Induction Motor Fault Diagnosis and Classification Through Sparse Representation

2017 ◽  
Author(s):  
Jianjing Zhang ◽  
Peng Wang ◽  
Chuang Sun ◽  
Ruqiang Yan ◽  
Robert X. Gao
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Induction Motor ◽  
Vibration Signal ◽  
Performance Measure ◽  
Fault Classification ◽  
Human Intervention ◽  
Robustness To Noise ◽  
Fault Characteristic

Condition monitoring and fault diagnosis of induction motor play a critical role in operation safety and production efficiency. In recent study, sparse representation has demonstrated its simplicity in training, robustness to noise and high accuracy in classification. This paper evaluates the effectiveness of sparse representation as an alternative approach to induction motor fault diagnosis with fault classification rate and robustness to noise as performance measure. Aiming at eliminating the human intervention in fault characteristic frequency detection and extensive feature extraction steps in traditional method, the spatial pattern of the vibration signal is studied as the classifier input. The residual sparsity index (RSI) is proposed to quantify the degree of multi-class data separation and evaluate the reliability of classification results. Experimental results show that the sparse representation method using vibration signal achieves high motor multi-fault classification accuracy and good robustness to noise, with no human intervention required for fault characteristic pattern detection and the need for long feature extraction eliminated. Finally, RSI confirms the high overall reliability of classification results.

Intelligent fault diagnosis for rotating machinery using L1/2-SF under variable rotational speed

2020 ◽  
pp. 095440702096462
Author(s):  
Jinrui Wang ◽  
Shanshan Ji ◽  
Baokun Han ◽  
Huaiqian Bao
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotational Speed ◽  
Vibration Signal ◽  
Fault Classification ◽  
Mechanical Transmission ◽  
Intelligent Fault Diagnosis ◽  
Planetary Gearbox ◽  
Non Stationary Signal ◽  
Regularization Strategy

Sparse filtering (SF), as an effective feature extraction technique, has attracted considerable attention in the field of mechanical fault diagnosis. But the generalization ability of SF to handle non-stationary signal under variable rotational speed is still poor. When the rotating parts of mechanical transmission work at a constant speed, the collected vibration signal is strongly correlated with the fault type. However, the mappings will no longer be so simple under the condition of variable rotational speed, which brings a rigorous challenge to intelligent fault diagnosis. To overcome the aforementioned deficiency, a novel L1/2 regularized SF method ( L1/2-SF) is studied in this paper. Specifically, L1/2 regularization strategy is added to the cost function of SF, then the L1/2-SF is directly employed to extract sparse features from the raw vibration data under variable rotational speed condition. In order to understand the sparse feature extraction ability of the L1/2 regularization, a physical explanation of the sparse solution generated by the L1/2 regularization strategy is explored. Next, softmax regression is employed for fault classification connected with the output layer of L1/2-SF. The effectiveness of L1/2-SF method is verified using a planetary gearbox dataset and a bearing dataset, respectively. Experiment results show that L1/2-SF can deal well with the variable rotational speed problem and is superior to other methods.

scholarly journals A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

2021 ◽  
Vol 11 (11) ◽  
pp. 4996
Author(s):  
Gang Yao ◽  
Yunce Wang ◽  
Mohamed Benbouzid ◽  
Mourad Ait-Ahmed
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Optimization Techniques ◽  
Fault Classification ◽  
Vibration Signals ◽  
Kernel Parameter ◽  
Diagnosis Method ◽  
The Impact ◽  
Fault Feature Extraction

In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
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.

scholarly journals Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

2019 ◽  
Vol 9 (8) ◽  
pp. 1696 ◽  
Author(s):  
Wang ◽  
Lee
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Background Noise ◽  
Vibration Signal ◽  
Spectral Subtraction ◽  
Accurate Identification ◽  
Side Band ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

scholarly journals Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7467
Author(s):  
Shih-Lin Lin
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Image Feature ◽  
Signal Acquisition ◽  
Time Varying ◽  
Intelligent Fault Diagnosis ◽  
Image Block ◽  
Wide Range

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

Feature Extraction of Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 190-191 ◽  
pp. 993-997
Author(s):  
Li Jie Sun ◽  
Li Zhang ◽  
Yong Bo Yang ◽  
Da Bo Zhang ◽  
Li Chun Wu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Attribute Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Characteristic Parameters ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Experimental Trials

Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis. This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening. The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, "zoom" method and vibration signal as the characteristic parameters. The latter includes two methods: sensitivity extraction and data mining methods to select attributes. Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

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.

scholarly journals Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Weigang Wen ◽  
Robert X. Gao ◽  
Weidong Cheng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Manifold Learning ◽  
Planetary Gear ◽  
Vibration Signal ◽  
Planetary Gearbox ◽  
Characteristic Extraction ◽  
Gear Fault ◽  
Planet Gear ◽  
Fault Characteristic

The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Manifold learning is utilized to reduce the effect of noise within meshing frequency band. The fault characteristic frequency of the planetary gear is shown by spectrogram. The planetary gearbox model and test rig are established and experiments with planet gear faults are conducted for verification. All results of experiment analysis demonstrate its effectiveness and reliability.

A Novel Feature Selection Method for Process Fault Diagnosis

2013 ◽  
Vol 427-429 ◽  
pp. 2045-2049
Author(s):  
Chun Mei Yu ◽  
Sheng Bo Yang
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Selection Process ◽  
Feature Selection Method ◽  
Principal Component ◽  
Selection Method ◽  
Fault Classification ◽  
Process Data ◽  
Bhattacharyya Distance

To increase fault classification performance and reduce computational complexity,the feature selection process has been used for fault diagnosis.In this paper, we proposed a sparse representation based feature selection method and gave detailed procedure of the algorithm. Traditional selecting methods based on wavelet package decomposition and Bhattacharyya distance methods,and sparse methods, including sparse representation classifier, sparsity preserving projection and sparse principal component analysis,were compared to the proposed method.Simulations showed the proposed selecting method gave better performance on fault diagnosis with Tennessee Eastman Process data.

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.

Sign in / Sign up

Export Citation Format

Share Document