A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis

Shock and Vibration ◽

10.1155/2019/1908485 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 6

Author(s):

Lei You ◽

Wenjie Fan ◽

Zongwen Li ◽

Ying Liang ◽

Miao Fang ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Vibration Signal ◽

Rotating Machinery ◽

Fault Classification ◽

Support Vector ◽

Local Optimum ◽

Classification Rate ◽

Wavelet Energy ◽

Diagnosis Model

Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time-spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog-leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA-SVM), and SFLA-SVM. The experiment results show that MSFLA-SVM achieves a much higher fault classification rate than BPNN, ACROA-SVM, and SFLA-SVM.

Fault Classification Model of Rotor Based on Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.66-68.1982 ◽

2011 ◽

Vol 66-68 ◽

pp. 1982-1987

Author(s):

Wei Niu ◽

Guo Qing Wang ◽

Zheng Jun Zhai ◽

Juan Cheng

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Classification Model ◽

Fault Classification ◽

Support Vector ◽

Vibration Signals ◽

Good Classification ◽

Speed Change ◽

Diagnosis Model

The vibration signals of rotating machinery in operation consist of plenty of information about its running condition, and extraction and identification of fault signals in the process of speed change are necessary for the fault diagnosis of rotating machinery. This paper improves DDAG classification method and proposes a new fault diagnosis model based on support vector machine to solve the problem of restricting the rotating machinery fault intelligent diagnosis due to the lack of fault data samples. The testing results demonstrate that the model has good classification precision and can correctly diagnose faults.

Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽

10.3390/pr9060919 ◽

2021 ◽

Vol 9 (6) ◽

pp. 919

Author(s):

Wanlu Jiang ◽

Chenyang Wang ◽

Jiayun Zou ◽

Shuqing Zhang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Generative Adversarial Networks ◽

Extraction Ability ◽

One Dimensional ◽

Adversarial Networks ◽

Diagnosis Model ◽

The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

A Fault Diagnosis Method of Rolling Bearing Integrated with Cooperative Energy Feature Extraction and Improved Least-Squares Support Vector Machine

Mathematical Problems in Engineering ◽

10.1155/2020/6643167 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Zhang Xu ◽

Darong Huang ◽

Tang Min ◽

Yunhui Ou

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Fault Diagnosis ◽

Working Conditions ◽

Rolling Bearing ◽

Support Vector ◽

Swarm Optimization ◽

Energy Characteristics ◽

Wavelet Energy ◽

Energy Feature

To solve the problem that the bearing fault of variable working conditions is challenging to identify and classify in the industrial field, this paper proposes a new method based on optimization of multidimension fault energy characteristics and integrates with an improved least-squares support vector machine (LSSVM). First, because the traditional wavelet energy feature is difficult to effectively reflect the characteristics of rolling bearing under different working conditions, based on analyzing the wavelet energy feature extraction in detail, a collaborative method of multidimension fault energy feature extraction combined with the method of Transfer Component Analysis (TCA) is constructed, which improves the discrimination between the different features and the compactness between the same features of rolling bearing faults. Then, for solving the problem of the local optimal of particle swarm optimization (PSO) in fault diagnosis and recognition of rolling bearing, an improved LSSVM based on particle swarm optimization and wavelet mutation optimization is established to realize the collaborative optimization and adjustment of LSSVM dynamic parameters. Based on the improved LSSVM and optimization of multidimensional energy characteristics, a new method for fault diagnosis of rolling bearing is designed. Finally, the simulation and analysis of the proposed algorithm are verified by the experimental data of different working conditions. The experimental results show that this method can effectively extract the multidimensional fault characteristics under variable working conditions and has a high fault recognition rate.

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

10.21203/rs.3.rs-940146/v1 ◽

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.

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Energies ◽

10.3390/en12214170 ◽

2019 ◽

Vol 12 (21) ◽

pp. 4170 ◽

Cited By ~ 5

Author(s):

Bing Zeng ◽

Jiang Guo ◽

Wenqiang Zhu ◽

Zhihuai Xiao ◽

Fang Yuan ◽

...

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Least Square ◽

Ratio Method ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Local Optimum ◽

Grey Wolf ◽

Diagnosis Model ◽

Transformer Fault

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

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

Mathematical Problems in Engineering ◽

10.1155/2016/2727684 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

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.

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

Advances in Mechanical Engineering ◽

10.1177/1687814016683085 ◽

2016 ◽

Vol 8 (12) ◽

pp. 168781401668308 ◽

Cited By ~ 1

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.

Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

Journal of Sensors ◽

10.1155/2017/8092691 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 15

Author(s):

Xiaochen Zhang ◽

Dongxiang Jiang ◽

Te Han ◽

Nanfei Wang ◽

Wenguang Yang ◽

...

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Clustering Algorithm ◽

Imbalanced Data ◽

Principal Component ◽

Rotating Machinery ◽

Support Vector ◽

Data Set ◽

Diagnosis Model ◽

Sample Set

To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm

Entropy ◽

10.3390/e20090626 ◽

2018 ◽

Vol 20 (9) ◽

pp. 626 ◽

Cited By ~ 13

Author(s):

Wenlong Fu ◽

Jiawen Tan ◽

Chaoshun Li ◽

Zubing Zou ◽

Qiankun Li ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Production Efficiency ◽

Economic Benefits ◽

Rotating Machinery ◽

Permutation Entropy ◽

Support Vector ◽

Industrial Manufacture ◽

Sine Cosine Algorithm ◽

Diagnosis Approach

As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.

Rolling bearing fault diagnosis of PSO–LSSVM based on CEEMD entropy fusion

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2019-0114 ◽

2020 ◽

Vol 44 (3) ◽

pp. 405-418

Author(s):

Shuzhi Gao ◽

Tianchi Li ◽

Yimin Zhang

Keyword(s):

Fault Diagnosis ◽

Roller Bearing ◽

Vibration Signal ◽

Rolling Bearing ◽

Fault Classification ◽

Kernel Principal Component Analysis ◽

Permutation Entropy ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Taking aim at the nonstationary nonlinearity of the rolling bearing vibration signal, a rolling bearing fault diagnosis method based on the entropy fusion feature of complementary ensemble empirical mode decomposition (CEEMD) is proposed in combination with information fusion theory. First, CEEMD of the vibration signal of the rolling bearing is performed. Then the signal is decomposed into the sum of several intrinsic mode functions (IMFs), and the singular entropy, energy entropy, and permutation entropy are obtained for the IMFs with fault features. Second, the feature extraction method of entropy fusion is proposed, and the three entropy data obtained are input into kernel principal component analysis (KPCA) for feature fusion and dimensionality reduction to obtain complementary features. Finally, the extracted features are imported into the particle swarm optimization (PSO) algorithm to optimize the least-squares support vector machine (LSSVM) for fault classification. Through experimental verification, the proposed method can be used for roller bearing fault diagnosis.