Combination Of Quadratic Discriminant Analysis And Daubechis Wavelet For Classification Level Of Misalignment On Induction Motor

Saputra, Pressa Perdana Surya; Murdianto, Farid Dwi; Firmansyah, Rifqi; Widarsono, Kukuh

doi:10.1109/isesd.2019.8909431

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…The DWT can be particularly useful for detecting this type of fault (Saputra et al 2019b). It allows the decomposition of diagnostic signals to the frequency range in which diagnostic information is contained.…”

Section: Symptoms Of Misalignment In the Stator Phase Current Signalmentioning

confidence: 99%

Application of Spectral and Wavelet Analysis of Stator Current to Detect Angular Misalignment in PMSM Drive Systems

Pietrzak

Wolkiewicz

2021

Power Electronics and Drives

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This paper deals with the selected methods of detecting angular misalignment in drive systems with a permanent magnet synchronous motor (PMSM), which are based on the analysis of the stator phase current signal, as well as their experimental verification and comparison. The proposed and compared methods are spectral analysis and wavelet analysis of the stator current, stator current envelope, stator current space vector module. Furthermore, the influence of power supply frequency and load torque on the performance of the proposed diagnostic methods is also discussed. The experimental tests were carried out for an undamaged motor and for two levels of angular misalignment. The article discusses the question of exactly what damage symptoms can be extracted from each of the methods. In the case of spectral analyses, it is demonstrated which multiplicities of the failure frequency are the most sensitive to misalignment and the least sensitive to changes in motor operating condition, which may be considered novel in the case of drive systems with permanent magnet motors. It is also proven that discrete wavelet transform (DWT) of the envelope and monitoring of the value of the relevant components allows the detection of misalignment with the availability of measuring current only in one phase in various motor operating conditions.

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Section: Symptoms Of Misalignment In the Stator Phase Current Signalmentioning

confidence: 99%

Application of Spectral and Wavelet Analysis of Stator Current to Detect Angular Misalignment in PMSM Drive Systems

Pietrzak

Wolkiewicz

2021

Power Electronics and Drives

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“…Whereas, the signal energy, e, is the sum of the squares of each signal component. This can be written as (7) [19][20][21][22].…”

Section: Feature Extractionmentioning

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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“…Haddad [11] proposed the LDA method in combination with motor current signature analysis (MCSA) to detect damage to the motor magnet. Saputra [12] combined QDA and wavelet to classify misalignment of two shafts in induction motors.…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Discriminant Analysis for Pipeline Leaks Detection

Kamiel

Rukmana

2022

JMPM

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Pipelines are useful for transporting liquids from one place to another. The main problem that often occurs in pipelines is leakage which results in production and financial losses. The importance of detecting pipeline leaks makes the industries look for effective detection methods to avoid bigger losses. Several previous studies have proven that the vibration-based method is successful in detecting leaks in pipelines. However, the vibration-based method used in the previous study is relatively complicated and requires specialists to interpret the results. This study proposes a machine learning-based detection method that can classify pipe conditions directly without the help of a specialist. The proposed method is vibration-based discriminant analysis; a machine learning algorithm that recognizes pipeline conditions from their vibration pattern instead of spectrum. The proposed method was tested on a test rig consisting of a closed-loop pipeline equipped with a leak-pipe test segment. The vibration signal is taken using an accelerometer placed on the leak-pipe test segment. Time domain vibration data is extracted using several statistical parameters which aims to reveal information related to pipe conditions. The vibration data collected is divided into two groups, namely training-data and testing-data. The discriminant analysis model is trained to recognize the vibration pattern of the pipeline using training-data and then tested using testing-data. There are four leak sizes introduced in this study, small, medium, and large. Meanwhile, normal condition (no leaks) is used as benchmarking. The study shows that the proposed method is effective in classifying four pipe conditions with the accuracy up to 95%.

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Combination Of Quadratic Discriminant Analysis And Daubechis Wavelet For Classification Level Of Misalignment On Induction Motor

Cited by 7 publications

References 9 publications

Application of Spectral and Wavelet Analysis of Stator Current to Detect Angular Misalignment in PMSM Drive Systems

Application of Spectral and Wavelet Analysis of Stator Current to Detect Angular Misalignment in PMSM Drive Systems

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

Vibration-Based Discriminant Analysis for Pipeline Leaks Detection

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