scholarly journals A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8453
Author(s):  
Rafia Nishat Toma ◽  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Classification Model ◽  
Data Driven ◽  
Fault Classification ◽  
Current Signal ◽  
Bearing Fault ◽  
Motor Current ◽  
Diagnosis And Classification

Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain good fault classification accuracy, yet domain expertise and a fair amount of labeled data are important for better features. This paper proposes a deep auto-encoder (DAE) and convolutional neural network (CNN)-based bearing fault classification model using motor current signals of an induction motor (IM). Motor current signals can be easily and non-invasively collected from the motor. However, the current signal collected from industrial sources is highly contaminated with noise; feature calculation thus becomes very challenging. The DAE is utilized for estimating the nonlinear function of the system with the normal state data, and later, the residual signal is obtained. The subsequent CNN model then successfully classified the types of faults from the residual signals. Our proposed semi-supervised approach achieved very high classification accuracy (more than 99%). The inclusion of DAE was found to not only improve the accuracy significantly but also to be potentially useful when the amount of labeled data is small. The experimental outcomes are compared with some existing works on the same dataset, and the performance of this proposed combined approach is found to be comparable with them. In terms of the classification accuracy and other evaluation parameters, the overall method can be considered as an effective approach for bearing fault classification using the motor current signal.

scholarly journals Optimization of a Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Wavelet Packet Transform by Simulated Annealing

2022 ◽  
Author(s):  
Feng He ◽  
Qing Ye
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Optimal Solution ◽  
Fault Classification ◽  
Parameter Adjustment ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Bearings are widely used in various types of electrical machinery and equipment. As their core components, failures will often cause serious consequences . At present, most methods of parameter adjustment are still manual adjustment of parameters. This adjustment method is susceptible to prior knowledge and easy to fall into the local optimal solution, failing to obtain the global optimal solution and requires a lot of resources.Therefore, this paper proposes a new method of bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by simulated annealing algorithm.The experimental results show that the method proposed in this paper has a more accurate effect in feature extraction and fault classification compared with traditional bearing fault diagnosis methods. At the same time, compared with the traditional artificial neural network parameter adjustment, this paper introduces the simulated annealing algorithm to automatically adjust the parameters of the neural network, thereby obtaining an adaptive bearing fault diagnosis method. To verify the effectiveness of the method, the Case Western Reserve University bearing database was used for testing, and the traditional intelligent bearing fault diagnosis method was compared. The results show that the method proposed in this paper has good results in bearing fault diagnosis. Provides a new way of thinking in the field of bearing fault diagnosis in parameter adjustment and fault classification algorithms

scholarly journals Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5150
Author(s):  
Shiza Mushtaq ◽  
M. M. Manjurul Islam ◽  
Muhammad Sohaib
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Algorithms ◽  
Data Driven ◽  
Support Vector ◽  
Comprehensive Review ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented.

scholarly journals Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network

2021 ◽  
Vol 67 (10) ◽  
pp. 489-500
Author(s):  
Shuai Yang ◽  
◽  
Xing Luo ◽  
Chuan Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Industrial Robot ◽  
Rotation Vector ◽  
Fault Classification ◽  
Economic Losses ◽  
Network Training ◽  
Rv Reducer

As a key component of a mechanical drive system, the failure of the reducer will usually cause huge economic losses and even lead to serious casualties in extreme cases. To solve this problem, a two-dimensional convolutional neural network (2D-CNN) is proposed for the fault diagnosis of the rotation vector (RV) reducer installed on the industrial robot (IR). The proposed method can automatically extract the features from the data and reduce the connections between neurons and the parameters that need to be trained with its local receptive field, weight sharing, and subsampling features. Due to the aforementioned characteristics, the efficiency of network training is significantly improved, and verified by the experimental simulations. Comparative experiments with other mainstream methods are carried out to further validate the fault classification accuracy of the proposed method. The results indicate that the proposed method out-performs all the selected methods.

scholarly journals Rolling bearings fault diagnosis and classification based on optimal wavelet subband selection and deep neural network

2021 ◽  
Author(s):  
Rakesh Kumar Jha ◽  
Preety D Swami
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Vital Role ◽  
Fault Classification ◽  
Vibration Signals ◽  
Softmax Classifier ◽  
Practical Applications ◽  
Time Frequency ◽  
Diagnosis And Classification

Abstract Time-frequency analysis plays a vital role in fault diagnosis of nonstationary vibration signals acquired from mechanical systems. However, the practical applications face the challenges of continuous variation in speed and load. Apart from this, the disturbances introduced by noise are inevitable. This paper aims to develop a robust method for fault identification in bearings under varying speed, load and noisy conditions. An Optimal Wavelet Subband Deep Neural Network (OWS-DNN) technique is proposed that automatically extracts features from an optimal wavelet subband selected on the basis of Shannon entropy. After denoising the optimal subband, the optimal subbands are dimensionally reduced by the encoder section of an autoencoder. The output of the encoder can be considered as data features. Finally, softmax classifier is employed to classify the encoder output. The vibration signals were recorded on a machinery fault simulator setup for various combinations of speed and load for healthy and faulty bearings. The signals were subjected to various noise levels and the deep neural network was trained. The achieved experimental results reveal high accuracy in fault classification as compared to other techniques under comparison.

scholarly journals Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

2020 ◽  
Vol 10 (3) ◽  
pp. 770 ◽  
Author(s):  
Guoqiang Li ◽  
Chao Deng ◽  
Jun Wu ◽  
Zuoyi Chen ◽  
Xuebing Xu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Fault Classification ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.

scholarly journals Comprehensive Analysis of Fault Diagnosis Methods for Aluminum Electrolytic Control System

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Jie-jia Li ◽  
Xiao-yan Han ◽  
Peng Zhou ◽  
Xiao-yu Sun ◽  
Na Chang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Decomposition ◽  
Aluminum Electrolysis ◽  
Decision Fusion ◽  
Comprehensive Analysis ◽  
Fault Classification ◽  
Current Signal ◽  
Fault Diagnosis System ◽  
Simulation Results

This paper established the fault diagnosis system of aluminum electrolysis, according to the characteristics of the faults in aluminum electrolysis. This system includes two subsystems; one is process fault subsystem and the other is fault subsystem. Process fault subsystem includes the subneural network layer and decision fusion layer. Decision fusion neural network verifies the diagnosis result of the subneural network by the information transferring over the network and gives the decision of fault synthetically. EMD algorithm is used for data preprocessing of current signal in stator of the fault subsystem. Wavelet decomposition is used to extract feature on current signal in the stator; then, the system inputs the feature to the rough neural network for fault diagnosis and fault classification. The rough neural network gives the results of fault diagnosis. The simulation results verify the feasibility of the method.

Fault Diagnosis of Railway Axlebox Bearing Based on Wavelet Packet and Neural Network

2012 ◽  
Vol 226-228 ◽  
pp. 749-755 ◽  
Author(s):  
Xiao Feng Li ◽  
Yun Xiao Fu ◽  
Li Min Jia
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Diagnostic System ◽  
Vibration Signal ◽  
Fault Classification ◽  
Wavelet Packet Decomposition ◽  
Bearing Fault ◽  
Fault Diagnostic

A real time and effective axlebox bearing fault diagnostic method is significant in the condition-based maintenance. In the axlebox bearing fault diagnostic system, fault features extraction and fault patterns classification are two important aspects to identify whether a axlebox bearing is failure or not. This paper presents a method of axlebox bearing fault diagnosis based on wavelet packet decomposition and BP neural network. First decompose the vibration signal into a finite number of coefficients by wavelet packet decomposition. Then calculate energy moment of each coefficient and take the energy moment as an eigenvector to effectively express the failure feature. Finally BP neural network is used for fault classification. The experimental results show that combining wavelet packet decomposition with BP neural network could identify the axlebox bearing fault effectively. The average diagnosis accuracy rate is 96.67%.

The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shanling Han ◽  
Shoudong Zhang ◽  
Yong Li ◽  
Long Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Classification Model ◽  
Multilabel Classification ◽  
Content Type ◽  
Bearing Fault ◽  
Binary Relevance ◽  
Fault Information ◽  
Gated Recurrent Unit

PurposeIntelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment. At present, the diagnosis of various kinds of bearing fault information, such as the occurrence, location and degree of fault, can be carried out by machine learning and deep learning and realized through the multiclassification method. However, the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information. To improve the above shortcomings, an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.Design/methodology/approachIn this model, the labels of each bearing are binarized by using the binary relevance method. Then, the integrated convolutional neural network and gated recurrent unit (CNN-GRU) is employed to classify faults. Different from the general CNN networks, the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.FindingsThe Paderborn University bearing dataset is utilized to demonstrate the practicability of the model. The experimental results show that the average accuracy in test set is 99.7%, and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing, and the multilabel classification method is superior to the multiclassification method. Consequently, the model can intuitively classify faults with higher accuracy.Originality/valueThe fault labels of each bearing are labeled according to the failure or not, the fault location, the damage mode and the damage degree, and then the binary value is obtained. The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method, and the predicted probability value of each fault label is directly output in the output layer, which visually distinguishes different fault conditions.

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