A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds

When performing fault diagnosis tasks on bearings, the change of any bearing’s rotation speed will cause the frequency spectrum of bearing fault characteristics to be blurred. This makes it difficult to extract stable fault features based on manual or intelligent methods, resulting in a decrease in diagnostic accuracy. In this paper, a two-stage, intelligent fault diagnosis method (order-tracking one-dimensional convolutional neural network, OT-1DCNN) is proposed to deal with the problem of fault diagnosis under variable speed conditions. Firstly, the order tracking algorithm is used to resample the monitoring data obtained under different rotation speeds. Then, the one-dimensional convolutional neural network is adopted to extract features of the fault data. Finally, the fault type of collected data can be obtained by fully connected networks based on the features extracted. In the time domain, while the proposed algorithm only relies on the fault data collected under one speed as the training dataset, it is capable of doing fault diagnosis under different speed conditions. In the condition with the largest difference in speed with each dataset, the accuracy of the proposed method is higher than the baseline methods by 0.54% and 11.00%—on CWRU dataset and our own dataset respectively. The results show that the proposed method performs well in dealing with the fault diagnosis under the condition of variable speeds.

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

Sensors ◽

10.3390/s21217319 ◽

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.

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Bearing Fault Diagnosis Method Based on Vision Measurement and Convolutional Neural Network

10.1007/978-981-15-8155-7_448 ◽

2021 ◽

pp. 5429-5437

Author(s):

Shan Chao ◽

Cong Peng

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Vision Measurement ◽

Diagnosis Method

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

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1207/1/012003 ◽

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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Rolling Bearing Fault Diagnosis Method Based on Multilayer Noise Reduction Technology and Improved Convolutional Neural Network

Journal of Mechanical Engineering ◽

10.3901/jme.2021.01.148 ◽

2021 ◽

Vol 57 (1) ◽

pp. 148

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Noise Reduction ◽

Rolling Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method

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Bearing Fault Diagnosis Method Based on Improved Convolutional Neural Network

10.1109/iccais52680.2021.9624654 ◽

2021 ◽

Author(s):

Jiangyun Duan ◽

Youming Wang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method

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Bearing Fault Diagnosis Based on Hermitian Wavelet and One-Dimensional Convolutional Neural Network

Advances in Intelligent Automation and Soft Computing - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-3-030-81007-8_41 ◽

2021 ◽

pp. 369-375

Author(s):

Weizheng Xu ◽

Hui Li

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

One Dimensional ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network

Measurement Science and Technology ◽

10.1088/1361-6501/ab0793 ◽

2019 ◽

Vol 30 (5) ◽

pp. 055402 ◽

Cited By ~ 17

Author(s):

Ping Ma ◽

Hongli Zhang ◽

Wenhui Fan ◽

Cong Wang ◽

Guangrui Wen ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Image Representation ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method

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Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification

Shock and Vibration ◽

10.1155/2020/1971945 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Mingxing Jia ◽

Yuemei Xu ◽

Maoyi Hong ◽

Xiyu Hu

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

Rolling Element Bearing ◽

One Dimensional ◽

Generalization Ability ◽

Bearing Fault ◽

Damage Degree

As one of the most vital parts of rotating equipment, it is an essential work to diagnose rolling bearing failure. The traditional signal processing-based rolling bearing fault diagnosis algorithms rely on artificial feature extraction and expert knowledge. The working condition of rolling bearings is complex and changeable, so the traditional algorithm is slightly lacking adaptability. The damage degree also plays a crucial role in fault monitoring. Different damage degrees may take different remedial measures, but traditional fault-diagnosis algorithms roughly divide the damage degree into several categories, which do not correspond to the continuous value of the damage degree. To solve the abovementioned two problems, this paper proposes a fault-diagnosis algorithm based on “end-to-end” one-dimensional convolutional neural network. The one-dimensional convolution kernel and the pooling layer are directly applied to the original time domain signal. Feature extraction and classifier are merged together, and the extracted features are used to judge the damage degree at the same time. Then, the generalization ability of the model is studied under a variety of conditions. Experiments show that the algorithm can achieve more than 99% accuracy and can accurately give the damage degree of the bearing. It has good performance under different speeds, different types of motors, and different sampling frequencies, and so it has good generalization ability.

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