An Improved Stator Winding Short-circuit Fault Diagnosis using AdaBoost Algorithm

Shifat, Tanvir Alam; Hur, Jin

doi:10.1109/icaiic48513.2020.9065081

Cited by 9 publications

(9 citation statements)

References 13 publications

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“…To classify unknown data represented by the feature vector as a point in the feature space, the KNN calculates the distance between the new point and points that were used in the training process-the training data set. Then, this classifier assigns the point to the class among its K-nearest neighbors, where K is a pre-determined integer value [56,57].…”

Section: K-nearest Neighborsmentioning

confidence: 99%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.

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Section: K-nearest Neighborsmentioning

confidence: 99%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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“…This hints at an increased need for ensuring that the SMPS operates under the recommended specifications to avoid unforeseen failure. On the brighter side, condition monitoring offers a cost-effective solution for controlled and uninterrupted functioning [14].…”

Section: Motivation and Literature Overviewmentioning

confidence: 99%

“…Feature and usage-based diagnostics and prognostics for electronic systems can be utilized by finding essential diagnostic parameters that coincide with failure progression and recognized failure models. To provide collective proof of a worsening or deteriorating condition, collected features can be identified, evaluated, and exploited for data-driven (AI-based) FDI [10,13,14]. From a realistic perspective, the complexity of electronic data, the vast number of parameters, conflicting failure mechanisms, and the prevalence of intermittent faults and failures make health management and assessment of electronic systems a difficult task.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Cost-Aware Dual Monitoring Framework for SMPS Switching Device Diagnosis

2021

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The ability of a switch-mode AC/DC power supply to shrink supplies is a benefit and a requirement for most electronic devices with limited space. Major failures in switch-mode power supply (SMPS) during adverse working conditions are subject to mostly the switching devices and capacitors. For effective condition monitoring of the SMPS, dual (or multiple) sensing provides a more reliable standpoint against the traditional single sensing techniques as it provides a more comprehensive paradigm for accurate condition monitoring. This study proposes an integrated approach to SMPS condition monitoring by exploiting statistically extracted features from current and voltage signals for system fault diagnosis based on electrical stress. Following a correlation-based feature selection approach, salient features were utilized for improved fault detection and isolation (FDI) using ML-based classifiers. Diagnostic results by the classifiers reveal that the random forest and gradient boosting classifiers are highly reliable but computationally expensive when compared with the others while the decision tree was quite cost-efficient with reliable diagnostic results. The proposed framework is effectively applicable for use in diagnosing the switching devices and classification at different states.

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“…In a data-driven approach, different sensor data are acquired on the basis of system type and operating condition. These data are analyzed using different signal processing and machine learning (ML) methods to find the fault patterns in acquired data [3]. Recently, fault diagnosis using sensor data has been widely adopted for the condition monitoring of many industrial assets; small devices, such as transistors, to large machines, such as generators [4].…”

Section: Introductionmentioning

confidence: 99%

Reliability improvement in the presence of weak fault features using non-Gaussian IMF selection and AdaBoost technique

Shifat

Hur

2021

J Mech Sci Technol

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In machinery fault detection and identification (FDI), decomposing vibration signals into corresponding intrinsic mode functions (IMFs) reduces the intricacy in extracting weak fault features at the early failure state. However, selecting a suitable IMF for fault information extraction is a challenging task. Analyzing the non-Gaussian IMFs allows extracting effective fault-related information rather than the entire signal or other IMFs because the vibration signals are random in nature. In this study, we present an IMF selection method based on the maximum kurtosis value of each IMF. A kurtosis computation method named autogram is used. It considers the autocovariance function to characterize the 2nd order cyclostationary. We deploy the AdaBoost algorithm with a decision tree classifier to gain a better performance compared with other tree-based classifiers. The proposed FDI framework can effectively detect and classify multiple fault features at the incipient failure stage.

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An Improved Stator Winding Short-circuit Fault Diagnosis using AdaBoost Algorithm

Cited by 9 publications

References 13 publications

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

An Integrated Cost-Aware Dual Monitoring Framework for SMPS Switching Device Diagnosis

Reliability improvement in the presence of weak fault features using non-Gaussian IMF selection and AdaBoost technique

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