scholarly journals Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Decai Zhang ◽  
Xueping Ren ◽  
Hanyue Zuo
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Sample Entropy ◽  
Processing Methods ◽  
Vibration Signals ◽  
One Dimensional ◽  
Signal Processing Methods ◽  
Matrix Sample

Vibration signals of gearbox under different loads are sensitive to the existence of the fault and composite fault vibration signals are complex. Traditional fault diagnosis methods mostly rely on signal processing methods. It is difficult for signal processing methods to separate effective information from those fault signals. Therefore, traditional fault diagnosis methods are difficult to accurately identify those faults. In this paper, a one-dimensional convolutional neural network (1-D CNN) intelligent diagnosis method with improved SoftMax function is proposed. Local mean decomposition (LMD) decomposes the signals into different physical fictions (PF). PFs are input into the matrix sample entropy based on Euclidean distance (MESE), and the PFs which best reflect fault characteristics are selected. Finally, the PFs by MESE are used to train the CNN to identify the faults of parallel-shaft gearbox. Experiment shows that MESE can quickly and accurately select the PFs with the most significant fault features. 1-D CNN can get nearly 100% recognition rate with less time and the CNN of SoftMax improved can effectively eliminate LMD endpoint effect. This method can successfully identify single faults, combination faults, and faults under different loads of the gearbox. Compared with other methods, this method has the characteristics of high efficiency, accuracy, and strong anti-interference. Therefore, it can effectively solve the problem of complex fault signal decomposition of gearbox and can diagnose the gearbox fault under different load operation. It has great significance for gearbox fault diagnosis in actual production.

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

2022 ◽  
Author(s):  
Canyi Du ◽  
Rui Zhong ◽  
Yishen Zhuo ◽  
Xinyu Zhang ◽  
Feifei Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Vibration Signals ◽  
One Dimensional ◽  
Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

scholarly journals Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7319
Author(s):  
Jiajun He ◽  
Ping Wu ◽  
Yizhi Tong ◽  
Xujie Zhang ◽  
Meizhen Lei ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Vibration Signals ◽  
Maintenance Costs ◽  
One Dimensional ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Diagnosis Method

Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.

scholarly journals Multi-input Convolutional Neural Network Fault Diagnosis Algorithm Based on the Hydraulic Pump

2021 ◽  
Vol 2095 (1) ◽  
pp. 012069
Author(s):  
Lishan Zhang ◽  
Lei Han ◽  
Yuzhen Meng ◽  
Wenkui Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Radial Vibration ◽  
Hydraulic Pump ◽  
Neural Network Models ◽  
Vibration Signals ◽  
One Dimensional ◽  
Fully Connected

Abstract Convolutional neural network used in fault diagnosis can effectively extract fault features in vibration signals. However, in the feature extraction of mechanical fault diagnosis, usually more than two feature signals including at least axial and radial vibration signals can be extracted. This paper proposes two multi-input convolutional neural network models based on the fault data of the aircraft hydraulic pump including axial and radial vibration. The first is the Independent Input Multi-input Convolutional Neural Network model. The two inputs are respectively used for convolution pooling operation with CNN, and are combined through the concatenate function before the fully connected layer, and then all frames are integrated and flattened by the flatten function. A one-dimensional array, finally enters the fully connected layer and outputs the result through the softmax function. The second is the Combined Input Multiinput Convolutional Neural Network, that is, combine two one-dimensional signals into a twodimensional signal in the input layer of the convolutional neural network and then perform convolution pooling, and finally output the result through the softmax function. The results show that the two models have good accuracy and stability, and the second one has a higher convergence and fitting efficiency than the first one.

Multiscale convolutional neural network and decision fusion for rolling bearing fault diagnosis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Defeng Lv ◽  
Huawei Wang ◽  
Changchang Che
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Decision Fusion ◽  
Fusion Model ◽  
Vibration Signals ◽  
Content Type ◽  
Proposed Model

Purpose The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing. Design/methodology/approach To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results. Findings The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models. Originality/value The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

scholarly journals Signal Processing Methods for Identification of Induction Motor Bearing Fault

2019 ◽  
Vol 8 (3) ◽  
pp. 143-151
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Induction Motor ◽  
Dominant Mode ◽  
Processing Methods ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Vibration Signal Analysis ◽  
Signal Processing Methods ◽  
Motor Bearing

To diagnose early faults as soon as possible, the feature extraction of vibration signals is very important in real engineering applications. Recently, the advanced signal processing-based weak feature extraction method has been becoming a hot research topic. The dominant mode of failure in rolling element bearings is spalling of the races or the rolling elements. Localized defects generate a series of impact vibrations every time whenever running roller passes over the surface of a defect. Therefore, vibration analysis is a conventional method for bearing fault detection. However, the measured vibration signals of rotating machinery often present nonlinear and non-stationary characteristics. This paper deals with the diagnosis of induction motor bearing based on vibration signal analysis. It provides a comparative study between traditional signal processing methods, such as Power Spectrum, Short Time Fourier Transform, Wavelet Transform, and Hilbert Transform. Performances of these techniques are assessed on real vibration data and compared for healthy and faulty bearing.

scholarly journals Fault diagnosis of rolling bearing based on multi-scale one-dimensional convolutional neural network

2021 ◽  
Vol 1207 (1) ◽  
pp. 012003
Author(s):  
Xukun Hou ◽  
Pengjie Hu ◽  
Wenliao Du ◽  
Xiaoyun Gong ◽  
Hongchao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
One Dimensional ◽  
Bearing Fault ◽  
Multi Scale ◽  
Reconstructed Signal ◽  
Using Data

Abstract Aiming at the typical non-stationary and nonlinear characteristics of rolling bearing vibration signals, a multi-scale convolutional neural network method for bearing fault diagnosis based on wavelet transform and one-dimensional convolutional neural network is proposed. First, the signal is decomposed into multi scale components with wavelet transform, and then each scale component is reconstructed. The reconstructed signal is subjected to the Fourier transform to obtain the frequency spectrum representation, which is used as the input of the one-dimensional convolutional neural network. Finally, one-dimensional convolution neural network is used to learn the features of the input data and recognize the bearing fault. The performance of the model is verified by using data sets of rolling bearing. The results show that this method can intelligent feature extraction and obtain 99.94% diagnostic accuracy.

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