A Fault Diagnosis Method Based on EEMD and Statistical Distance Analysis

Serious vibration or wear with large friction usually appear when faults occur, which leads to more serious faults such as the destruction of the oil film, bringing great damages to both the society and economic sector. Therefore, the accurate diagnosis of a fault in the early stage is important for the safety operation of machinery. To effectively extract the fault features for diagnosis, EMD-based methods are widely used. However, these methods spend lots of efforts diagnosing faults and require plenty of professional knowledge of diagnosis. Although many intelligent classifiers can be used to automatically diagnose faults such as wear, a broken tooth and imbalance, the combing EMD-based method, the scarcity of samplings with labels hinder the application of these methods to engineering. It is because the model of the intelligent classifier must be constructed based on sufficient samplings with a label. To solve this problem, we propose a novel fault diagnosis method, which is performed based on the EEMD and statistical distance analysis. In this method, the EEMD is used to decompose one original signal into several IMFs and then the probability density distribution of each IMF is calculated. To diagnose the fault of the machinery, the Euclidean distance between the signal acquired under an unknown fault with the other referenced signals acquired previously under various fault types is calculated. At last, the fault of the signal is the same with the referenced signal when the distance is the smallest. To verify the effectiveness of our proposed method, a dataset of bearings with different faults, and a dataset of 2009 Prognostics and Health Management (PHM) data challenge, including gear, bearing and shaft faults are used. The result shows that the proposed method can not only automatically diagnose faults effectively, but also fewer samplings with a label are used compared with the intelligent methods.

Download Full-text

An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE

Sensors ◽

10.3390/s19051041 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1041 ◽

Cited By ~ 6

Author(s):

Yang Liu ◽

Lixiang Duan ◽

Zhuang Yuan ◽

Ning Wang ◽

Jianping Zhao

Keyword(s):

Fault Diagnosis ◽

Health Management ◽

Vibration Signal ◽

Intelligent Fault Diagnosis ◽

Traditional Methods ◽

Learning Abilities ◽

Fault Features ◽

Correlation Criterion ◽

Long Time ◽

Diagnosis Method

The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.

Download Full-text

The Application of Improved SVM in Mixed Circuit

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.224.493 ◽

2012 ◽

Vol 224 ◽

pp. 493-496 ◽

Cited By ~ 1

Author(s):

Huai Long Wang ◽

Qiang Pan ◽

Hong Liu

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Small Samples ◽

Lifting Wavelet Transform ◽

Fault Features ◽

Diagnosis Method ◽

Lifting Wavelet ◽

Current Characteristics ◽

Mixed Circuit ◽

Better Than

In order to improve the speed and the rate of fault diagnosis in mixed circuit, this paper introduces a new fault diagnosis method. Through extracting fault features of current characteristics effectively and applying to Improved SVM, the ability of pattern recognition will be better than the traditional BP Neural Network and Single SVM, especially in small samples or non-linear cases. Meanwhile, this paper presents the lifting wavelet transform in order to obtain the feature information accurately. The accuracy of fault diagnosis can greatly enhance by discussing the Improved SVM combined with lifting wavelet transform in a specific monostable trigger. That points out a new direction for the fault diagnosis of mixed circuit.

Download Full-text

Application of a Novel Adaptive Med Fault Diagnosis Method in Gearboxes

Entropy ◽

10.3390/e21111106 ◽

2019 ◽

Vol 21 (11) ◽

pp. 1106

Author(s):

Wenhua Du ◽

Xiaoming Guo ◽

Xiaofeng Han ◽

Junyuan Wang ◽

Jie Zhou ◽

...

Keyword(s):

Fault Diagnosis ◽

Fitness Function ◽

Vibration Signal ◽

Ar Model ◽

Fluctuation Analysis ◽

Minimum Entropy ◽

Fault Features ◽

Filter Size ◽

Diagnosis Method ◽

Reduction Effect

Minimum entropy deconvolution (MED) is not effective in extracting fault features in strong noise environments, which can easily lead to misdiagnosis. Moreover, the noise reduction effect of MED is affected by the size of the filter. In the face of different vibration signals, the size of the filter is not adaptive. In order to improve the efficiency of MED fault feature extraction, this paper proposes a firefly optimization algorithm (FA) to improve the MED fault diagnosis method. Firstly, the original vibration signal is stratified by white noise-assisted singular spectral decomposition (SSD), and the stratified signal components are divided into residual signal components and noisy signal components by a detrended fluctuation analysis (DFA) algorithm. Then, the noisy components are preprocessed by an autoregressive (AR) model. Secondly, the envelope spectral entropy is proposed as the fitness function of the FA algorithm, and the filter size of MED is optimized by the FA algorithm. Finally, the preprocessed signal is denoised and the pulse enhanced with the proposed adaptive MED. The new method is validated by simulation experiments and practical engineering cases. The application results show that this method improves the shortcomings of MED and can extract fault features more effectively than the traditional MED method.

Download Full-text

A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography

Complexity ◽

10.1155/2019/2619252 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Yongbo Li ◽

Xianzhi Wang ◽

Shubin Si ◽

Xiaoqiang Du

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Infrared Thermography ◽

Rotating Machinery ◽

Support Vector ◽

Intelligent Fault Diagnosis ◽

Fault Features ◽

Diagnosis Method ◽

Systematic Framework ◽

Machinery Fault Diagnosis

A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.

Download Full-text

A Novel Bearing Fault Diagnosis Method Based on GL-mRMR-SVM

Processes ◽

10.3390/pr8070784 ◽

2020 ◽

Vol 8 (7) ◽

pp. 784

Author(s):

Xianghong Tang ◽

Qiang He ◽

Xin Gu ◽

Chuanjiang Li ◽

Huan Zhang ◽

...

Keyword(s):

Fault Diagnosis ◽

Feature Fusion ◽

Global Features ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Fault Recognition ◽

Fault Features ◽

Svm Model ◽

Diagnosis Method ◽

The Time Domain

A convolutional neural network (CNN) has been used to successfully realize end-to-end bearing fault diagnosis due to its powerful feature extraction ability. However, the CNN is prone to focus on local information, ignoring the relationship between the whole and the part of the signal due to its unique structure. In addition, it extracts some fault features with poor robustness under noisy environment. A novel diagnosis model based on feature fusion and feature selection, GL-mRMR-SVM, is proposed to address this problem in this paper. First, the model combines the global features in the time-domain and frequency-domain of the raw data with the local features extracted by CNN to make full use of the signal information and overcome the weakness of traditional CNNs neglecting the overall signal. Then, the max-relevance min-redundancy (mRMR) algorithm is used to automatically extract the discriminative features from the fused features without any prior knowledge. Finally, the extracted discriminative features are input into the SVM for training and output the fault recognition results. The proposed GL-mRMR-SVM model was evaluated through experiments on bearing data of Case Western Reserve University (CWRU) and CUT-2 platform. The experimental results show that the proposed method is more effective than other intelligent diagnosis methods.

Download Full-text

Design of Fault Diagnosis Algorithm for Electromechanical System Based on Artificial Intelligence

Journal of Physics Conference Series ◽

10.1088/1742-6596/2050/1/012011 ◽

2021 ◽

Vol 2050 (1) ◽

pp. 012011

Author(s):

Fuyou Zhao ◽

Mingying Huo ◽

Naiming Qi ◽

Lianfeng Li ◽

Weiwei Cui

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Deep Learning ◽

Fault Diagnosis ◽

Data Sets ◽

Learning Technology ◽

Electrical System ◽

Fault Recognition ◽

Fault Features ◽

Diagnosis Method

Abstract A relatively perfect system for the fault diagnosis of mechanical and electrical products has been formed through decades of development. Nevertheless, the traditional fault diagnosis methods fail to cope with the gradual huge mechanical and electrical system. As a result, the advantages of fault diagnosis mode driven by data are increasingly prominent. Meanwhile, the effect of fault diagnosis has exceeded the traditional fault diagnosis methods in many fields. Through the use of the deep learning technology based on artificial intelligence, it carries out mapping and fitting. By fully taking advantages of neural network, it can effectively obtain the accurate classification of fault data. A fault diagnosis method based on the fault data of mechanical and electrical system is designed in this thesis. When it comes to the basic process, it is to take data sets for different mechanical and electrical products. Through the use of feature engineering method, it extracts the fault features of data. Through the use of deep learning technology, it carries out the intelligent diagnosis. According to the experimental results, it indicates that the fault diagnosis method based on deep learning technology can distinguish a variety of fault modes in mechanical and electrical system in an effective way. What’s more, good classification results in fault recognition have been achieved by a variety of deep convolutional neural network structures, so the feasibility of the method is further verified.

Download Full-text

An Intelligent Quadrotor Fault Diagnosis Method Based on Novel Deep Residual Shrinkage Network

Drones ◽

10.3390/drones5040133 ◽

2021 ◽

Vol 5 (4) ◽

pp. 133

Author(s):

Pu Yang ◽

Huilin Geng ◽

Chenwan Wen ◽

Peng Liu

Keyword(s):

Fault Diagnosis ◽

Window Method ◽

Signal Features ◽

Fault Features ◽

Diagnosis Method ◽

Diagnosis Accuracy ◽

Sliding Window Method ◽

Diagnosis Algorithm ◽

Soft Threshold ◽

Fitting Ability

In this paper, a fault diagnosis algorithm named improved one-dimensional deep residual shrinkage network with a wide convolutional layer (1D-WIDRSN) is proposed for quadrotor propellers with minor damage, which can effectively identify the fault classes of quadrotor under interference information, and without additional denoising procedures. In a word, that fault diagnosis algorithm can locate and diagnose the early minor faults of the quadrotor based on the flight data, so that the quadrotor can be repaired before serious faults occur, so as to prolong the service life of quadrotor. First, the sliding window method is used to expand the number of samples. Then, a novel progressive semi-soft threshold is proposed to replace the soft threshold in the deep residual shrinkage network (DRSN), so the noise of signal features can be eliminated more effectively. Finally, based on the deep residual shrinkage network, the wide convolution layer and DroupBlock method are introduced to further enhance the anti-noise and over-fitting ability of the model, thus the model can effectively extract fault features and classify faults. Experimental results show that 1D-WIDRSN applied to the minimal fault diagnosis model of quadrotor propellers can accurately identify the fault category in the interference information, and the diagnosis accuracy is over 98%.

Download Full-text

Fault Diagnosis Method for Rotating Machinery Based on Hierarchical Amplitude-Aware Permutation Entropy and Pairwise Feature Proximity

Shock and Vibration ◽

10.1155/2021/4395500 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Ling Shu ◽

Jinxing Shen ◽

Xiaoming Liu

Keyword(s):

Time Series ◽

Fault Diagnosis ◽

Low Frequency ◽

Rotating Machinery ◽

Permutation Entropy ◽

Support Vector ◽

Feature Vectors ◽

Fault Features ◽

Diagnosis Method ◽

Frequency Features

With a view to solving the defect that multiscale amplitude-aware permutation entropy (MAAPE) can only quantify the low-frequency features of time series and ignore the high-frequency features which are equally important, a novel nonlinear time series feature extraction method, hierarchical amplitude-aware permutation entropy (HAAPE), is proposed. By constructing high and low-frequency operators, this method can extract the features of different frequency bands of time series simultaneously, so as to avoid the issue of information loss. In view of its advantages, HAAPE is introduced into the field of fault diagnosis to extract fault features from vibration signals of rotating machinery. Combined with the pairwise feature proximity (PWFP) feature selection method and gray wolf algorithm optimization support vector machine (GWO-SVM), a new intelligent fault diagnosis method for rotating machinery is proposed. In our method, firstly, HAPPE is adopted to extract the original high and low-frequency fault features of rotating machinery. After that, PWFP is used to sort the original features, and the important features are filtered to obtain low-dimensional sensitive feature vectors. Finally, the sensitive feature vectors are input into GWO-SVM for training and testing, so as to realize the fault identification of rotating machinery. The performance of the proposed method is verified using two data sets of bearing and gearbox. The results show that the proposed method enjoys obvious advantages over the existing methods, and the identification accuracy reaches 100%.

Download Full-text

A Multi-Stage Fault Diagnosis Method for Proton Exchange Membrane Fuel Cell Based on Support Vector Machine with Binary Tree

Energies ◽

10.3390/en14206526 ◽

2021 ◽

Vol 14 (20) ◽

pp. 6526

Author(s):

Jiaping Xie ◽

Chao Wang ◽

Wei Zhu ◽

Hao Yuan

Keyword(s):

Fault Diagnosis ◽

Binary Tree ◽

Proton Exchange Membrane ◽

Pem Fuel Cells ◽

Proton Exchange ◽

Support Vector ◽

Multi Stage ◽

Fault Features ◽

Diagnosis Method ◽

Exchange Membrane

The reliability and durability of the proton exchange membrane (PEM) fuel cells are vital factors restricting their applications. Therefore, establishing an online fault diagnosis system is of great significance. In this paper, a multi-stage fault diagnosis method for the PEM fuel cell is proposed. First, the tests of electrochemical impedance spectroscopy under various fault conditions are conducted. Specifically, prone recoverable faults, such as flooding, membrane drying, and air starvation, are included, and different fault degrees from minor, moderate to severe, are covered. Based on this, an equivalent circuit model (ECM) is selected to fit impedance spectroscopy by the hybrid genetic particle swarm optimization algorithm, and then fault features are determined by the analysis of each model parameter under different fault conditions. Furthermore, a multi-stage fault diagnosis model is constructed with the support vector machine with the binary tree, in which fault features obtained from the ECM are used as the characteristic inputs to realize the fault classification (including fault type and fault degree) online. The results show that the accuracy of the basic fault test and subdivided fault test can reach 100% and 98.3%, respectively, which indicates that the proposed diagnosis method can effectively identify flooding, drying, and air starvation of PEM fuel cells.

Download Full-text

A method for bearing fault diagnosis of mine hoist using convolutional attention autoencoder

10.21203/rs.3.rs-678821/v1 ◽

2021 ◽

Author(s):

Jie Zhang ◽

Ke Yang ◽

Yuanyuan Jiang ◽

Ling Xia

Keyword(s):

Fault Diagnosis ◽

Input Signal ◽

Experimental Validation ◽

Failure Modes ◽

Noisy Environment ◽

Bearing Fault Diagnosis ◽

Fault Features ◽

Diagnosis Method ◽

Actual Operation ◽

Mine Hoist

Abstract In view of the complex environment and frequent faults in the actual operation of mine hoist, a fault diagnosis method based on Convolution Attention Autoencoder (CAAE) is proposed through theoretical analysis and experimental verification to improve the diagnostic stability of mine hoist under strong noise. First, a CAAE is constructed, which uses a combination of a convolutional neural network (CNN) and a channel attention module (CAM) to compress and encode the input signal, and then the input signal is reconstructed by a decoder to train the CAAE to extract the original signal fault features. Then, a fault diagnosis classifier is constructed to classify different fault patterns. Finally, experimental validation is performed with the Case Western Reserve University bearing dataset. The results show that the method has a strong feature extraction capability and a high classification accuracy for bearing failure modes compared with existing methods. And the experiments on the application effect of the proposed method in noisy environment are conducted to verify that the method is highly effective and challenging.

Download Full-text