Fault Feature Extraction of High-Speed Automaton Based on Motion Morphology Decomposition

2013 ◽  
Vol 347-350 ◽  
pp. 224-227
Author(s):  
Ai Yu Wang ◽  
Hong Xia Pan ◽  
Hui Ling Liu
Keyword(s):  
Feature Extraction ◽  
High Speed ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Characteristic Parameters ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Fault State ◽  
Fault Feature Extraction ◽  
Fault Characteristic

In order to obtain the characteristic parameters reflecting fault state of high-speed automaton (HSA), the fault feature extraction method based on motion morphology decomposition and wavelet packet transform (WPT) was presented. According to the movement law of the automaton, the vibration signal generated in three bursts of fire was decomposed into three separate signals, then the response signal in each shooting is a separate signal. Then using WPT to respectively extract wavelet packet energy from three separate signals as the fault characteristic parameters of HSA. By the example, the results show that the extracted fault features can well reflect the working conditions of automaton. Thus the proposed method could be used to extract the fault feature of automaton for monitoring the condition and diagnosing the fault of automaton.

scholarly journals The Extraction Method of Gearbox Compound Fault Features Based on EEMD and Cloud Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ling Zhao ◽  
Jiaxing Gong ◽  
Hu Chong
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Cloud Model ◽  
Multiple Faults ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Measurement Principle ◽  
Feature Dimension ◽  
Fault Feature Extraction ◽  
Fault Characteristic

When a compound fault occurs, the randomness and ambiguity of the gearbox will cause uncertainty in the collected signal and reduce the accuracy of signal feature extraction. To improve accuracy, this research proposes a gearbox compound fault feature extraction method, which uses the inverse cloud model to obtain the signal feature value. First, EEMD is used to decompose the collected vibration signals of gearbox faults in normal and fault states. Then, the mutual information method is used to select the sensitive eigenmode function that can reflect the characteristics of the signal. Subsequently, the inverse cloud generator is used to extract cloud digital features and construct sample feature sets. On this basis, the concept of synthetic cloud is introduced, and the cloud-based distance measurement principle is used to synthesize new clouds, reduce the feature dimension, and extract relevant features. Finally, a simulation experiment on a rotating machinery unit with a certain type of equipment verifies that the proposed method can effectively extract the feature of gearbox multiple faults with less feature dimension. And comparing with the feature set extracted by the single cloud model, the results show that the method can better represent the fault characteristic information of the signal.

scholarly journals A Feature Extraction Method of Wheelset-Bearing Fault Based on Wavelet Sparse Representation with Adaptive Local Iterative Filtering

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Zhan Xing ◽  
Jianhui Lin ◽  
Yan Huang ◽  
Cai Yi
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Extraction Method ◽  
High Speed ◽  
Optimal Solution ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Fault Features ◽  
Iterative Filtering ◽  
The Impact

The feature extraction of wheelset-bearing fault is important for the safety service of high-speed train. In recent years, sparse representation is gradually applied to the fault diagnosis of wheelset-bearing. However, it is difficult for traditional sparse representation to extract fault features ideally when some strong interference components are imposed on the signal. Therefore, this paper proposes a novel feature extraction method of wheelset-bearing fault based on the wavelet sparse representation with adaptive local iterative filtering. In this method, the adaptive local iterative filtering reduces the impact of interference components effectively and contributes to the extraction of sparse impulses. The wavelet sparse representation, which adopts L1-regularized optimization for a globally optimal solution in sparse coding, extracts intrinsic features of fault in the wavelet domain. To validate the effectiveness of this proposed method, both simulated signals and experimental signals are analyzed. The results show that the fault features of wheelset-bearing are sufficiently extracted by the proposed method.

scholarly journals Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Long Zhang ◽  
Binghuan Cai ◽  
Guoliang Xiong ◽  
Jianmin Zhou ◽  
Wenbin Tu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Diagnostic Methods ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Transmission Path ◽  
The Individual ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

Fault diagnosis of rolling bearings is not a trivial task because fault-induced periodic transient impulses are always submerged in environmental noise as well as large accidental impulses and attenuated by transmission path. In most hybrid diagnostic methods available for rolling bearings, the problems lie in twofolds. First, most optimization indices used in the individual signal processing stage do not take the periodical characteristic of fault transient impulses into consideration. Second, the individual stages make use of different optimization indices resulting in inconsistent optimization directions and possibly an unsatisfied diagnosis. To solve these problems, a multistage fault feature extraction method of consistent optimization for rolling bearings based on correlated kurtosis (CK) is proposed where maximum correlated kurtosis deconvolution (MCKD) is employed to attenuate the influence of transmission path followed by tunable Q factor wavelet transform (TQWT) to further enhance fault features by decomposing the preprocessed signals into multiple subbands under different Q values. The major contribution of the proposed approach is to consistently use CK as an optimization index of both MCKD and TQWT. The subband signal with the maximum CK which is an index being able to measure the periodical transient impulses in vibration signal yields an envelope spectrum, from which fault diagnosis is implemented. Simulated and experimental signals verified the effectiveness and advantages of the proposed method.

Feature Extraction of Rolling Bearing Based on LMD Energy Feature

2017 ◽  
Vol 868 ◽  
pp. 363-368
Author(s):  
Bang Sheng Xing ◽  
Le Xu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Small Samples ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Local Mean ◽  
Energy Feature

For the situation that it is difficult to diagnose rolling bearings fault effectively for small samples, so it proposes a feature extraction method of rolling bearing based on local mean decomposition (LMD) energy feature. Due to the frequency domain distribution of vibration signals will change when different faults occur in rolling bearings, so it can use LMD energy feature method to extract the fault features of rolling bearings. The instances analysis and extracted results show that the LMD energy feature can extract the vibration signal fault feature of rolling bearings effectively.

A Fault Feature Extraction Method Based on LMD and MSE for Reciprocating Compressor

2014 ◽  
Vol 530-531 ◽  
pp. 345-348
Author(s):  
Min Qiang Xu ◽  
Hai Yang Zhao ◽  
Jin Dong Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Vibration Signal ◽  
Multiscale Entropy ◽  
Reciprocating Compressor ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Euclidean Distances ◽  
Coupling Characteristics ◽  
Fault Feature Extraction

This paper presents a feature extraction method based on LMD and MSE for reciprocating compressor according to the strong nonstationarity, nonlinearity and features coupling characteristics of vibration signal. The vibration signal was decomposed into a set of PFs, and then multiscale entropy of the first several PFs were calculated as feature vectors with different scale factors. Based on the maximum of average Euclidean distances, the feature vectors which have the best divisibility were selected. The feature vectors of reciprocating compressor at different bearing clearance states were extracted using this method, and superiority of this method is verified by comparing with the results of sample entropy.

Fan Fault Feature Extraction Based on Wavelet Packet Transform

2014 ◽  
Vol 668-669 ◽  
pp. 999-1002
Author(s):  
Xin Li ◽  
Pan Feng Guo
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Research Topic ◽  
Signal Characteristic ◽  
Manufacturing Enterprises ◽  
Important Position ◽  
Fault Feature Extraction

Fan occupies the important position in many industry, it give rise to that fault diagnosis become the new hot research topic, also is the urgent demand of many manufacturing enterprises. This paper based on the theory of wavelet packet transform, selecting wavelet packet transform and energy spectrum to wavelet de-noising and fault feature extraction the fan vibration signal. And use the MATLAB get the fan vibration signal characteristic vector, lay the foundation for the fan fault diagnosis.

scholarly journals The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Te Han ◽  
Dongxiang Jiang ◽  
Nanfei Wang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Singular Value ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
The Difference ◽  
Fault Feature Extraction

Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears). Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD) and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise.

scholarly journals Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals

2019 ◽  
Vol 10 ◽  
pp. 13 ◽  
Author(s):  
Long Li ◽  
Jianfeng Xiao ◽  
Bin Wu ◽  
Mengge Zhou ◽  
Qian Wang
Keyword(s):  
Feature Extraction ◽  
High Voltage ◽  
Extraction Method ◽  
Wavelet Packet ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Normal Operation ◽  
Support Vector ◽  
Feature Extraction Method ◽  
High Voltage Circuit Breaker

The development of power grid system not only increases voltage and capacity, but also increases power risk. This paper briefly introduces the feature extraction method of the vibration signal of high voltage circuit breaker and support vector machine (SVM) algorithm and then analyzed the high voltage circuit breaker in three states: normal operation, fixed screw loosening and falling of opening spring, using the SVM based on the above feature extraction method. The results showed that the accuracy and precision rates of fault identification of circuit breaker were the highest by using the wavelet packet energy entropy extraction features, the false alarm rate was the lowest, and the detection time was the shortest.

scholarly journals The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jun Ma ◽  
Jiande Wu ◽  
Yugang Fan ◽  
Xiaodong Wang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Dominant Frequency ◽  
Working Process ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Common Time ◽  
Local Mean ◽  
Fault Feature Extraction

Since the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault feature extraction method for the rolling bearing based on the local mean decomposition (LMD) and envelope demodulation is proposed. Firstly, decompose the original vibration signal by LMD to get a series of production functions (PFs). Then dispose the envelope demodulation analysis on PF component. Finally, perform Fourier Transform on the demodulation signals and judge failure condition according to the dominant frequency of the spectrum. The results show that the proposed method can correctly extract the fault characteristics to diagnose faults.

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