Motor Fault Diagnosis Based on Wavelet Transform

Wang, Lijun; Guo, Huijuan; Zhang, Shenfeng

doi:10.11591/telkomnika.v11i9.3288

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…In case of fault detection methods based on acoustic signals, wavelet transform is mostly used to extract time-frequency map of signals [14]. In [15], induction motor fault detection is proposed through a combination of IMFs of sound spectrum.…”

Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

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Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

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Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

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“…LPF and HPF filter with different cut off frequency is used to transform signal. High frequency signal or detail signal (cD) is output from HPF and low frequency signal or approximation signal (cA) is output from LPF [16]. The relationship between LPF/father wavelet and HPF/mother wavelet can be described as in the following (1-3) :…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Various and multilevel of wavelet transform for classification misalignment on induction motor with quadratic discriminant analysis

et al. 2020

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Induction motors have become a major part of the industry because of strong construction, cheap in purchasing and maintenance, high efficiency, and easy to operate. Preventive maintenance must always be carried out on all industrial equipment, including induction motors to last long and prevent further damage. Based on research in the industry, around 42%-50% or almost 50% is bearing damage. One reason is the occurrence of misalignment during the installation of the load on the induction motor. This study tries to identify the condition of the motor and classify the level of misalignment damage that occurs. In the process, the mother wavelet like as Daubechis, Symlet and Coiflet discrete wavelet transform (DWT) are selected as tools in processing motor vibration data. The level of DWT applied is 1st to 3rd level. Then, the three types of signal extraction, namely sum, range, and energy, which are obtained from a high-frequency signal of DWT, are used as input to Quadratic and Linear Discriminant Analysis. Then, discriminant analysis analyzes and classifies them into normal operation and two misalignments conditions. The simulation shows that 1st level of Daubechis DWT combined with quadratic discriminant analysis generates the best classification. It results 0% error of classification with Db3, Db4 and Db5, 4.17% error with Db1 and 8.33% error with Db2.

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“…The author suggested a unique technique that uses wavelet transform with 99.3% effectiveness for fault location to distinguish between different fault states and detect line faults in HVDC transmission [20]. To detect motor faults, the multi-resolution wavelet analysis is applied with the signal frequency corresponding to the peak of each power spectrum [21].…”

Section: Introductionmentioning

confidence: 99%

Analysis of converter transformer pressboard insulation degradation under surge using mathematical morphology

Mopari,

More,

Bhalchandra

et al. 2024

IJEECS

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Nowadays, with the significant expansion of industrial growth, the bulk power requirement can only be satisfied through high-voltage direct current HVDC transmission. The converter transformer is the utmost vital part of the HVDC transmission. Pressboard insulation is most commonly used as inter-disc insulation in converter transformers. During working conditions due to elevated temperature and different operational stresses, insulation material gets deteriorated. It may cause a risk to the life of the converter transformer. The effects of elevated temperatures as well as frequency on pressboard insulation of the converter transformer are examined in this study. The condition evaluation and morphological changes in pressboard insulation at elevated temperatures can evaluate with the help of frequency domain spectroscopy (FDS) and atomic force microscopy (AFM) techniques. The impact of elevated temperatures on insulation material can be analyzed based on surface roughness and dielectric parameters. In MATLAB Simulink environment, a dual winding single-phase converter transformers valve side star winding 60 discs model is constructed for impulse test. Based upon arrival time and velocity of traveling wave, insulation degradation location can be identified by using mathematical morphology. The simulation results demonstrate that the suggested method can notably located degradation across disc winding.

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Motor Fault Diagnosis Based on Wavelet Transform

Cited by 4 publications

References 12 publications

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Various and multilevel of wavelet transform for classification misalignment on induction motor with quadratic discriminant analysis

Analysis of converter transformer pressboard insulation degradation under surge using mathematical morphology

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