Fault Diagnosis of Rotating Machinery Based on Improved Wavelet Packet Algorithm

2013 ◽  
Vol 5 (6) ◽  
pp. 267-275 ◽  
Author(s):  
Jiao Xintao ◽  
Ding Kang ◽  
Yu Songsen
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rotating Machinery

scholarly journals Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zong Yuan ◽  
Taotao Zhou ◽  
Jie Liu ◽  
Changhe Zhang ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Ranking And Selection ◽  
Fault Features ◽  
Feature Importance ◽  
Importance Ranking ◽  
Common Signal ◽  
Diagnosis Approach

The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.

Intelligent fault diagnosis of rotating machinery based on multiple relevance vector machines with variance radial basis function kernel

2011 ◽  
Vol 225 (7) ◽  
pp. 1718-1729 ◽  
Author(s):  
C He ◽  
C Liu ◽  
Y Li ◽  
J Yuan
Keyword(s):  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Intelligent Fault Diagnosis ◽  
Relevance Vector Machines ◽  
Vector Machines ◽  
Radial Basis ◽  
Diagnosis Model

Automatic and accurate fault diagnosis is very important for condition-based maintenance. In this study, an intelligent fault diagnosis method based on relevance vector machines (RVM) is proposed for automatic fault diagnosis of rotating machinery. First, the global optimal features from all node energies of full wavelet packet tree are obtained by combining wavelet packet transform with an improved Fisher feature selection method. Individual salient feature subsets are selected for each pair of classes separately. Then, RVM method is adopted to train the intelligent fault diagnosis model. The multi-class RVM classifier is constructed by combining several RVM binary classifiers using ‘max-probability-win’ strategy. Moreover, improved from Gaussian radial basis function, a new kernel function denoted variance radial basis function is developed and used for RVM to adaptively balance the difference between the scales of different features. The proposed method was carried out to develop a multi-class bearing fault diagnosis model under varying load conditions, resulting in high accuracy around 99.58 per cent. Experimental results demonstrate that the proposed method is promising for intelligent fault diagnosis of rotating machinery.

Research on the Imbalance-Crack Coupling Fault Diagnosis Based on Wavelet Packet and Energy Spectrum Analysis

2011 ◽  
Vol 143-144 ◽  
pp. 675-679 ◽  
Author(s):  
Fu Ze Xu ◽  
Xue Jun Li ◽  
Guang Bin Wang ◽  
Da Lian Yang
Keyword(s):  
Mechanical Properties ◽  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Spectrum Analysis ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Vibration Acceleration ◽  
Acceleration Signal ◽  
Coupling Fault ◽  
Fault Information

It is common for the imbalance-crack coupling fault in rotating machinery, while the crack information is often overshadowed by unbalanced fault information, which is difficult to extract the crack signal. In order to extract the crack signal of the imbalance-crack coupling fault, and realize the fault diagnosis, the paper mainly analyzes its mechanical properties, and then use wavelet packet to de-nosing, decomposing and reconstructing the acquisition of vibration acceleration signal, and then analyzing the characteristics of frequency domain of the fault signal by using the energy spectrum. So the experiment proved that analyze and dispose the acquisition of the fault signal by using the method of the energy spectrum and the wavelet packet, which can effectively distinguish between the crack signal and unbalanced signals in imbalance-crack coupling faults .It also can provide some reference for the diagnosis and prevention for such fault.

scholarly journals Early Fault Diagnosis for Planetary Gearbox Based Wavelet Packet Energy and Modulation Signal Bispectrum Analysis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2908 ◽  
Author(s):  
Junchao Guo ◽  
Zhanqun Shi ◽  
Haiyang Li ◽  
Dong Zhen ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
High Energy ◽  
Rotating Machinery ◽  
Economic Losses ◽  
Mechanical Transmission ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Reconstructed Signal ◽  
Modulation Signal

The planetary gearbox is at the heart of most rotating machinery. The premature failure and subsequent downtime of a planetary gearbox not only seriously affects the reliability and safety of the entire rotating machinery but also results in severe accidents and economic losses in industrial applications. It is an important and challenging task to accurately detect failures in a planetary gearbox at an early stage to ensure the safety and reliability of the mechanical transmission system. In this paper, a novel method based on wavelet packet energy (WPE) and modulation signal bispectrum (MSB) analysis is proposed for planetary gearbox early fault diagnostics. First, the vibration signal is decomposed into different time-frequency subspaces using wavelet packet decomposition (WPD). The WPE is calculated in each time-frequency subspace. Secondly, the relatively high energy vectors are selected from a WPE matrix to obtain a reconstructed signal. The reconstructed signal is then subjected to MSB analysis to obtain the fault characteristic frequency for fault diagnosis of the planetary gearbox. The validity of the proposed method is carried out through analyzing the vibration signals of the test planetary gearbox in two fault cases. One fault is a chipped sun gear tooth and the other is an inner-race fault in the planet gear bearing. The results show that the proposed method is feasible and effective for early fault diagnosis in planetary gearboxes.

scholarly journals A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 461 ◽  
Author(s):  
Yangyang Zhang ◽  
Yunxian Jia ◽  
Weiyi Wu ◽  
Zhonghua Cheng ◽  
Xiaobo Su ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Failure Modes ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Adaboost Algorithm ◽  
Feature Parameters ◽  
Reconstructed Signal ◽  
Diagnosis Method ◽  
Frequency Band Energy

Gearbox is an important structure of rotating machinery, and the accurate fault diagnosis of gearboxes is of great significance for ensuring efficient and safe operation of rotating machinery. Aiming at the problem that there is little common compound fault data of gearboxes, and there is a lack of an effective diagnosis method, a gearbox fault simulation experiment platform is set up, and a diagnosis method for the compound fault of gearboxes based on multi-feature and BP-AdaBoost is proposed. Firstly, the vibration signals of six typical states of gearbox are obtained, and the original signals are decomposed by empirical mode decomposition and reconstruct the new signal to achieve the purpose of noise reduction. Then, perform the time domain analysis and wavelet packet analysis on the reconstructed signal, extract three time domain feature parameters with higher sensitivity, and combine them with eight frequency band energy feature parameters obtained by wavelet packet decomposition to form the gearbox state feature vector. Finally, AdaBoost algorithm and BP neural network are used to build the BP-AdaBoost strong classifier model, and feature vectors are input into the model for training and verification. The results show that the proposed method can effectively identify the gearbox failure modes, and has higher accuracy than the traditional fault diagnosis methods, and has certain reference significance and engineering application value.

scholarly journals A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2381 ◽  
Author(s):  
Shangjun Ma ◽  
Wei Cai ◽  
Wenkai Liu ◽  
Zhaowei Shang ◽  
Geng Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Learning Ability ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Network ◽  
Memory Space ◽  
Distribution Features

To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results.

scholarly journals Intelligent Fault Diagnosis of Machines Based on Adaptive Transfer Density Peaks Search Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meng Li ◽  
Yanxue Wang ◽  
Chuyuan Wei
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Computational Time ◽  
Intelligent Fault Diagnosis ◽  
Computational Accuracy ◽  
Density Peak ◽  
Density Peaks ◽  
Peak Search

Intelligent fault diagnosis technology of the rotating machinery is an important way to guarantee the safety of industrial production. To enhance the accuracy of autonomous diagnosis using unlabelled mechanical faults data, a novel intelligent diagnosis algorithm has been developed for rotating machinery based on adaptive transfer density peak search clustering. Combined with the wavelet packet energy feature extraction algorithm, the proposed algorithm can enhance the computational accuracy and reduce the computational time consumption. The proposed adaptive transfer density peak search clustering algorithm can adaptively adjust the classification parameters and mark the categories of unlabelled experimental data. Results of bearing experimental analysis demonstrated that the proposed technique is suitable for machinery fault diagnosis using unlabelled data, compared with other traditional algorithms.

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