scholarly journals Feature extraction for fault diagnosis based on wavelet packet decomposition: An application on linear rolling guide

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879636 ◽  
Author(s):  
Hutian Feng ◽  
Rong Chen ◽  
Yiwei Wang
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Wavelet Packet ◽  
Numerical Control ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Good Ability ◽  
Rolling Guide ◽  
Lubrication Conditions

Linear rolling guide is increasingly being used as the transmission system in computer numerical control machine tools due to its high stiffness, low friction, good ability of precision retaining, and so on. The lubrication of rolling linear guide affects significantly its performance and hence monitoring the lubrication condition during its operation is of great importance. In this article, the relation between different lubrication conditions of linear rolling guide and their corresponding vibration signals is studied. Three lubrication conditions labeled as “Poor,”“Medium,” and “Good” are simulated to represent the actual working conditions. A data acquisition system is set up to acquire the vibration signals corresponding to different conditions. The wavelet packet decomposition is employed to perform time–frequency analysis of the raw signal, after which the energy distribution of the decomposed signals is extracted as the feature. Two linear rolling guides manufactured by different companies are used in the experiments. The results demonstrate that the relation between the energy distribution extracted from vibration signals and lubrication conditions follows a certain rule. A typical feedforward backpropagation neural network is used as the classifier to verify the effectiveness of energy distribution. The average classification accuracy of the network with energy distribution as input is more than 95%. The results show that the lubrication conditions can be characterized by “energy” hidden in the vibration signals and the energy distribution is an appropriate feature that can be used for fault diagnosis of linear rolling guide.

Time-Frequency Characteristics of Vibration Signals Analysis for Large Instantaneous Impact Mill Rolling Based on Wavelet Packet

2013 ◽  
Vol 834-836 ◽  
pp. 1061-1064
Author(s):  
Qi Jun Xiao ◽  
Zhong Hui Luo
Keyword(s):  
Time Domain ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Signal Wave ◽  
Reconstruction Technique ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Impact Vibration ◽  
Signal Time

The wavelet packet decomposition and reconstruction technique is applied to time-frequency analysis of bite steel impact vibration signal by big rolling machine, it is obtained the bite steel impact signal wave packet. According to the size of the wavelet packet energy, it is reconstructed the signal of No.1 and No.2 wavelet packet. According to reconstruction of the signal time domain waveform and FFT spectrum chart, some meaningful conclusions are obtained.

The Fault Diagnosis Method of Diesel Based on Wavelet Pocket and BP Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 910-913 ◽  
Author(s):  
Hong Hui Sun ◽  
Jun Xu ◽  
Qing Hua Zhang ◽  
Hong Xia Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Vibration Signals ◽  
Signal Energy ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Neural Network Algorithm

Because of the well time-frequency spectrum disposal capability of wavelet packet, the wavelet packet algorithm is used to analyze the time - frequency characteristics of diesel vibration signals. The signal energy distributing characteristics based on wavelet packet transform. are extracted and taken as diagnostic characteristic vector, then improved BP neural network algorithm that connects additional momentum with self-adaptive learning rate was used to classify and recognize faults of diesel valves. The experimental results show the fault diagnosis method of diesel based on wavelet pocket and BP neural network is effective and feasible.

scholarly journals Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Maohua Xiao ◽  
Wei Zhang ◽  
Kai Wen ◽  
Yue Zhu ◽  
Yilidaer Yiliyasi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Frequency Resolution ◽  
Particle Swarm Algorithm ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Training Time

AbstractIn the process of Wavelet Analysis, only the low-frequency signals are re-decomposed, and the high-frequency signals are no longer decomposed, resulting in a decrease in frequency resolution with increasing frequency. Therefore, in this paper, firstly, Wavelet Packet Decomposition is used for feature extraction of vibration signals, which makes up for the shortcomings of Wavelet Analysis in extracting fault features of nonlinear vibration signals, and different energy values in different frequency bands are obtained by Wavelet Packet Decomposition. The features are visualized by the K-Means clustering method, and the results show that the extracted energy features can accurately distinguish the different states of the bearing. Then a fault diagnosis model based on BP Neural Network optimized by Beetle Algorithm is proposed to identify the bearing faults. Compared with the Particle Swarm Algorithm, Beetle Algorithm can quickly find the error extreme value, which greatly reduces the training time of the model. At last, two experiments are conducted, which show that the accuracy of the model can reach more than 95%, and the model has a certain anti-interference ability.

scholarly journals A Novel Improved Local Binary Pattern and Its Application to the Fault Diagnosis of Diesel Engine

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Yanping Cai ◽  
Guanghua Xu ◽  
Aihua Li ◽  
Xu Wang
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Local Binary Pattern ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Wavelet Packet ◽  
Texture Feature ◽  
Support Vector ◽  
Vibration Signals ◽  
Time Frequency

Aiming at the feature extraction difficulty of vibration signals, an improved local binary pattern- (ILBP-) based diesel engine fault diagnosis approach is proposed. To effectively make use of the component spatial information in time-frequency images, local binary pattern (LBP) algorithm is applied. Also, in view of the problems that traditional LBP coding is easily interfered by singular pixel points and the relative spatial information is not prominent, an improved coding rule of the LBP operator is put forward in this paper. Compared with some typical LBP algorithms, computational complexity of the proposed ILBP algorithm is greatly reduced, and the coding sparsity is greatly improved. The ILBP operator is applied to fault diagnosis of BF4L1011F diesel engine with eight different valve conditions. For comparison, six kinds of time-frequency distribution are used to convert raw vibration signals into time-frequency images, and then circular LBP, rotation-invariant LBP, uniform LBP, and ILBP operator are applied for texture coding. Finally, nearest neighbor classifier (NNC) and support vector machine (SVM) are used for fault identification. The classification results show that the ILBP operator proposed in this paper can better describe the texture feature information in vibration time-frequency images of the diesel engine, and a good diagnostic effect can be achieved by combining wavelet packet (WP) distribution and ILBP.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

scholarly journals Time–Frequency Domain Characteristics of Acoustic Emission Signals and Critical Fracture Precursor Signals in the Deep Granite Deformation Process

2021 ◽  
Vol 11 (17) ◽  
pp. 8236
Author(s):  
Le Zhang ◽  
Hongguang Ji ◽  
Liyuan Liu ◽  
Jiwei Zhao
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
High Frequency ◽  
Wavelet Packet ◽  
Confining Pressure ◽  
Wavelet Packet Decomposition ◽  
Evolution Law ◽  
Time Frequency ◽  
Characteristic Signal ◽  
Failure Precursor

To study the crack evolution law and failure precursory characteristics of deep granite rocks in the process of deformation and failure under high confining pressure, granite samples obtained from a depth of 1150 m are tested using a TAW-2000 triaxial hydraulic servo testing machine and a PCI-II acoustic emission monitoring system. Based on the stress–strain curve and IET function, the loading process of the sample is divided into five stages: crack closure, linear elastic deformation, microcrack generation and development, macroscopic fracture generation and energy surge, and post-peak failure. The evolution trend and fracture evolution law of the acoustic emission signal event interval function in different stages are analyzed. In particular, the signals with an amplitude greater than 85 dB, a peak frequency greater than 350 kHz, and a frequency centroid greater than 275 kHz are defined as the failure precursor signals before the rock reaches the peak stress. The defined precursor signal conditions agree well with the experimental results. The time–frequency analysis and wavelet packet decomposition of the precursor signal are performed on the extracted characteristic signal of the failure precursor. The results show that the time-domain signal is in the form of a continuous waveform, and the frequency-domain waveform has multi-peak coexistence that is mainly concentrated in the high-frequency region. The energy distribution obtained by the wavelet packet decomposition of the characteristic signal is verified with the frequency-domain waveform. The energy distribution of the signal is mainly concentrated in the 343.75–375 kHz frequency band, followed by the 281.25–312.5 kHz frequency band. The energy proportion of the high-frequency signal increases with the confining pressure.

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