Automated bearing fault classification based on discrete wavelet transform method

2020 ◽  
Author(s):  
R. Shukla ◽  
P. K. Kankar ◽  
R. B. Pachori
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Bearing Fault

scholarly journals Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

2020 ◽  
Vol 10 (15) ◽  
pp. 5251 ◽  
Author(s):  
Rafia Nishat Toma ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Induction Motors ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Current Signal ◽  
Bearing Fault ◽  
Ensemble Machine Learning

Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.

MOBILE ROBOT MOTOR BEARING FAULT DETECTION AND CLASSIFICATION ON DISCRETE WAVELET TRANSFORM AND LSTM NETWORK

2018 ◽  
Vol 18 (08) ◽  
pp. 1840034 ◽  
Author(s):  
SHIWEI LI ◽  
YONGPING ZHAO ◽  
MINGLI DING
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Mobile Robot ◽  
Discrete Wavelet Transform ◽  
Vibration Signal ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Bearing Fault ◽  
Fault Type ◽  
Motor Bearing

The impact of motors breakdown and failures on mobile robot motor bearing is an important concern for robot industries. For this reason, predictive motor lifetime and bearing fault classification techniques are being investigated extensively as a method of decreasing motor downtime and enhancing mobile robot reliability. With increasing attention on neural network technologies, many researchers have carried out lots of the relevant experiments and analyses, very plentiful and important conclusions are obtained. In this article, a classification method based on discrete wavelet transform (DWT) and long short-term memory network (LSTM) a proposed to find and classify fault type of mobile robot permanent magnet synchronous motor (PMSM). First, a set of mobile robot motor vibration signal were collected by the sensors. Second, the obtained vibration signal is decomposed into six frequency bands by the DWT. Haar function is selected as the mother function in the processing. The energy of every frequency band was calculated as a classification feature. Thirdly, four classification features with high classification rate are obtained. The feature vector is used as input of the neural network, and the fault type is identified by LSTM classifier with deviation unit. From the results of the experiments provided in the paper, the method can detect the fault type accurately and it is feasible and effective under different motor speed.

Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method

2015 ◽  
Vol 81 ◽  
pp. 56-64 ◽  
Author(s):  
U. Rajendra Acharya ◽  
K. Sudarshan Vidya ◽  
Dhanjoo N. Ghista ◽  
Wei Jie Eugene Lim ◽  
Filippo Molinari ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Computer Aided Diagnosis ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Computer Aided ◽  
Aided Diagnosis

Traditional Chinese Medicine Pulse-Condition Simulation and Wavelet Recognition Method

2010 ◽  
Vol 139-141 ◽  
pp. 2029-2032
Author(s):  
Dong Cao ◽  
Jian Wei Ye ◽  
Jun Yi ◽  
Wen Jie Ruan ◽  
Chong Chen
Keyword(s):  
Wavelet Transform ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Discrete Wavelet Transform ◽  
Subjective Experience ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Energy Characteristics ◽  
Pulse Condition

The human pulse-condition diagnosis is an important part of the traditional Chinese medicine (TCM) which is difficult to recognize accurately by doctor’s subjective experience. Objective identification of pulse-conditions has important meanings for modernization of TCM. In this paper human pulse-condition system transfer function model and model parameter estimation were introduced, which are used to construct four kinds of typical pulse-conditions simulation signals. There are normal pulse, taut pulse, slippery pulse and thready pulse. And then, discrete wavelet transform for extracting the multi-scale energy characteristics and wavelet packet decomposition for extracting the multi-band energy characteristics are proposed so as to recognize the pulse-conditions simulation signals. The results show that the recognition effect of discrete wavelet transform method is better. Moreover, the data features of characteristic parameters demonstrate the reality of simulation signals.

Data Hiding Scheme for Video Watermarking Using Horizontal and Vertical Coefficients of Single Level Discrete Wavelet Transform Method

2021 ◽  
pp. 2250044
Author(s):  
J. Jerisha Liby ◽  
T. Jaya
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Similarity Index ◽  
Video Watermarking ◽  
Data Matrix ◽  
Experimental Result ◽  
Video Frame ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Single Level

This paper proposes a new watermarking algorithm based on a single-level discrete wavelet transform (DWT). This method initially chooses ‘[Formula: see text]’ number of carrier frames to hide the data. After estimating the carrier frames, each frame is separated into RGB frames. Each R, G, and B frames are decomposed using a single-level DWT. The horizontal and vertical coefficients are selected to embed the watermark information since small changes in the horizontal and vertical coefficients do not highly affect the quality of the video frame. The watermark image pixels are shuffled using a predetermined key before embedding. The shuffled pixels are converted to binary, and they are grouped into three data matrices. Each data matrix is embedded in horizontal and vertical coefficients of the R, G and B frames of the video frame. After embedding the data, the watermarked video is reconstructed using the original approximation coefficients, the embed coefficients, and the original diagonal coefficients. During the extraction process, the watermark is extracted from the horizontal and vertical coefficients of the watermarked video. Experimental result reveals that the proposed method outperforms other related methods in terms of video quality and structural similarity index measurement.

Analysis of gastric myoelectrical activity from the electrogastrogram signals based on wavelet transform and line length feature

2018 ◽  
Vol 232 (4) ◽  
pp. 403-411 ◽  
Author(s):  
Abdullah Al Kafee ◽  
Aydin Akan
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Functional Dyspepsia ◽  
Line Length ◽  
Diabetic Gastroparesis ◽  
Surface Measurement ◽  
Control Group ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Control Subjects

Electrogastrogram is used for the abdominal surface measurement of the gastric electrical activity of the human stomach. The electrogastrogram technique has significant value as a clinical tool because careful electrogastrogram signal recordings and analyses play a major role in determining the propagation and coordination of gastric myoelectric abnormalities. The aim of this article is to evaluate electrogastrogram features calculated by line length features based on the discrete wavelet transform method to differentiate healthy control subjects from patients with functional dyspepsia and diabetic gastroparesis. For this analysis, the discrete wavelet transform method was used to extract electrogastrogram signal characteristics. Next, line length features were calculated for each sub-signal, which reflect the waveform dimensionality variations and represent a measure of sensitivity to differences in signal amplitude and frequency. The analysis was carried out using a statistical analysis of variance test. The results obtained from the line length analysis of the electrogastrogram signal prove that there are significant differences among the functional dyspepsia, diabetic gastroparesis, and control groups. The electrogastrogram signals of the control subjects had a significantly higher line length than those of the functional dyspepsia and diabetic gastroparesis patients. In conclusion, this article provides new methods with increased accuracy obtained from electrogastrogram signal analysis. The electrogastrography is an effective and non-stationary method to differentiate diabetic gastroparesis and functional dyspepsia patients from the control group. The proposed method can be considered a key test and an essential computer-aided diagnostic tool for detecting gastric myoelectric abnormalities in diabetic gastroparesis and functional dyspepsia patients.

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