scholarly journals Vibration-Based Fault Diagnosis of Commutator Motor

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Adam Glowacz ◽  
Witold Glowacz
Keyword(s):  
Fault Diagnosis ◽  
Extraction Methods ◽  
Total Efficiency ◽  
Practical Application ◽  
Backpropagation Neural Network ◽  
Vibration Signals ◽  
Linear Discriminant ◽  
Recognition Efficiency ◽  
Signal Processing Methods ◽  
Selection Of

This paper presents a study on vibration-based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF-RATIO-50-SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF-RATIO-50-SFC-EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF-RATIO-50-SFC, MSAF-RATIO-50-SFC-EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (TV). The proposed methods have practical application in industries.

Intelligent fault diagnosis using image representation of multi-domain features

2021 ◽  
pp. 1-13
Author(s):  
Yulong Zhang ◽  
Chaofei Zhang ◽  
Jian Tan ◽  
Frank Lim ◽  
Menglan Duan
Keyword(s):  
Fault Diagnosis ◽  
Image Representation ◽  
Experimental Studies ◽  
Extraction Methods ◽  
Single Domain ◽  
Image Transformation ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Multiple Domains ◽  
Domain Information

Deep learning (DL) algorithms, especially the convolutional neural network (CNN), have been proven as a newly developed tool in machinery intelligent diagnosis. However, the current CNN-based fault diagnosis studies usually consider features or images extracted from a single domain as model input. This single domain information may not reflect fault patterns comprehensively, leading to low modeling accuracy and inaccurate diagnostic results. To overcome this limitation, this paper proposes a new CNN-based fault diagnosis approach using image representation considering multi-domain features of vibration signals. First, multi-domain features of vibration signals are extracted. These extracted features are then used to construct a n × n matrix, and subsequently to form images by RGB color transformations. This image transformation technique allows for capturing complementary and rich diagnostic information from multiple domains. At last, these images associated with different mechanical defects are fed into a CNN model that is improved based on the classic LeNet-5 CNN architecture for fault diagnosis and identification. Comparative experiments with the traditional feature extraction methods as well as state-of-the-art CNN-based methods are also investigated. Experimental studies on rolling bearings validate the effectiveness and superiorities of the proposed approach.

scholarly journals Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Gao ◽  
Jiangang Lv
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Feature Space ◽  
Single Stage ◽  
Training Data ◽  
Learning Machine ◽  
Hidden Layer ◽  
Signal Processing Methods ◽  
Hidden Nodes ◽  
Selection Of

Single-Stage Extreme Learning Machine (SS-ELM) is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM) has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

scholarly journals Acoustic-Based Fault Diagnosis of Commutator Motor

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 299 ◽  
Author(s):  
Adam Glowacz
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Acoustic Analysis ◽  
Recognition Rate ◽  
Gear Train ◽  
Self Organizing Map ◽  
Nearest Neighbour ◽  
Classification Methods ◽  
Backpropagation Neural Network ◽  
Selection Of

In the paper, the author presents acoustic-based fault diagnosis of a commutator motor (CM). Five states of the commutator motor were considered: healthy commutator motor, commutator motor with broken rotor coil, commutator motor with shorted stator coils, commutator motor with broken tooth on sprocket, commutator motor with damaged gear train. A method of feature extraction MSAF-15-MULTIEXPANDED-8-GROUPS (Method of Selection of Amplitudes of Frequency Multiexpanded 8 Groups) was described and implemented. Classification methods, such as nearest neighbour (NN), nearest mean (NM), self-organizing map (SOM), backpropagation neural network (BNN) were used for acoustic analysis of the commutator motor. The paper provides results of acoustic analysis of the commutator motor. The results had a good recognition rate. The results of acoustic analysis were in the range of 88.4–94.6%. The NM classifier and the MSAF-15-MULTIEXPANDED-8-GROUPS provided TERCM = 94.6%.

scholarly journals A Segmented Preprocessing Method for the Vibration Signal of an On-Load Tap Changer

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 131
Author(s):  
Rongyan Shang ◽  
Changqing Peng ◽  
Ruiming Fang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Contact Wear ◽  
Preprocessing Method ◽  
Mechanical Faults ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Selection Of

The vibration signal of an on-load tap changer (OLTC) consists of a series of sharp vibration bursts, and its fault feature in certain periods is easily missed. This study considered that preprocessing the vibration signal of the OLTC in segments could effectively solve the aforementioned problem. First, the collection of the signal is discussed, the waveform characteristics of the vibration signal when the OLTC was in normal action was described, and the selection of the signal was analyzed. Second, the time domain characteristics and frequency spectrum analyses were carried out to demonstrate the necessity of segmented preprocessing. Further, the segmented preprocessing method for the vibration signal of the OLTC was presented. Finally, the main mechanical faults of the OLTC were simulated, and the vibration signals were collected to carry out the fault diagnosis experiment on the OLTC. The experimental results showed that the accuracy of the fault diagnosis increased from 94.30% of the nonsegmented preprocessing to 98.46% of the segmented preprocessing. The increase was greater, especially for contact wear faults. The method was successfully applied to the actual project.

scholarly journals Bearing Fault Detection Using Multi-Scale Fractal Dimensions Based on Morphological Covers

2012 ◽  
Vol 19 (6) ◽  
pp. 1373-1383 ◽  
Author(s):  
Pei-Lin Zhang ◽  
Bing Li ◽  
Shuang-Shan Mi ◽  
Ying-Tang Zhang ◽  
Dong-Sheng Liu
Keyword(s):  
Fractal Dimension ◽  
Fault Diagnosis ◽  
Fractal Dimensions ◽  
Extraction Methods ◽  
Experimental Results ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Self Similar

Vibration signals acquired from bearing have been found to demonstrate complicated nonlinear characteristics in literature. Fractal geometry theory has provided effective tools such as fractal dimension for characterizing the vibration signals in bearing faults detection. However, most of the natural signals are not critical self-similar fractals; the assumption of a constant fractal dimension at all scales may not be true. Motivated by this fact, this work explores the application of the multi-scale fractal dimensions (MFDs) based on morphological cover (MC) technique for bearing fault diagnosis. Vibration signals from bearing with seven different states under four operations conditions are collected to validate the presented MFDs based on MC technique. Experimental results reveal that the vibration signals acquired from bearing are not critical self-similar fractals. The MFDs can provide more discriminative information about the signals than the single global fractal dimension. Furthermore, three classifiers are employed to evaluate and compare the classification performance of the MFDs with other feature extraction methods. Experimental results demonstrate the MFDs to be a desirable approach to improve the performance of bearing fault diagnosis.

scholarly journals Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Decai Zhang ◽  
Xueping Ren ◽  
Hanyue Zuo
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Sample Entropy ◽  
Processing Methods ◽  
Vibration Signals ◽  
One Dimensional ◽  
Signal Processing Methods ◽  
Matrix Sample

Vibration signals of gearbox under different loads are sensitive to the existence of the fault and composite fault vibration signals are complex. Traditional fault diagnosis methods mostly rely on signal processing methods. It is difficult for signal processing methods to separate effective information from those fault signals. Therefore, traditional fault diagnosis methods are difficult to accurately identify those faults. In this paper, a one-dimensional convolutional neural network (1-D CNN) intelligent diagnosis method with improved SoftMax function is proposed. Local mean decomposition (LMD) decomposes the signals into different physical fictions (PF). PFs are input into the matrix sample entropy based on Euclidean distance (MESE), and the PFs which best reflect fault characteristics are selected. Finally, the PFs by MESE are used to train the CNN to identify the faults of parallel-shaft gearbox. Experiment shows that MESE can quickly and accurately select the PFs with the most significant fault features. 1-D CNN can get nearly 100% recognition rate with less time and the CNN of SoftMax improved can effectively eliminate LMD endpoint effect. This method can successfully identify single faults, combination faults, and faults under different loads of the gearbox. Compared with other methods, this method has the characteristics of high efficiency, accuracy, and strong anti-interference. Therefore, it can effectively solve the problem of complex fault signal decomposition of gearbox and can diagnose the gearbox fault under different load operation. It has great significance for gearbox fault diagnosis in actual production.

The Research Based on Empirical Mode Decomposition in Bearing Fault Diagnosis

2011 ◽  
Vol 103 ◽  
pp. 225-228 ◽  
Author(s):  
Tong Le Xu ◽  
Xue Zheng Lang ◽  
Xin Yi Zhang ◽  
Xin Cai Pei
Keyword(s):  
Fault Diagnosis ◽  
Resolving Power ◽  
Vibration Signals ◽  
Hilbert Huang Transform ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Interference Signals ◽  
Bearing Vibration ◽  
Signal Processing Methods

Comparing with many modern signal processing methods in the bearing fault diagnosis, Hilbert-Huang transform is proposed which has good resolving power, and is adapt to process non-stationary, nonlinear and no cross-interference signals. Experimental bearing vibration signals have been analyzed by using this method, whose impulse response waveform was effectively separated from the signals. The results show that the sensitivity and reliability of the method are satisfactory.

COMPOUND FAULT DIAGNOSIS OF BEARINGS USING AN IMPROVED SPECTRAL KURTOSIS BY MAXIMUM CORRELATION KURTOSIS DECONVOLUTION (MCKD).

2020 ◽  
Vol 9 (8) ◽  
pp. 66-72
Keyword(s):  
Fault Diagnosis ◽  
Resonance Frequency ◽  
Background Noise ◽  
Maximum Correlation ◽  
Vibration Signals ◽  
Filter Length ◽  
Frequency Bands ◽  
Noise Interference ◽  
Spectral Kurtosis

This paper discusses the use of Maximum Correlation kurtosis deconvolution (MCKD) method as a pre-processor in fast spectral kurtosis (FSK) method in order to find the compound fault characteristics of the bearing, by enhancing the vibration signals. FSK only extracts the resonance bands which have maximum kurtosis value, but sometimes it might possible that faults occur in the resonance bands which has low kurtosis value, also the faulty signals missed due to noise interference. In order to overcome these limitations FSK used with MCKD, MCKD extracts various faults present in different resonance frequency bands; also detect the weak impact component, as MCKD also dealt with strong background noise. By obtaining the MCKD parameters like, filter length & deconvolution period, we can extract the compound fault feature characteristics.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

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