Rolling Bearing Fault Classification Utilizing Adaptive Density Peaks Search Clustering Based on Wavelet Packet Transform

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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One-level Stationary Wavelet Packet Transform & Hilbert Transform based Rolling Bearing Fault Diagnosis *

2018 IEEE International Conference on Information and Automation (ICIA) ◽

10.1109/icinfa.2018.8812534 ◽

2018 ◽

Author(s):

Yihua Liu

Keyword(s):

Fault Diagnosis ◽

Hilbert Transform ◽

Wavelet Packet ◽

Rolling Bearing ◽

Wavelet Packet Transform ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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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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Fault feature extraction and classification based on WPT and SVD: Application to element bearings with artificially created faults under variable conditions

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406216663782 ◽

2016 ◽

Vol 231 (22) ◽

pp. 4186-4196 ◽

Cited By ~ 7

Author(s):

Mourad Kedadouche ◽

Zhaoheng Liu

Keyword(s):

Support Vector Machine ◽

Singular Value Decomposition ◽

Wavelet Packet ◽

Rolling Bearing ◽

Wavelet Packet Transform ◽

Singular Value ◽

Support Vector ◽

Rolling Bearings ◽

The Matrix ◽

Value Decomposition

Achieving a precise fault diagnosis for rolling bearings under variable conditions is a problematic challenge. In order to enhance the classification and achieves a higher precision for diagnosing rolling bearing degradation, a hybrid method is proposed. The method combines wavelet packet transform, singular value decomposition and support vector machine. The first step of the method is the decomposition of the signal using wavelet packet transform and then instantaneous amplitudes and energy are computed for each component. The Second step is to apply the singular value decomposition to the matrix constructed by the instantaneous amplitudes and energy in order to reduce the matrix dimension and obtaining the fault feature unaffected by the operating condition. The features extracted by singular value decomposition are then used as an input to the support vector machine in order to recognize the fault mode of rolling bearings. The method is applied to a bearing with faults created using electro-discharge machining under laboratory conditions. Test results show that the proposed methodology is effective to classify rolling bearing faults with high accuracy.

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