scholarly journals Research on the Sparse Representation for Gearbox Compound Fault Features Using Wavelet Bases

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Chunyan Luo ◽  
Changqing Shen ◽  
Wei Fan ◽  
Gaigai Cai ◽  
Weiguo Huang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Background Noise ◽  
Practical Application ◽  
Impulse Responses ◽  
Wavelet Bases ◽  
Potential Approach ◽  
Fault Features ◽  
Engineering Practices

The research on gearbox fault diagnosis has been gaining increasing attention in recent years, especially on single fault diagnosis. In engineering practices, there is always more than one fault in the gearbox, which is demonstrated as compound fault. Hence, it is equally important for gearbox compound fault diagnosis. Both bearing and gear faults in the gearbox tend to result in different kinds of transient impulse responses in the captured signal and thus it is necessary to propose a potential approach for compound fault diagnosis. Sparse representation is one of the effective methods for feature extraction from strong background noise. Therefore, sparse representation under wavelet bases for compound fault features extraction is developed in this paper. With the proposed method, the different transient features of both bearing and gear can be separated and extracted. Both the simulated study and the practical application in the gearbox with compound fault verify the effectiveness of the proposed method.

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 Sparse Representation of Transients Based on Wavelet Basis and Majorization-Minimization Algorithm for Machinery Fault Diagnosis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Fan ◽  
Gaigai Cai ◽  
Weiguo Huang ◽  
Li Shang ◽  
Zhongkui Zhu
Keyword(s):  
Fault Diagnosis ◽  
Sparse Representation ◽  
Background Noise ◽  
Augmented Lagrangian ◽  
Signal To Noise Ratio ◽  
Wavelet Basis ◽  
Mm Algorithm ◽  
Novel Method ◽  
Machinery Fault Diagnosis ◽  
Detection Effect

Vibration signals captured from faulty mechanical components are often associated with transients which are significant for machinery fault diagnosis. However, the existence of strong background noise makes the detection of transients a basis pursuit denoising (BPD) problem, which is hard to be solved in explicit form. With sparse representation theory, this paper proposes a novel method for machinery fault diagnosis by combining the wavelet basis and majorization-minimization (MM) algorithm. This method converts transients hidden in the noisy signal into sparse coefficients; thus the transients can be detected sparsely. Simulated study concerning cyclic transient signals with different signal-to-noise ratio (SNR) shows that the effectiveness of this method. The comparison in the simulated study shows that the proposed method outperforms the method based on split augmented Lagrangian shrinkage algorithm (SALSA) in convergence and detection effect. Application in defective gearbox fault diagnosis shows the fault feature of gearbox can be sparsely and effectively detected. A further comparison between this method and the method based on SALSA shows the superiority of the proposed method in machinery fault diagnosis.

scholarly journals Bearing Intelligent Fault Diagnosis Based on Convolutional Neural Networks

2022 ◽  
Vol 16 ◽  
pp. 470-477
Author(s):  
Jing An ◽  
Peng An
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Fault Location ◽  
Extraction Process ◽  
Operating Conditions ◽  
Intelligent Fault Diagnosis ◽  
Fault Features ◽  
Benchmark Datasets ◽  
Diagnosis Accuracy ◽  
Bearing Vibration

The traditional intelligent identification method requires a complex feature extraction process and much diagnosis experience, considering the characteristics of one dimension of bearing vibration signals, a new method of intelligent fault diagnosis based on 1-dimensional convolutional neural network is presented. This method automatically extracts features from frequency domain signals and avoids artificial feature selection and feature extraction. The proposed method is validated on bearing benchmark datasets, these datasets are collected in different fault location, different health conditions and different operating conditions. The result shows that the proposed method can not only adaptively obtain representative fault features from the datasets, but also achieve higher diagnosis accuracy than the existing methods.

A clustering K-SVD-based sparse representation method for rolling bearing fault diagnosis

2021 ◽  
Vol 63 (3) ◽  
pp. 160-167
Author(s):  
Qingwen Yu ◽  
Jimeng Li ◽  
Zhixin Li ◽  
Jinfeng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Sparse Representation ◽  
Rolling Bearing ◽  
Vibration Signals ◽  
Redundant Dictionary ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Representation Method ◽  
The Impact

It is challenging to extract weak impulse features from vibration signals corrupted by strong noise in mechanical fault diagnosis. Due to its simple calculation, fast convergence and easy implementation, K-singular value decomposition (K-SVD) has been widely used in rolling bearing fault diagnosis. However, it fails to consider the influence of noise and harmonics on atoms learning from impulse characteristics, which results in many irrelevant atoms, and then increases the difficulty of extracting the impulse features in bearing fault signals. Therefore, a clustering K-SVD-based sparse representation method is proposed in this paper and it is combined with the particle swarm optimisation (PSO)-based time-varying filter empirical mode decomposition (TVF-EMD) for rolling bearing fault diagnosis. The PSO-based TVF-EMD is developed to automatically decompose the original signal to eliminate the impact of noise and harmonics on the impulses in the signal. Then, the clustering K-SVD method is applied to perform dictionary learning on the sensitive component containing impulses to obtain a redundant dictionary of atoms with obvious impulse patterns. Finally, the orthogonal matching pursuit (OMP) algorithm is introduced to extract the fault features from rolling bearing vibration signals. The experimental results show that the proposed method can improve the robustness of the dictionary atoms to noise and achieve the extraction of rolling bearing fault features.

scholarly journals Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Hao Sun ◽  
Ke Li ◽  
Huaqing Wang ◽  
Peng Chen ◽  
Yi Cao
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Background Noise ◽  
Particle Swarm ◽  
Adaptive Threshold ◽  
Rotating Machinery ◽  
Swarm Optimization ◽  
Vibration Signals ◽  
Fault Features ◽  
Condition Diagnosis

The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO) is proposed. Geronimo, Hardin, and Massopust (GHM) multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs) in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI) using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO) algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.

scholarly journals Regularized Deep Clustering Method for Fault Trend Analysis

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Yongzhi Qu ◽  
Yue Zhang ◽  
David He ◽  
Miao He ◽  
Dude Zhou
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Linear Trend ◽  
Optimization Method ◽  
External Noise ◽  
Cluster Center ◽  
Health States ◽  
Fault Features ◽  
Feature Optimization

Effective fault feature extraction is the key of fault diagnosis. In previous works, it is shown that some embedding methods and unsupervised deep learning methods have the ability to extract fault features from raw signals directly, such as PCA and deep autoencoder. Particularly, deep autoencoder has been shown in relevant research that it caneffectively extract the hidden ‘trend’ associated with machinery health states which can be useddirectly for online anomaly detection and prediction. However, in practical online fault diagnosis, the discrimination between successive signals is small due to the slow degradation progress and the external noise. Therefore,it is important to optimize the feature extraction process to achieve better online fault tracking. In this paper, a regularized deep clustering algorithm is proposed to guide the optimization process of feature extraction which combines embedding method and semi-guided learning. A regularization term for the cluster center points is proposed to make the feature optimization convergein a monotonic linear trend. In order to verify the effectiveness of the method, an accelerated gearbox run-to-failure experiment is carried out. The result shows that the feature optimization method can optimize the fault features on the basis of the deep autoencoder algorithm in two aspects: a better distinction of the fault features in short term and a more consistent trend of the gear wear in the long term.

Fault diagnosis of rolling bearings based on multi-scale deep subdomain adaptation network

2022 ◽  
pp. 1-11
Author(s):  
Qin Zhou ◽  
Zuqiang Su ◽  
Lanhui Liu ◽  
Xiaolin Hu ◽  
Jianhang Yu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Receptive Fields ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Fault Features ◽  
Diagnosis Method ◽  
Fault Information

This study presents a fault diagnosis method for rolling bearing based on multi-scale deep subdomain adaptation network (MSDSAN). The proposed MSDSAN, as improvement of deep subdomain adaptation network (DSAN), is an unsupervised transfer learning method. MSDSAN reduces the subdomain distribution discrepancy between domains rather than marginal distribution discrepancy, and so better domain invariant fault features are derived to avoid misalignment between domains. Aiming at avoiding fault information loss by fixed receptive fields feature extraction, selective kernel convolution module is introduced into feature extraction of MSDSAN, by which multiple receptive fields are applied to ensure an optimal receptive field for each working condition. Moreover, contribution rates are adaptively assigned to all receptive fields, and the disturbing information extracted by inappropriate receptive fields is further eliminated. As a result, more comprehensive and effective fault information is derived for bearing fault diagnosis. Fault diagnosis experiment of bearings is performed to verify the superiority of the proposed method, and the experimental results demonstrate that MSDSAN achieves better transfer effects and higher accuracy than SOTA methods under varying working conditions.

scholarly journals Open-Circuit Fault Diagnosis of Three-Phase PWM Rectifier Using Beetle Antennae Search Algorithm Optimized Deep Belief Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1570
Author(s):  
Bolun Du ◽  
Yigang He ◽  
Yaru Zhang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Open Circuit ◽  
Deep Belief Network ◽  
Fault Classification ◽  
Pwm Rectifier ◽  
Belief Network ◽  
Fault Features ◽  
Three Phase ◽  
Circuit Fault Diagnosis

Effective open-circuit fault diagnosis for a two-level three-phase pulse-width modulating (PWM) rectifier can reduce the failure rate and prevent unscheduled shutdown. Nevertheless, traditional signal-based feature extraction methods show poor distinguishability for insufficient fault features. Shallow learning diagnosis models are prone to fall into local extremum, slow convergence speed, and overfitting. In this paper, a novel fault diagnosis strategy based on modified ensemble empirical mode decomposition (MEEMD) and the beetle antennae search (BAS) algorithm optimized deep belief network (DBN) is proposed to cope with these problems. Initially, MEEMD is applied to extract useful fault features from each intrinsic mode function (IMF) component. Meanwhile, to remove features with redundancy and interference, fault features are selected by calculating the importance of each feature based on the extremely randomized trees (ERT) algorithm, and the dimension of fault feature vectors is reduced by principal component analysis. Additionally, the DBN stacked with two layers of a restricted Boltzmann machine (RBM) is selected as the classifier, and the BAS algorithm is used as the optimizer to determine the optimal number of units in the hidden layers of the DBN. The proposed method combined with feature extraction, feature selection, optimization, and fault classification algorithms significantly improves the diagnosis accuracy.

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