Vibration fault diagnosis for hydroelectric generating unit based on generalized S-transform and QPSO-SVM

2019 ◽  
Author(s):  
Jian Luo ◽  
Xinyang Wang ◽  
Yonggan Xu
Keyword(s):  
Fault Diagnosis ◽  
S Transform ◽  
Generalized S Transform

Gear fault diagnosis based on time–frequency domain de-noising using the generalized S transform

2017 ◽  
Vol 24 (15) ◽  
pp. 3338-3347 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Time Frequency ◽  
Transmission Modes ◽  
S Transform ◽  
Mining Machinery ◽  
Gear Fault ◽  
Generalized S Transform ◽  
Gear Fault Diagnosis

Gears are the most important transmission modes used in mining machinery, and gear faults can cause serious damage and even accidents. In the work process, vibration signals are influenced not only by friction, nonlinear stiffness, and nonstationary loads, but also by strong noise. It is difficult to separate the useful information from the noise, which brings some trouble to the fault diagnosis of mining machinery gears. The generalized S transform has the advantages of the short time Fourier transform and wavelet transform and is reversible. The time–frequency energy distribution of the gear vibration signal can be accurately presented by the generalized S transform, and a time–frequency filter factor can be constructed to filter the vibration signal in the time–frequency domain. These characteristics play an important role when the generalized S transform is used to remove the noise in the time–frequency domain. In this paper, a new gear fault diagnosis based on the time–frequency domain de-noising is proposed that uses the generalized S transform. The application principle, method steps, and evaluation index of the method are presented, and a wavelet soft-threshold filtering method is implemented for comparison with the proposed approach. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a gear with a tooth crack. Our analyses also indicate that the proposed method can be used for fault diagnosis of mining machinery gears.

Bearing fault diagnosis method based on the generalized S transform time–frequency spectrum de-noised by singular value decomposition

2018 ◽  
Vol 233 (7) ◽  
pp. 2467-2477 ◽  
Author(s):  
Jianhua Cai ◽  
Yongliang Xiao
Keyword(s):  
Fault Diagnosis ◽  
Frequency Spectrum ◽  
Singular Values ◽  
Rolling Bearing ◽  
Singular Value ◽  
Time Frequency ◽  
Bearing Fault ◽  
S Transform ◽  
Generalized S Transform ◽  
Value Decomposition

In view of the fact that the random noise interferes with the characteristic extraction of a rolling bearing fault signal, a new method of fault feature extraction is proposed based on the combination of the generalized S transform and singular value decomposition (SVD). Firstly, the 2D time–frequency spectrum bearing fault signal is obtained by applying the generalized S transform, and the time–frequency spectrum matrix is used as the objective matrix of SVD to solve the singular values. Then the K-means clustering algorithm is used to classify the singular value sequence, and the singular values for reconstruction are determined. Finally, the de-noised matrix is carried out the generalized S inversion transform to get the de-noised fault signal, and the power spectrum is calculated to finish the fault diagnosis. By analyzing the simulated signal and the actual bearing fault data, results show that the proposed method can effectively identify typical faults of rolling bearings and improve the diagnosis effect of rolling bearing faults. And it provides a new way to realize the fault diagnosis of rolling bearings under noise.

Feature extraction for engine fault diagnosis utilizing the generalized S-transform and non-negative tensor factorization

2011 ◽  
Vol 225 (8) ◽  
pp. 1936-1949 ◽  
Author(s):  
B Li ◽  
P-L Zhang ◽  
S-B Liang ◽  
Y-T Zhang ◽  
H-B Fan
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Fault Classification ◽  
Energy Concentration ◽  
Tensor Factorization ◽  
Structure Information ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform ◽  
Extraction Scheme

In this study, a novel feature extraction scheme was proposed for engine fault diagnosis utilizing the generalized S-transform combined with the non-negative tensor factorization (NTF). To represent the information of the non-stationary vibration signals acquired from engine, the generalized S-transform was used to get a time–frequency distribution with enhanced energy concentration. Meanwhile, a newly developed technique called NTF, which can preserve more structure information hiding in original two-dimensional matrices compared to the non-negative matrix factorization (NMF), was adopted to extract more informative features from the time–frequency matrices. Five operating states of engine were tested in an experiment for evaluating the proposed feature extraction scheme. Four different types of learning algorithms were employed to conduct the fault classification task. The NMF technique was also used for feature extraction and compared with the NTF approach. The experimental results have demonstrated that the proposed feature extraction scheme can achieve a satisfactory performance when applied to diagnose the engine faults.

Signal separation and correction with multiple Doppler acoustic sources for wayside fault diagnosis of train bearings

2016 ◽  
Vol 231 (14) ◽  
pp. 2664-2680
Author(s):  
Shangbin Zhang ◽  
Qingbo He ◽  
Haibin Zhang ◽  
Kesai Ouyang ◽  
Fanrang Kong
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Frequency Distribution ◽  
Signal Separation ◽  
Frequency Distributions ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform ◽  
Original Time ◽  
Time Frequency Distribution

The extraction of single train signal is necessary in wayside fault diagnosis because the acoustic signal acquired by a microphone is composed of multiple train bearing signals and noises. However, the Doppler distortion in the signal acquired by a microphone effectively hinders the signal separation and fault diagnosis. To address this issue, we propose a novel method based on the generalized S-transform, morphological filtering, and time–frequency amplitude matching-based resampling time series for multiple-Doppler-acoustic-source signal separation and correction. First, the original time–frequency distribution is constructed by applying generalized S-transform to the raw signal acquired by a microphone. Based on a morphological filter, several time–frequency distributions corresponding to different single source Doppler fault signals are extracted from the original time–frequency distribution. Subsequently, the time–frequency distributions are reverted to time signals by inverse generalized S-transform. Then, a resampling time series is built by time–frequency amplitude matching to obtain the correct signals without Doppler distortion. Finally, the bearing fault is diagnosed by the envelope spectrum of the correction signal. The effectiveness of this method is verified by simulated and practical signals.

A Fault Diagnosis Approach for Rolling Element Bearing Based on S-Transform and Artificial Neural Network

2017 ◽  
Author(s):  
Ningbo Zhao ◽  
Hongtao Zheng ◽  
Lei Yang ◽  
Zhitao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Health Status ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Rolling Element Bearing ◽  
S Transform ◽  
Rolling Element ◽  
Element Bearing ◽  
Diagnosis Approach

The condition monitoring and fault diagnosis of rolling element bearing is a very important research content in the field of gas turbine health management. In this paper, a hybrid fault diagnosis approach combining S-transform with artificial neural network (ANN) is developed to achieve the accurate feature extraction and effective fault diagnosis of rolling element bearing health status. Considering the nonlinear and non-stationary vibration characteristics of rolling element bearing under stable loading and rotational speeds, S-transform and singular value decomposition (SVD) theory are firstly used to process the vibration signal and extract its time-frequency information features. Then, radical basis function (RBF) neural network classification model is designed to carry out the state pattern recognition and fault diagnosis. As a practical application, the experimental data of rolling element bearing including four health status are analyzed to evaluate the performance of the proposed approach. The results demonstrate that the present hybrid fault diagnosis approach is very effective to extract the fault features and diagnose the fault pattern of rolling element bearing under different rotor speed, which may be a potential technology to enhance the condition monitoring of rotating equipment. Besides, the advantages of the developed approach are also confirmed by the comparisons with the other two approaches, i.e. the Wigner-Ville (WV) distribution and RBF neural network based method as well as the S-transform and Elman neural network based one.

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