scholarly journals A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 128
Author(s):  
Chenbo Xi ◽  
Guangyou Yang ◽  
Lang Liu ◽  
Hongyuan Jiang ◽  
Xuehai Chen
Keyword(s):  
Fault Diagnosis ◽  
Single Channel ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Support Vector ◽  
Data Set ◽  
Rotary Machine ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Low Sensitivity

In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

scholarly journals A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3105 ◽  
Author(s):  
Cong Dai Nguyen ◽  
Alexander Prosvirin ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Reference Signal ◽  
Vibration Signal ◽  
Support Vector ◽  
Health States ◽  
Informative Signal ◽  
Vibration Signals ◽  
Optimal Output ◽  
Gear Fault ◽  
Diagnosis Method

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

scholarly journals Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

2016 ◽  
Vol 8 (12) ◽  
pp. 168781401668308 ◽  
Author(s):  
Shuangyuan Wang ◽  
Yixiang Huang ◽  
Liang Gong ◽  
Lin Li ◽  
Chengliang Liu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Sparse Coding ◽  
Extraction Procedure ◽  
Vibration Signal ◽  
Support Vector ◽  
Data Set ◽  
Vibration Signals ◽  
Efficiency And Effectiveness ◽  
Gear Fault

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

scholarly journals Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion Entropy

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Cheng Gu ◽  
Xin-Yong Qiao ◽  
Huaying Li ◽  
Ying Jin
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Feature Vector ◽  
Single Channel ◽  
Vibration Signal ◽  
Fault Classification ◽  
Support Vector ◽  
Reconstructed Signal ◽  
Diagnosis Method ◽  
The Imf

As a main source of power, diesel engines are widely used in large mechanical systems. Fire failure is a kind of common fault condition, which seriously affects the power and economy of the diesel engine. Previously, scholars mostly used single-channel signal to diagnose the misfire fault of the diesel engine. However, the single-channel signal has limitations in reflecting the information of fault. A novel fault diagnosis method based on MEMD and dispersion entropy is proposed in this paper. Firstly, the multichannel vibration signal of the diesel engine cylinder head is decomposed by multivariate empirical mode decomposition (MEMD), which obtains the IMF component groups with the same frequency in the same order. Then, the IMF component with a large correlation coefficient with the original signal in each group is selected to reconstruct new signal, and dispersion entropy (DE) of the reconstructed signal is calculated as a fault feature vector. Finally, the fault feature vector is input into the support vector machine (SVM) for misfire fault classification. Compared with the other three methods, the results show that the diagnosis method proposed in this paper can effectively extract the fault features and accurately identify the fault type, which is superior to the comparison method.

scholarly journals Fault diagnosis method of rolling bearings Based on SPA-FE-IFSVM

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096947
Author(s):  
Hui Han ◽  
Lina Hao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fuzzy Entropy ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Rolling Bearings ◽  
Data Set ◽  
Diagnosis Method

Rolling bearings are the most frequently failed components in rotating machinery. Once a failure occurs, the entire system will be shut down or even cause catastrophic consequences. Therefore, a fault detection of rolling bearings is of great significance. Due to the complexity of the mechanical system, the randomness of the vibration signal appears on different scales. Based on the multi-scale fuzzy entropy (FE) analysis of the vibration signal, a rolling bearing fault diagnosis method based on smoothness priors approach (SPA) -FE-IFSVM is proposed. The SPA method was used to adaptively decompose the vibration signal and obtain the trend item and de-trend item of the vibration signal. Then the fuzzy entropy of the trend item and de-trend item was calculated respectively. Meanwhile, aiming at the problem that the support vector machine (SVM) cannot process the data set containing fuzzy messages and was sensitive to noise, the fuzzy support vector machine (FSVM) was introduced and improved, and then the FE as the feature vector was input into the improved fuzzy support vector machine (IFSVM) to identify the failure. The method was applied to the rolling bearing experimental data. The analysis results show that: this method can achieve 100% fault diagnosis accuracy when only two component features are extracted, which can effectively realize the fault diagnosis of rolling bearings.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 98
Author(s):  
Haodong Yuan ◽  
Nailong Wu ◽  
Xinyuan Chen ◽  
Yueying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Training Samples ◽  
Diagnosis Method ◽  
Fault Feature Extraction

The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dictionary learned by the training samples, including thedifferent classes, shift invariant sparse feature for the training as well as test samples can be formed through sparse codes and employed as the input of classifier. A support vector machine (SVM) with optimized parameters has been extensively used in intelligent diagnosis of machinery fault. Hence, in this study, a novel fault diagnosis method of rolling bearings using shift invariant sparse feature and optimized SVM is proposed. Firstly, dictionary learning by shift invariant K-SVD algorithm is conducted. Then, shift invariant sparse feature is constructed with the learned over-complete dictionary. Finally, optimized SVM is employed for classification of the shift invariant sparse feature corresponding to different classes, hence, bearing fault diagnosis is achieved. With regard to the optimized SVM, three methods including grid search, generic algorithm (GA), and particle swarm optimization (PSO) are respectively carried out. The experiment results show that the shift invariant sparse feature using shift invariant K-SVD can effectively distinguish the bearing vibration signals corresponding to different running states. Moreover, optimized SVM can significantly improve the diagnosis precision.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Gear Fault Diagnosis Methods Based on EMD and HMM

2013 ◽  
Vol 333-335 ◽  
pp. 1684-1687
Author(s):  
Bin Wu ◽  
Song He Zhang ◽  
Yue Gang Luo ◽  
Shan Ping Yu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Modulation Amplitude ◽  
Double Frequency ◽  
Gear Mesh ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Modulation Signal ◽  
Gear Fault Diagnosis ◽  
Modulation Sideband

Due to the feature and the forms of motion of the gears, the vibration signal of the gear is mainly the frequency modulation, amplitude modulation, or hybrid modulation signal corresponding to the gear-mesh frequency and its double frequency signal. When faults arise on the gears, the number and shape of the modulation sideband will be changed. The structures and forms of the FM composition differ according to the type of faults. According to the above mentioned characteristic, this essay raises a method to disassemble the gear vibrate signal, points out the formulas to build up characteristic vector, on that basis, the essay raised a gear fault diagnosis method based on EMD and Hidden Markov Model (HMM), this method can identify the working condition of the normal gears, snaggletooth gears, and pitting gears.

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

2021 ◽  
Author(s):  
Hao DeChen ◽  
HuaLing Li ◽  
JinYing Huang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Network Structure ◽  
Rotating Machinery ◽  
Variable Speed ◽  
Data Set ◽  
Public Data ◽  
Diagnosis Method ◽  
Diagnosis Model

Abstract Rotating machinery (RM) is one of the most common mechanical equipment in engineering applications and has a broad and vital role. Rotating machinery includes gearboxes, bearing motors, generators, etc. In industrial production, the important position of rotating machinery and its variable speed and complex working conditions lead to unstable vibration characteristics, which have become a research hotspot in mechanical fault diagnosis. Aiming at the multi-classification problem of rotating machinery with variable speed and complex working conditions, this paper proposes a fault diagnosis method based on the construction of improved sensitive mode matrix (ISMM), isometric mapping (ISOMAP) and Convolution-Vision Transformer network (CvT) structure. After overlapping and sampling the variable speed signals, a high-dimensional ISMM is constructed, and the ISMM is mapped into the manifold space through ISOMAP manifold learning. This method can extract the fault transient characteristics of the variable speed signal, and the experiment proves that it can solve the problem that the conventional method cannot effectively extract the characteristics of the variable speed data. CvT combines the advantages of self-attention mechanism and convolution in CNN, so the CvT network structure is used for feature extraction and fault recognition and classification. The CvT network structure takes into account both global feature extraction and local feature extraction, which greatly reduces the number of training iterations and the size of the network model. Two data sets (the HFXZ-I planetary gearbox variable speed data set in the laboratory and the bearing variable speed public data set of the University of Ottawa in Canada) are used to experimentally verify the proposed fault diagnosis model. Experimental results show that the proposed fault diagnosis model has good recognition accuracy and robustness.

Rotor Crack Detection Based on Multi-Vibration Signal Fusion Collected from the Basement of Machinery Using SVM and Statistical Characteristics Methodology

2010 ◽  
Vol 34-35 ◽  
pp. 1000-1004
Author(s):  
Xue Jun Li ◽  
K. Wang ◽  
Ling Li Jiang ◽  
T. Zhang
Keyword(s):  
Fault Diagnosis ◽  
Crack Detection ◽  
Feature Fusion ◽  
Vibration Signal ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Signal Acquisition ◽  
Multiple Sensors ◽  
Signal Fusion ◽  
Diagnosis Method

As the poor generability of special sensor support frame and the inconvenience of signal acquisition in the process of common fault diagnosis for cracked rotor, a new fault diagnosis method is presented in this paper. this method takes the basement of rotor test rig as the monitoring objects and makes feature fusion for time-domain statistics of multiple sensors using SVM (support vector machine). The result of experiment showed that the method using the multi-sensor signal fusion technology collected from the basement of machinery has the advantages of better diagnostic precision for rotor crack diagnosis, furthermore, it supplies a new way for rotor fault diagnosis.

scholarly journals Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Na Lu ◽  
Guangtao Zhang ◽  
Yuanchu Cheng ◽  
Diyi Chen
Keyword(s):  
Fault Diagnosis ◽  
Second Generation ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Support Vector ◽  
Signal Denoising ◽  
Denoising Method ◽  
Machine Method ◽  
Second Generation Wavelet ◽  
Machinery Fault Diagnosis

Vibration signal of rotating machinery is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. In order to improve the denoising effect of the vibration signal, an adaptive redundant second-generation wavelet (ARSGW) denoising method is proposed. In this method, a new index for denoising result evaluation (IDRE) is constructed first. Then, the maximum value of IDRE and the genetic algorithm are taken as the optimization objective and the optimization algorithm, respectively, to search for the optimal parameters of the ARSGW. The obtained optimal redundant second-generation wavelet (RSGW) is used for vibration signal denoising. After that, features are extracted from the denoised signal and then input into the support vector machine method for fault recognition. The application result indicates that the proposed ARSGW denoising method can effectively improve the accuracy of rotating machinery fault diagnosis.

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