighting factor is proposed to render the BRB fault signal irrelevant to the slip frequency and load condition. Consequently, the proposed fault diagnosis can be conducted without the slip frequency information or searching for the maximum coefficient component. Finally, the proposed fault diagnosis method is validated through experiments using a 55 kW induction motor with and without a BRB fault. It is implemented with a DSP controller at time intervals of 20, 10, 5, and 4 s for the Fourier series. The proposed diagnosis method performs well under various load conditions and shows that the derived fault signal exhibits a large difference between healthy and BRB faulty induction motors.

A Motor Fault Diagnosis Method Based on Industrial Wireless Sensor Networks

<p>Nowadays, with the development of the Internet of Things (IoT), the relationship between sensor manufacturing technology and wireless network communication technology is getting closer. It is a great direction that diagnosing motor fault using the sensors with information perception, data processing, and wireless communication capabilities. To reduce the memory requirements, and improve the accuracy and stability of the fault diagnosis, we propose a motor fault diagnosis method based on the industrial wireless sensor network. Our proposed method includes an early warning method based on Bloom filter and a fault diagnosis based method based on decision tree-Bayesian network. Simulation results show that our proposed early warning method can reduce the memory requirements compare to the tradinational early warning method based on a hash table. In addition, simulation results also show that our proposed fault diagnosis method can achieve higher diagnostic accuracy compared to the fault diagnosis method based on traditional Bayesian network and diagnostic Bayesian networks. Moroever, we evaluate our proposed method by experiments. Experimental results show that our proposed method can effectively solve the problem of information data uncertainty in the field of motor fault diagnosis, which verifies that our proposed motor fault diagnosis method can achieve high stability.</p> <p> </p>

Motor Fault Diagnosis Based on Scale Invariant Image Features

Traditional fault diagnosis methods are easy to be affected by different working conditions. This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant feature transform is established in this article. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. The efficiency of this method is validated by experiments, and the data image processing technology has great industrial application value in the field of motor fault detection or monitoring in the age of intelligence.

Research on fault diagnosis method of asynchronous motor based on Park-WPT and WOA-LSSVM

Research on fault diagnosis method of asynchronous motor based on Park-WPT and WOA-LSSVM

Motor Fault Diagnosis Using Image Visual Information and Bag of Words Model

In order to solve the tough issue that is difficult to extract the representative fault features of the hybrid vibration signals from the various industrial applications, a motor fault diagnosis method based on image visual information and bag of words model (BoW) are proposed in this paper. Based on the redundancy information of raw signals, the mapping which is between the original fault signal and the visual information is obtained by the methods of SDP (Symmetrized Dot Pattern) image and dense SIFT (Scale Invariant Feature Transform) features. The bag of visual word (BoVW) and pyramid histogram intersection kernel support vector machine (PHIK-SVM) are applying to complete the fault diagnosis and state recognition of related motors. The test results reveal that the diagnostic accuracy of four fault states could reach 99.50%, moreover, the diagnostic accuracy of different operating conditions under each fault could reach 91.83% as well. Compared with the traditional methods, the proposed method is able to obtain the detailed characteristics of various motors from the perspective of visual knowledge, furthermore, it is more robust and without complex data processing.

Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults.

Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fault diagnosis. Research data are used to compare the performance of the three models through a data set released by the Federal University of Rio de Janeiro (UFRJ). VMD is a non-recursive adaptive signal decomposition method that is suitable for processing the vibration signals of motor equipment under variable working conditions. Applied to bearing fault diagnosis, high-dimensional fault features are extracted. Deep learning shows an absolute advantage in the field of fault diagnosis with its powerful feature extraction capabilities. ResNet101 is used to build a model of motor fault diagnosis. The method of using ResNet101 for image feature learning can extract features for each image block of the image and give full play to the advantages of deep learning to obtain accurate results. Through the three links of signal acquisition, feature extraction, and fault identification and prediction, a mechanical intelligent fault diagnosis system is established to identify the healthy or faulty state of a motor. The experimental results show that this method can accurately identify six common motor faults, and the prediction accuracy rate is 94%. Thus, this work provides a more effective method for motor fault diagnosis that has a wide range of application prospects in fault diagnosis engineering.

Study on fault diagnosis method of splitter intelligent wheelchair motor based on wavelet and energy feature

As a key component of a split-type intelligent sports wheelchair for the disabled, the reliability of the motor is related to the personal safety of the wheelchair user and the accurate realization of the wheelchair’s sports functions. This motor is actually just a rotating machine. In order to achieve detection and analysis of rotating machinery bearing vibration signal, a method based on wavelet and energy feature of rotating machinery fault diagnosis is introduced. This method applies wavelet to obtain de-noising and then uses wavelet packet energy feature extraction to diagnose faults effectively caused by rotating machinery such as rotor unbalance fault, rotor misalignment fault, and rotor-to-stator rub fault. Test results illustrate that when different faults occur to the bearing, it is viable to utilize pattern recognition to diagnose faults for the reason that discrepancies appear in sub-hand energy after wavelet packet decomposition. The main research conclusions of this paper are also directly applied to the fault diagnosis of such wheelchair motors.

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

With the development of reliability theory, people realized that “absolutely reliable” machines could not be made. With its incomparable advantages, the high-speed permanent-magnet brushless DC motor is usually used in the symmetrical structure of high-speed operation working systems, which at present are widely used in aerospace and other fields. The structure of the manufacturing process involves a strict processing, but in the process of work failure could still occur. No matter what field the high-speed permanent magnet brushless DC motor is applied to, it is very important to identify states and run fault diagnosis, which is of great significance to maintain the reliability of the motor and its working system. In this study, the fault diagnosis method of a high-speed permanent-magnet brushless DC motor is studied, and a combination model of modified gray wolf optimization algorithm (MGWO) and support vector machine (SVM) have been proposed for the motor fault diagnosis research. Based on the traditional gray wolf optimization (GWO) algorithm, the optimization performance of the algorithm is improved by initializing the population through a tent map and introducing a sine wave dynamic adaptive factor. Then the modified algorithm is used to optimize the internal parameters of SVM to improve the diagnostic accuracy of the model. Through the signal acquisition test, the current signals under different fault states and faultless states were collected, and the current signal data set required for the experiment is obtained. The experimental result showed that, compared with GWO or sailfish optimization (SFO) optimized SVM models, Extreme learning machine and Back Propagation neural network classical classification models, the fault diagnosis accuracy of the proposed model is the highest, proving the excellent classification performance and good robustness of the MGWO-SVM model.

Intelligent Diagnosis of Subway Traction Motor Bearing Fault Based on Improved Stacked Denoising Autoencoder

Aiming at the problem that the complex working conditions affect the effect of manual feature extraction in bearing fault diagnosis of metro traction motor, a fault diagnosis method of metro traction motor bearing based on improved stacked denoising autoencoder (SDAE) is proposed. This method extracts fault features directly from the original vibration signal through deep learning, reduces the dependence on signal processing technology and diagnosis experience, and solves the problem of unsatisfactory effect of extracting feature values under complex working conditions. The effect of the improved SDAE network structure on the accuracy of bearing fault diagnosis is studied through experiments, and the best network parameters are selected. The test results show that the proposed method can well extract the deep features of the fault under the condition of variable speed and variable load; when using data sets with complex working conditions, the classification accuracy of the proposed method is better than that of many traditional fault diagnosis methods.

Intelligent fault diagnosis of three-phase asynchronous motor based on PCA-SVCNN

In order to solve the problems caused by the complex motor structure signals and big data of non-stationary machinery in the traditional asynchronous motor fault diagnosis method, the speed and accuracy of three-phase asynchronous motor fault diagnosis are improved. In this paper, a new fault diagnosis method of three-phase asynchronous motor is proposed. Firstly, the principal component analysis (PCA) method is used to reduce the dimension of the collected current data, and then the support vector machine (SVM) is used to realise the two classification of the data. Finally, the two types of data are classified by convolutional neural network (CNN), and the accurate diagnosis of three-phase asynchronous motor fault can be realised. The simulation results show that the proposed algorithm can improve the accuracy of fault classification quickly and effectively, which is of great significance to the accurate diagnosis of motor faults.

Fault Diagnosis of Subway Traction Motor Bearing Based on Information Fusion under Variable Working Conditions

Under the variable working condition, the fault signal of the rolling bearing contains rich characteristic information. In view of the problem that the traditional fault diagnosis method of the rolling bearing depends on the prior knowledge and expert experience too much and the low recognition rate of some faults with the single signal, one method of rolling bearing fault diagnosis based on information fusion under the variable working condition is proposed. Firstly, one test and multi‐information acquisition system of the rolling bearing is built. Secondly, the metro traction motor bearing nu216 is selected as the research object, and to prefabricate the defects, the data of acoustic emission and vibration acceleration signals during the test of the bearing is acquired. Then, the original signal is processed and extracted by the wavelet packet decomposition, and the normalized feature information is fused by the convolution neural network. Finally, the two‐dimensional convolution neural network model is established to diagnose the bearing fault of the metro traction motor under different conditions. The test results show that the intelligent fault diagnosis method of the subway traction motor bearing based on information fusion under variable working conditions can accurately identify the fault type of the bearing, while the load and speed change. When the neural network training set and the test set cover the same working conditions, the accuracy can reach 100%.

Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information

Fault diagnosis driven by the single signal has been widely used in motor fault diagnosis, but it can not meet the diagnostic requirements of complex motor system. In this study, a motor fault diagnosis method using attention mechanism and improved AdaBoost driven by multi-sensor information is proposed. Firstly, the corresponding frequency domain feature information is obtained by Hilbert transform and Fourier transform in different signals. The improved AdaBoost multi-classification classifier is then used to train signals from different sources and obtain sub classifier results. Finally, a dynamic weight distribution matrix is used to obtain the final diagnosis results with sub classifiers. The proposed method is verified by current, magnetic and vibration signals. The results show that the proposed method dynamically evaluates sensitivity of different detection signals to different faults. Compared with the conventional method, the proposed method can enhance the robustness, generalization ability and accuracy of fault diagnosis.