scholarly journals An Integrated Fault Identification Approach for Rolling Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude-Aware Permutation Entropy

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Wuqiang Liu ◽  
Xiaoqiang Yang ◽  
Shen Jinxing
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Good Time ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Nonlinear Dimensionality Reduction ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Fault Type ◽  
Complex Wavelet

The health condition of rolling bearings, as a widely used part in rotating machineries, directly influences the working efficiency of the equipment. Consequently, timely detection and judgment of the current working status of the bearing is the key to improving productivity. This paper proposes an integrated fault identification technology for rolling bearings, which contains two parts: the fault predetection and the fault recognition. In the part of fault predetection, the threshold based on amplitude-aware permutation entropy (AAPE) is defined to judge whether the bearing currently has a fault. If there is a fault in the bearing, the fault feature is adequately extracted using the feature extraction method combined with dual-tree complex wavelet packet transform (DTCWPT) and generalized composite multiscale amplitude-aware permutation entropy (GCMAAPE). Firstly, the method decomposes the fault vibration signal into a set of subband components through the DTCWPT with good time-frequency decomposing capability. Secondly, the GCMAAPE values of each subband component are computed to generate the initial candidate feature. Next, a low-dimensional feature sample is established using the t-distributed stochastic neighbor embedding (t-SNE) with good nonlinear dimensionality reduction performance to choose sensitive features from the initial high-dimensional features. Afterwards, the featured specimen representing fault information is fed into the deep belief network (DBN) model to judge the fault type. In the end, the superiority of the proposed solution is verified by analyzing the collected experimental data. Detection and classification experiments indicate that the proposed solution can not only accurately detect whether there is a fault but also effectively determine the fault type of the bearing. Besides, this solution can judge the different faults more accurately compared with other ordinary methods.

A novel aging state recognition method of a viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine

2019 ◽  
Vol 19 (1) ◽  
pp. 156-172 ◽  
Author(s):  
Jinxiu Qu ◽  
Changquan Shi ◽  
Feng Ding ◽  
Wenjuan Wang
Keyword(s):  
Support Vector Machine ◽  
Sandwich Structure ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Vibration Response ◽  
Permutation Entropy ◽  
Support Vector ◽  
State Recognition ◽  
Complex Wavelet ◽  
Viscoelastic Layers

A viscoelastic sandwich structure is widely used in mechanical equipment, but therein viscoelastic layers inevitably suffer from aging which changes the dynamic characteristics of the structure and influences the whole performance of the equipment. Hence, accurate and automatic aging state recognition of the viscoelastic sandwich structure is very significant to monitor structural health state and guarantee equipment operating reliably. To fulfill this task, by analyzing the sensor-based vibration response signals, a novel aging state recognition approach of the viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine is proposed in this article. To extract effective aging feature information, the measured nonlinear and non-stationary vibration response signals are processed by dual-tree complex wavelet packet transform, and multiple permutation entropy features are extracted from the frequency-band signals to reflect structural aging states. For accurate and automatic aging state classification, generalized Chebyshev kernel is introduced, and multi-class generalized Chebyshev support vector machine is developed to classify structural aging states. In order to demonstrate the effectiveness of the proposed method, a typical viscoelastic sandwich structure is designed and fabricated, and various structural aging states are created through the hot oxygen–accelerated aging of viscoelastic layers. The testing results show that the proposed method can recognize the different structural aging states accurately and automatically. In addition, the superiority of dual-tree complex wavelet packet transform in processing vibration response signals and the performance of generalized Chebyshev support vector machine in classifying structural aging states are respectively validated by comparing with the commonly used methods.

Looseness condition feature extraction of viscoelastic sandwich structure using dual-tree complex wavelet packet-based deep autoencoder network

2019 ◽  
Vol 19 (3) ◽  
pp. 873-884
Author(s):  
Yue Si ◽  
Zhousuo Zhang ◽  
Chuiqing Kong ◽  
Shujuan Li ◽  
Guigeng Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Sandwich Structure ◽  
Detection Method ◽  
Sandwich Structures ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Vibration Response ◽  
Permutation Entropy ◽  
Response Signal ◽  
Complex Wavelet

It is significant to perform looseness condition detection of viscoelastic sandwich structures to avoid serious accidents. Due to the multilayer characteristic of the viscoelastic sandwich structure, the vibration response signal of such structures is nonlinear and nonstationary. Furthermore, the looseness condition feature signal contained in the vibration response signal is very puny. Condition feature extraction has become a challenging task in the looseness condition detection of viscoelastic sandwich structures. Therefore, a novel method called dual-tree complex wavelet packet-based deep autoencoder network is proposed for this task. First, the vibration response signal of the viscoelastic sandwich structure is decomposed by dual-tree complex wavelet packet transform and the sub-band signals which contain rich energy are extracted. Then, the energies of the extracted sub-band signals are calculated to form a feature set. Finally, a deep autoencoder network is established to fuse the feature set, and the fused feature is viewed as the detection index to detect the looseness condition of the viscoelastic sandwich structure. The proposed method is applied to the connecting bolt looseness condition detection of the viscoelastic sandwich structure to validate its effectiveness. Compared with the detection method based on dual-tree complex wavelet packet transform and energy and the detection method based on dual-tree complex wavelet packet transform and permutation entropy, the results indicate that the effectiveness of the proposed method in this article is more superior to that of the other two methods.

scholarly journals A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4965 ◽  
Author(s):  
Shoucong Xiong ◽  
Hongdi Zhou ◽  
Shuai He ◽  
Leilei Zhang ◽  
Qi Xia ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Rolling Bearings ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
End To End

Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.

scholarly journals A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Guiji Tang ◽  
Xiaolong Wang ◽  
Yuling He
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Permutation Entropy ◽  
Feature Vectors ◽  
Novel Method ◽  
Complex Wavelet ◽  
Learning Machine ◽  
Low Dimensional

A novel method of fault diagnosis for rolling bearing, which combines the dual tree complex wavelet packet transform (DTCWPT), the improved multiscale permutation entropy (IMPE), and the linear local tangent space alignment (LLTSA) with the extreme learning machine (ELM), is put forward in this paper. In this method, in order to effectively discover the underlying feature information, DTCWPT, which has the attractive properties as nearly shift invariance and reduced aliasing, is firstly utilized to decompose the original signal into a set of subband signals. Then, IMPE, which is designed to reduce the variability of entropy measures, is applied to characterize the properties of each obtained subband signal at different scales. Furthermore, the feature vectors are constructed by combining IMPE of each subband signal. After the feature vectors construction, LLTSA is employed to compress the high dimensional vectors of the training and the testing samples into the low dimensional vectors with better distinguishability. Finally, the ELM classifier is used to automatically accomplish the condition identification with the low dimensional feature vectors. The experimental data analysis results validate the effectiveness of the presented diagnosis method and demonstrate that this method can be applied to distinguish the different fault types and fault degrees of rolling bearings.

Seismic signal analysis based on the dual-tree complex wavelet packet transform

2004 ◽  
Vol 17 (S1) ◽  
pp. 117-122 ◽  
Author(s):  
Zhou-min Xie ◽  
En-fu Wang ◽  
Guo-hong Zhang ◽  
Guo-cun Zhao ◽  
Xu-geng Chen
Keyword(s):  
Signal Analysis ◽  
Wavelet Packet ◽  
Seismic Signal ◽  
Wavelet Packet Transform ◽  
Complex Wavelet

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

2016 ◽  
Vol 231 (22) ◽  
pp. 4186-4196 ◽  
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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