Bearing Fault Classification Based on Convolutional Neural Network in Noise Environment

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.

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Application of Nonlinear Convolutional Neural Network in Small Samples Bearing Fault Classification

2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL) ◽

10.1109/cvidl51233.2020.00053 ◽

2020 ◽

Author(s):

WU Yuhui ◽

Zhang Manjiao

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Fault Classification ◽

Small Samples ◽

Bearing Fault

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FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

IEEE Access ◽

10.1109/access.2021.3056944 ◽

2021 ◽

Vol 9 ◽

pp. 25189-25199

Author(s):

Rishikesh Magar ◽

Lalit Ghule ◽

Junhan Li ◽

Yang Zhao ◽

Amir Barati Farimani

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network ◽

Fault Classification ◽

Bearing Fault

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Bearing Fault Classification Based on Convolutional Neural Network and Uncertainty Analysis

10.23919/ccc52363.2021.9549707 ◽

2021 ◽

Author(s):

Diwang Ruan ◽

Shu Geng ◽

Jianping Yan ◽

Clemens Guhmann

Keyword(s):

Neural Network ◽

Uncertainty Analysis ◽

Convolutional Neural Network ◽

Fault Classification ◽

Bearing Fault

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Deep convolutional neural network with new training method and transfer learning for structural fault classification of vehicle instrument panel structure

Journal of Mechanical Science and Technology ◽

10.1007/s12206-020-1009-3 ◽

2020 ◽

Vol 34 (11) ◽

pp. 4489-4498

Author(s):

Sang-Yun Lee ◽

Sang-Kwon Lee

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Deep Convolutional Neural Network ◽

Fault Classification ◽

Instrument Panel ◽

Training Method

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Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽

10.3390/en13051094 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1094 ◽

Cited By ~ 3

Author(s):

Lanjun Wan ◽

Hongyang Li ◽

Yiwei Chen ◽

Changyun Li

Keyword(s):

Neural Network ◽

Working Conditions ◽

Bp Neural Network ◽

Evidence Theory ◽

Prediction Method ◽

Rolling Bearing ◽

Fault Prediction ◽

Fault Classification ◽

Bearing Fault ◽

Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

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Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽

10.3390/electronics10111248 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1248

Author(s):

Rafia Nishat Toma ◽

Cheol-Hong Kim ◽

Jong-Myon Kim

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Empirical Mode Decomposition ◽

Vibration Signal ◽

Ensemble Empirical Mode Decomposition ◽

Fault Classification ◽

Original Signal ◽

Time Frequency ◽

Mode Decomposition ◽

Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

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