Joint Wavelet Transform and Time Synchronous Averaging Source Separation Method and its Application on Aero-Engine

2020 ◽  
Author(s):  
Jiqing Cong ◽  
Jianping Jing ◽  
Changmin Chen ◽  
Zezeng Dai ◽  
Jianhua Cheng
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Averaging Method ◽  
Gaussian White Noise ◽  
Rotating Machinery ◽  
Vibration Source ◽  
Vibration Signals ◽  
Aero Engine ◽  
Harmonic Components

Abstract The reliability and safety of aero-engine are often the decisive factors for the safe and reliable flight of commercial aircraft. Hence, the vibration source location and fault diagnosis of aero-engine are of prime importance to detect faults and carry out fast and effective maintenance in time. However, the vibration signals collected by the sensors arranged on the casing of the aero-engine are generally the mixed signals of the main vibration sources inside the engine, and the components are extremely complicated. Therefore, the vibration source identification is a big challenge for a fault diagnosis and health management of the engine. In order to separate the key vibration sources of rotating machinery such as aero-engine, a Joint Wavelet Transform and Time Synchronous Averaging based algorithm (JWTS) is proposed in this paper. Based on the fact that the fundamental frequency and its harmonic and sub-harmonic components are generally included in the vibration spectrum of shaft fault signal of rotating machinery, wavelet transform and time synchronous averaging algorithm are combined to extract them. The algorithm completes separating the main vibration sources with three steps. First, the source number and fundamental frequency of each source are estimated using the wavelet transform. Second, every source is extracted from each observed signal by the time synchronous averaging method. Time synchronous averaging method can effectively extract a signal of cycle and harmonic rotor components and can suppress noise. Third, the optimal estimation of each source is determined according to signal’s 2-norm. Since the extracted source with a larger energy is closer to the real source, and signal’s 2-norm is a good indicator of the signal energy. Hence, the key vibration sources related to rotary speeds of the engine are obtained separately. The method is verified by synthetic mixed signals first. Three periodic signals of different frequencies are used to simulate the vibration sources of the aeroengine. The fundamental, harmonic and sub-harmonic components of them, as well as Gaussian white noise, are randomly mixed. The results show that the JWTS algorithm can estimate the number of the main sources and can extract each source effectively. Then the method is demonstrated using vibration signals of a real aero-engine. The results indicate that the proposed JWTS method has extracted and located the main sources within the aero-engine, including sources from the low-pressure rotor, high-pressure rotor, combustion chamber and accessory. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery, especially for a real aero-engine.

A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network

2017 ◽  
Vol 24 (12) ◽  
pp. 2512-2531 ◽  
Author(s):  
Boualem Merainani ◽  
Chemseddine Rahmoune ◽  
Djamel Benazzouz ◽  
Belkacem Ould-Bouamama
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Singular Value ◽  
Vibration Signals ◽  
Empirical Wavelet Transform ◽  
Som Neural Network ◽  
Value Decomposition

There are growing demands for condition monitoring and fault diagnosis of rotating machinery to lower unscheduled breakdown. Gearboxes are one of the fundamental components of rotating machinery; their faults identification and classification always draw a lot of attention. However, non-stationary vibration signals and low energy of weak faults makes this task challenging in many cases. Thus, a new fault diagnosis method which combines the Hilbert empirical wavelet transform (HEWT), singular value decomposition (SVD), and self-organizing feature map (SOM) neural network is proposed in this paper. HEWT, a new self-adaptive time-frequency analysis was applied to the vibration signals to obtain the instantaneous amplitude matrices. Then, the singular value vectors, as the fault feature vectors were acquired by applying the SVD. Last, the SOM was used for automatic gearbox fault identification and classification. An electromechanical model comprising an induction motor coupled with a single stage spur gearbox is considered where the vibration signals of four typical operation modes were simulated. The conditions include the healthy gearbox, input shaft slant crack, tooth cracking, and tooth surface pitting. Obtained results show that the proposed method effectively identifies the gearbox faults at an early stage and realizes automatic fault diagnosis. Moreover, performance evaluation and comparison between the proposed HEWT–SVD method and Hilbert–Huang transform (HHT)–SVD approach show that the HEWT–SVD is better for feature extraction.

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Bayesian Network ◽  
Frequency Domain ◽  
Evaluation Method ◽  
Roller Bearing ◽  
Rotating Machinery ◽  
Discrimination Index ◽  
Vibration Signals ◽  
Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

2016 ◽  
Vol 70-71 ◽  
pp. 1-35 ◽  
Author(s):  
Jinglong Chen ◽  
Zipeng Li ◽  
Jun Pan ◽  
Gaige Chen ◽  
Yanyang Zi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Inner Product

Identification of Fault Signals in Rotating Machinery Using Higher Order Time Frequency Analysis

1997 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Paul R. White
Keyword(s):  
Fault Diagnosis ◽  
Wigner Distribution ◽  
Higher Order ◽  
Rotating Machinery ◽  
Data Sets ◽  
Higher Order Statistics ◽  
Vibration Signals ◽  
Time Frequency ◽  
Increasing Trend ◽  
Monitoring Application

Abstract Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has superior detection performance to second order Wigner distribution for typical impulsive signals found in a condition monitoring application. These methods are also applied to data sets measured within a car engine and industrial gearbox.

scholarly journals Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jingli Yang ◽  
Tianyu Gao ◽  
Shouda Jiang ◽  
Shijie Li ◽  
Qing Tang
Keyword(s):  
Fault Diagnosis ◽  
Noise Immunity ◽  
Rotating Machinery ◽  
Experimental Results ◽  
Vibration Signals ◽  
One Dimensional ◽  
Noise Interference ◽  
Diagnosis Method ◽  
Diagnosis Model ◽  
Short Time

In actual engineering applications, inevitable noise seriously affects the accuracy of fault diagnosis for rotating machinery. To effectively identify the fault classes of rotating machinery under noise interference, an efficient fault diagnosis method without additional denoising procedures is proposed. First, a one-dimensional deep residual shrinkage network, which directly takes the raw vibration signals contaminated by noise as input, is developed to realize end-to-end fault diagnosis. Then, to further enhance the noise immunity of the diagnosis model, the first layer of the model is set to a wide convolution layer to extract short time features. Moreover, an adaptive batch normalization algorithm (AdaBN) is introduced into the diagnosis model to enhance the adaptability to noise. Experimental results illustrate that the fault diagnosis model for rotating machinery based on one-dimensional deep residual shrinkage network with a wide convolution layer (1D-WDRSN) can accurately identify the fault classes even under noise interference.

Improved Power Transformer Winding Deformation Fault Diagnosis Method

2014 ◽  
Vol 666 ◽  
pp. 149-153 ◽  
Author(s):  
Hong Zhong Ma ◽  
Ning Jiang ◽  
Chun Ning Wang ◽  
Zhi Hui Geng
Keyword(s):  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Characteristic Frequency ◽  
Power Transformer ◽  
Vibration Signal ◽  
Frequency Component ◽  
Transformer Winding ◽  
Diagnosis Method ◽  
Winding Deformation ◽  
Harmonic Components

according to analysing the generation principle of transformer winding deformation and its impact on the vibration signal, and make a large number of trial, it can be found in addition to the fundamental frequency component that can reflect the failure, the new characteristic frequency which conclude 50Hz frequency component and some of its harmonic components, the harmonic components of the fundamental frequency can also reflect the failure. Transformer winding deformation fault diagnosis method is proposed based on the relationship between the characteristic frequency, it can not only diagnose whether the failure inside the transformer windings, but also determine the type of fault. In order to verify the proposed method, deformation fault is set to the actual transformer winding. After de-noising, discounted processing, the acquisition monitoring points of vibration signal is used by the proposed method, and the actual transformer is diagnosed, The diagnostic result is same with actual failure. It is shown that the proposed diagnostic method is accurate and feasible.

scholarly journals Tensor Transfer Learning for Intelligence Fault Diagnosis of Bearing with Semisupervised Partial Label Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chaofan Hu ◽  
Zhichao Zhou ◽  
Biao Wang ◽  
WeiGuang Zheng ◽  
Shuilong He
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Subspace Learning ◽  
Rotating Machinery ◽  
Training Data ◽  
Tensor Representation ◽  
Vibration Signals ◽  
Tensor Representations ◽  
Partial Label Learning ◽  
And Training

A new tensor transfer approach is proposed for rotating machinery intelligent fault diagnosis with semisupervised partial label learning in this paper. Firstly, the vibration signals are constructed as a three-way tensor via trial, condition, and channel. Secondly, for adapting the source and target domains tensor representations directly, without vectorization, the domain adaptation (DA) approach named tensor-aligned invariant subspace learning (TAISL) is first proposed for tensor representation when testing and training data are drawn from different distribution. Then, semisupervised partial label learning (SSPLL) is first introduced for tackling a problem that it is hard to label a large number of instances and there exists much data left to be unlabeled. Ultimately, the proposed method is used to identify faults. The effectiveness and feasibility of the proposed method has been thoroughly validated by transfer fault experiments. The experimental results show that the presented technique can achieve better performance.

scholarly journals A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peng Chen ◽  
Xiaoqiang Zhao ◽  
HongMei Jiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
White Noise ◽  
Gaussian White Noise ◽  
Rolling Bearing ◽  
K Nearest Neighbor ◽  
Vibration Signals ◽  
Diagnosis Method ◽  
Feature Information ◽  
Fault Feature Extraction

In the process of fault feature extraction of rolling bearing, the feature information is difficult to be extracted fully. A novel method of fault feature extraction called hierarchical dispersion entropy is proposed in this paper. In this method, the vibration signals firstly are decomposed hierarchically. Secondly, dispersion entropies of different nodes are calculated. Hierarchical dispersion entropy can realize the comprehensive feature extraction of the high- and low-frequency band information of vibration signals and overcome the problems that dispersion entropy and multiscale dispersion entropy are insufficient to extract the fault feature information of vibration signals. The feasibility of hierarchical dispersion entropy is obtained by analyzing the hierarchical dispersion entropy of Gaussian white noise and compared with the multiscale dispersion entropy of Gaussian white noise. Meanwhile, a fault diagnosis method for rolling bearings based on hierarchical dispersion entropy and k-nearest neighbor (KNN) classifier is developed. Finally, the superiority of the proposed fault diagnosis method is verified in the realization of fault diagnosis of the rolling bearing in different positions and different degrees of damage.

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