Engine Fault Diagnosis Based on EEMD Difference Energy Spectrum

2014 ◽  
Vol 598 ◽  
pp. 210-214 ◽  
Author(s):  
Hong Xia Pan ◽  
Gang Xiang Guo ◽  
Hai Feng Ren
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Energy Spectrum ◽  
Working Condition ◽  
Hankel Matrix ◽  
Vibration Signal ◽  
Difference Spectrum ◽  
Singular Value ◽  
Value Decomposition ◽  
Fault Information

To fault diagnosis of diesel engine, put forward a fault diagnosis of diesel engine based on EEMD difference energy spectrum of singular value and RBF. Nonstationary original acceleration vibration signal of kinds of diesel engine’s working condition is separated to several IMF and structure a Hankel matrix by the IMF for singular value decomposition, then de-noise and reconstruction one IMF on the basis of the theory of singular value difference spectrum, and use the reconstructed IMF’s energy which include fault information as the income of RBF. This method can judge the kinds of diesel engine’s working condition and fault types accurately in the experiment.

scholarly journals Research on Rolling Bearing Fault Feature Extraction Based on Singular Value Decomposition considering the Singular Component Accumulative Effect and Teager Energy Operator

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Longlong Li ◽  
Yahui Cui ◽  
Runlin Chen ◽  
Lingping Chen ◽  
Lihua Wang
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Singular Value ◽  
Teager Energy Operator ◽  
Singular Component ◽  
Value Decomposition ◽  
Teager Energy

The extraction of impulsive signatures from a vibration signal is vital for fault diagnosis of rolling element bearings, which are always whelmed by noise, especially in the early stage of defect development. Aiming at the weak defect diagnosis, kurtosis of Teager energy operator (KTEO) spectrum is employed to indicate the fault information capacity of a spectrum, and considering the accumulative effect of a singular component, accumulative kurtosis of TEO (AKTEO) is firstly proposed to determine the proper signal reconstructed order during vibration signal processing using singular value decomposition (SVD). Then, a vibration processing scheme named SVD-AKTEO is designed where an iteration is employed to reflect an accumulative singular effect by kurtosis of TEO spectrum. Finally, the fault diagnosis results can be extracted from the TEO spectrum output by SVD-AKTEO. Simulation data and real data from a run-to-failure experiment of a rolling bearing are adopted to validate the efficiency, and comparative analysis demonstrates the feasibility to detect the early defect of the rolling bearing.

Research on Rolling Element Bearing Fault Diagnosis Based on Singular Value Decomposition and Kurtosis Criterion

2013 ◽  
Vol 432 ◽  
pp. 304-309 ◽  
Author(s):  
Xiao Lin Wang ◽  
Yong Xiang Zhang ◽  
Jie Ping Zhu ◽  
Zhong Qi Shi
Keyword(s):  
Singular Value Decomposition ◽  
Vibration Signal ◽  
Singular Value ◽  
Rolling Element Bearing ◽  
Feature Extraction Method ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
New Feature ◽  
Value Decomposition ◽  
Fault Information

In order to extract the faint fault information from complicated vibration signal of bearing, a new feature extraction method based on singular value decomposition (SVD) and kurtosis criterion is proposed in my work. According to the method, a group of component signals are obtained firstly using SVD, then component signals with equal kurtosis are selected to be summed together, and the weak fault signal is clearly extracted. The effectiveness of the method is demonstrated on both simulated signal and actual data.

Fault diagnosis of a wind turbine rolling bearing using adaptive local iterative filtering and singular value decomposition

2016 ◽  
Vol 39 (11) ◽  
pp. 1643-1648 ◽  
Author(s):  
Xueli An ◽  
Hongtao Zeng ◽  
Weiwei Yang ◽  
Xuemin An
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Wind Turbine ◽  
Multiple Scales ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Singular Value ◽  
Feature Vectors ◽  
Iterative Filtering ◽  
Value Decomposition

Adaptive local iterative filtering (ALIF) is a new signal decomposition method that uses the iterative filters strategy together with an adaptive and data-driven filter length selection to achieve the decomposition. The complexity of wind power generation systems means that the randomness and kinetic mutation behaviour of their vibration signals are demonstrated at different scales. Thus it is necessary to analyse the vibration signal across multiple scales. A method based on ALIF and singular value decomposition (SVD) was used for the fault diagnosis of a wind turbine roller bearing. The ALIF method is used to decompose the bearing vibration signal into several stable components. The components, which contain major fault information, are selected to build an initial feature vector matrix. The singular value of the matrix is computed as the feature vectors of each bearing fault. The feature vectors embody the characteristics of the vibration signal. The nearest neighbour algorithm is used as a classifier to identify faults in a roller bearing. Experimental data show that the proposed method can be used to identify roller bearing faults of a wind turbine.

scholarly journals Early Bearing Fault Diagnosis based on the Improved Singular Value Decomposition Method

2021 ◽  
Author(s):  
Lingli Cui ◽  
Mengxin Sun ◽  
Jinfeng Huang
Keyword(s):  
Singular Value Decomposition ◽  
Decomposition Method ◽  
Selection Index ◽  
Hankel Matrix ◽  
Singular Value ◽  
Bearing Fault ◽  
Singular Value Decomposition Method ◽  
Weak Fault ◽  
Value Decomposition ◽  
Fault Information

Abstract The traditional singular value decomposition (SVD) method is unable to diagnose the weak fault feature of bearings effectively, which means, it is difficult to retain the effective singular components (SCs). Therefore, a new singular value decomposition method, SVD based on the FIC (fault information content), is proposed, which takes the amplitude characteristics of fault feature frequency as the selection index FIC of singular components. Firstly, the Hankel matrix of the original signal is constructed and SVD is applied in the matrix. Secondly, the proposed index FIC is used to evaluate the information of the decomposed SCs. Finally, the SCs with fault information are selected and added to obtain the denoised signal. The results of bearing fault simulation signals and experimental signals show that compared with the traditional differential singular value decomposition (DS-SVD), the proposed method can select the singular components with larger amount of fault information, and is able to diagnose the fault under the heavy noise interference. The new method can be used for signal denoising and weak fault feature extraction.

Roller Bearing Fault Diagnosis Based on ELMD and Fuzzy C-Means Clustering Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1698-1700 ◽  
Author(s):  
Chang Liang Liu ◽  
Xiu Mei Huang ◽  
Xian Jin Luo
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Feature Vector ◽  
Vibration Signal ◽  
Singular Value ◽  
Fuzzy C Means ◽  
Bearing Fault Diagnosis ◽  
Fuzzy C Means Clustering ◽  
Value Decomposition ◽  
Vector Matrix

For the non-stationary characteristics of rotating machinery fault vibration signal, proposed a fault diagnosis method that based on ensemble local mean decomposition (ELMD) to extract fault feature, and fuzzy C-means clustering (FCM) to perform the fault identification. ELMD method can effectively solve the problem of aliasing modes in LMD. Firstly, decomposing the fault vibration signal by ELMD, PF components were obtained in which the initial feature vector matrix, The PF components compose a initial feature vector matrix, and do singular value decomposition, using the singular value decomposition feature vector as the fault characteristic vectors. Finally, using FCM clustering as a fault classifier. Achieved the identification of different fault types. Experimental results show that this method can effectively achieve the bearing fault diagnosis.

Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

2014 ◽  
Vol 229 (10) ◽  
pp. 1887-1903 ◽  
Author(s):  
Ying Zhang ◽  
Hongfu Zuo ◽  
Fang Bai
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Quantitative Indicator ◽  
Bearing Fault Diagnosis

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

scholarly journals Hybridization of time synchronous averaging, singular value decomposition, and adaptive neuro fuzzy inference system for multi-fault bearing diagnosis

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098056
Author(s):  
Walid Touzout ◽  
Djamel Benazzouz ◽  
Fawzi Gougam ◽  
Adel Afia ◽  
Chemseddine Rahmoune
Keyword(s):  
Singular Value Decomposition ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Vibration Signal ◽  
Relevant Information ◽  
Singular Value ◽  
Inference System ◽  
Operation Conditions ◽  
Neuro Fuzzy ◽  
Value Decomposition

Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation conditions is still regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented to provide a powerful automatic tool for features classification.

scholarly journals Research on Fault Diagnosis Model of Rotating Machinery Vibration Based on Information Entropy and Improved SVM

2019 ◽  
Vol 118 ◽  
pp. 02036 ◽  
Author(s):  
Hankun Bing ◽  
Yuzhu Zhao ◽  
Le Pang ◽  
Minmin Zhao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Time Domain ◽  
Information Entropy ◽  
Singular Value ◽  
Support Vector ◽  
Learning Platform ◽  
Diagnosis Model ◽  
Concept Of Information

Based on the concept of information entropy, this paper analyzes typical nonlinear vibration fault signals of steam turbine based on spectrum, wavelet and HHT theory methods, and extracts wavelet energy spectrum entropy, IMF energy spectrum entropy, time domain singular value entropy and frequency domain power spectrum entropy as faults. The feature is supported by a support vector machine (SVM) as a learning platform. The research results show that the fusion information entropy describes the vibration fault more comprehensively, and the support vector machine fault diagnosis model can achieve higher diagnostic accuracy.

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