A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier

Eren, Levent; İnce, Türker; Kıranyaz, Serkan

doi:10.1007/s11265-018-1378-3

Cited by 464 publications

(175 citation statements)

References 38 publications

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“…To further verify the effectiveness of the proposed rolling-element bearing fault diagnosis methods, the performance of improved 2D LeNet-5 network and improved 1D LeNet-5 network are compared with that of the other nine different fault diagnosis methods based on machine learning or deep learning, including SVM [8], k-NN [9], K-Means [10], BPNN [11], compact 1D CNN without fine-tuning [27], AlexNet [23], VGG-19 [24], ResNet-50 [25] and traditional LeNet-5 network [31].…”

Section: Comparison With Other Fault Diagnosis Methodsmentioning

confidence: 99%

“…The settings of network structure and hyper-parameters of compact 1D CNN, AlexNet, VGG-19 and ResNet-50 can be found in [27,[41][42][43], respectively. The setting of network structure of traditional LeNet-5 network is shown in Table 1, and the setting of hyper-parameters of traditional LeNet-5 network is the same with that of improved 2D LeNet-5 network, which is shown in Table 6.…”

Section: Comparison With Other Fault Diagnosis Methodsmentioning

confidence: 99%

“…The existing researches indicate that the fault diagnosis methods based on 2D CNN can get high diagnosis accuracy, but some problems exist such as time-consuming preprocessing stage, high computational complexity, long training time and poor real-time performance.Compared with 2D CNN, 1D CNN has a simpler network structure and a lower computational complexity, and it directly takes 1D raw vibration signals as input without any preprocessing, so it can provide a faster processing speed and is suitable for real-time fault diagnosis. Recently, there have been many works on rolling-element bearing fault diagnosis using 1D CNN [26][27][28][29][30]. Eren et al [26,27] developed a bearing fault diagnosis system using the compact adaptive 1D CNN classifier, which directly takes raw vibration signals as input and provides a competitive classification performance.…”

mentioning

confidence: 99%

“…Recently, there have been many works on rolling-element bearing fault diagnosis using 1D CNN [26][27][28][29][30]. Eren et al [26,27] developed a bearing fault diagnosis system using the compact adaptive 1D CNN classifier, which directly takes raw vibration signals as input and provides a competitive classification performance. Abdeljaber et al[28] studied a compact 1D CNN to identify, quantify, and localize ball bearing damage.…”

mentioning

confidence: 99%

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Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Wan

Chen

et al. 2020

Sensors

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To address the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional LeNet-5 network used in rolling-element bearing fault diagnosis, a rolling-element bearing fault diagnosis method using improved 2D LeNet-5 network is put forward. The following improvements to the traditional LeNet-5 network are made: the convolution and pooling layers are reasonably designed and the size and number of convolution kernels are carefully adjusted to improve fault classification capability; the batch normalization (BN) is adopted after each convolution layer to improve convergence speed; the dropout operation is performed after each full-connection layer except the last layer to enhance generalization ability. To further improve the efficiency and effectiveness of fault diagnosis, on the basis of improved 2D LeNet-5 network, an end-to-end rolling-element bearing fault diagnosis method based on the improved 1D LeNet-5 network is proposed, which can directly perform 1D convolution and pooling operations on raw vibration signals without any preprocessing. The results show that the improved 2D LeNet-5 network and improved 1D LeNet-5 network achieve a significant performance improvement than traditional LeNet-5 network, the improved 1D LeNet-5 network provides a higher fault diagnosis accuracy with a less training time in most cases, and the improved 2D LeNet-5 network performs better than improved 1D LeNet-5 network under small training samples and strong noise environment.Sensors 2020, 20, 1693 2 of 23 clustering [10], back propagation neural network (BPNN) [11], etc. The traditional rolling-element bearing fault diagnosis methods have been widely used, but with the increasing complexity of vibration signals, these methods have a certain limitation; however, the deep learning methods have a greater advantage in analyzing complicated and non-stationary vibration signals.The deep learning methods can automatically extract fault features from vibration signals [12], recently there are many researches are conducted on rolling-element bearing fault diagnosis using deep learning. Yin et al. [13] extracted the original features of vibration signals through time-domain analysis, frequency-domain analysis and wavelet transform, and obtained the low-dimensional features from 38 original features using the nonlinear global algorithm, and the low-dimensional features array is input into the deep belief network (DBN) to evaluate the performance status of rolling-element bearing. Liu et al. [14] obtained the spectrogram of vibration signals through STFT, used the stacked sparse auto-encoder (SAE) to automatically extract fault features, and employed the softmax regression to identify the fault type of rolling-element bearing. Liu et al. [15] used the recurrent neural network (RNN) to classify the faults of rolling-element bearing, and adopted the gated recurrent unit based denoising auto-encoder to enhance fault classification accuracy. Among different deep learning methods, compared with DBN, ...

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Section: Comparison With Other Fault Diagnosis Methodsmentioning

confidence: 99%

Section: Comparison With Other Fault Diagnosis Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Wan

Chen

et al. 2020

Sensors

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“…C_1 was a pure channel-based attention mechanism followed by the first convolution layer (C1). After getting the channel attention weights from the attention mechanism, we combined it with the feature maps of the C1 layer using Equations (11) and (12) and fed it into the next layer. C_2 was a pure channel-based attention mechanism followed by the MS layer.…”

Section: Evaluations Of Single Attentionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention

Huang

Hao

et al. 2019

Energies

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Recently, deep learning technology was successfully applied to mechanical fault diagnosis. The convolutional neural network (CNN), as a prevalent deep learning model, occupies a place in intelligent fault diagnosis, which reduces the need for human feature extraction and prior knowledge, thereby achieving an end-to-end intelligent fault diagnosis model. However, the data for mechanical fault diagnosis in practical application are limited, the CNN model is too deep and too complex, making it prone to overfitting, and a model with too simple a structure and shallow layers cannot fully learn the effective features of the data. Convolutional filters with fixed window sizes are widely used in existing CNN models, which cannot flexibly select variable pivotal features. The model may be interfered with by redundant information in feature maps during training. Therefore, in this paper, a novel shallow multi-scale convolutional neural network with attention is proposed for bearing fault diagnosis. The shallow multi-scale convolutional neural network structure can fully learn the feature information of input data without overfitting. For the first time, a feature attention mechanism is developed for fault diagnosis to adaptively select features for classification more effectively, where the pivotal feature was emphasized, and the redundant feature was weakened through an attention mechanism. The time frequency representations as the input of the model were obtained from the vibration time domain signals, which contain the complete time domain and frequency domain information of the vibration signals. Compared with the current popular diagnostic methods, the results show that the proposed diagnostic method has fairly high accuracy, and its performance is superior to the existing methods. The average recognition accuracy was 99.86%, and the weak recognition rate of I-07 and I-14 labels was improved.extracted manually include kurtosis [3] and entropy [4], and the time-frequency domain features that can be extracted manually include the wavelet packet [5] and Hilbert spectrum [6]. Classification tasks are involved in fault location, mostly using k-nearest neighbor (k-NN) [3], support vector machine (SVM) [7,8], artificial neural network (ANN) [4], and other methods. Zhang et al. [9] optimized support vector machines using the inter-cluster distance (ICD) in the feature space (ICDSVM) to identify the fault types and the fault severity of bearing. The experimental results, taking into consideration various combinations of fault types, fault degrees, and loads, showed that the proposed method has high accuracy in identifying the fault type and fault degree of the bearing. Although these methods can make full use of existing human knowledge, they cannot sufficiently meet the requirements of working conditions and automation. The automatic completion of diagnostic tasks of feature extraction and classification can be overcome by new advanced artificial intelligence technology.With the rapid improvement of computational operations ...

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A convolutional neural network deep learning method for model class selection

Impraimakis

2023

Earthq Engng Struct Dyn

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The response‐only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the responses from a unique degree of freedom along with their class information train and validate a one‐dimensional convolutional neural network. In doing so, the network selects the model class of new and unlabeled signals without the need of the system input information, or full system identification. An optional physics‐based algorithm enhancement is also examined using the Kalman filter to fuse the system response signals using the kinematics constraints of the acceleration and displacement data. Importantly, the method is shown to select the model class in slight signal variations attributed to the damping behavior or hysteresis behavior on both linear and nonlinear dynamic systems, as well as on a 3D building finite element model, providing a powerful tool for structural health monitoring applications.

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A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier

Cited by 464 publications

References 38 publications

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention

A convolutional neural network deep learning method for model class selection

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