State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

Akbar, Siddique; Vaimann, Toomas; Asad, Bilal; Kallaste, Ants; Sardar, Muhammad Usman; Kudelina, Karolina

doi:10.3390/en16176345

Cited by 9 publications

(7 citation statements)

References 249 publications

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“…The types of metering anomalies are diverse and vary slightly depending on the application scenario [1,42]. Ref.…”

Section: Description Of Metering Anomalymentioning

confidence: 99%

Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining

Sun,

Zhang,

Guo

et al. 2024

Energies

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Metering anomalies not only mean huge economic losses but also indicate the faults of equipment and power lines, especially within the substation. As a result, metering anomaly diagnosis is becoming one of the most important missions in smart grids. However, due to the insufficient and imbalanced anomaly cases, identifying the anomalies in smart meter data accurately and efficiently remains challenging. Existing methods usually employ few-shot learning models in computer vision directly, which requires the rich experience of human experts and sufficient abnormal cases for training. It blocks model generalizing to various application scenarios. To address these shortcomings, we propose a novel framework for metering anomaly diagnosis based on few-shot learning, named FSMAD. Firstly, we design a fault data injection model to emulate anomalies, so that no abnormal samples are required in the training phase. Secondly, we provide a learnable variable transformation to reveal inherent relationships among various smart meter data and help FSMAD extract more efficient features. Finally, the deeper metric network is equipped to support FSMAD in obtaining powerful comparison capability. Extensive experiments on a real-world dataset demonstrate the advantages of our FSMAD over state-of-the-art methods.

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“…The types of metering anomalies are diverse and vary slightly depending on the application scenario [1,42]. Ref.…”

Section: Description Of Metering Anomalymentioning

confidence: 99%

Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining

Sun,

Zhang,

Guo

et al. 2024

Energies

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“…K-Means clustering, a popular unsupervised machine learning algorithm, divides data into K clusters. It aims to minimize the within-cluster variance by iteratively updating cluster centroids, as explained in Equation (1).…”

Section: K-means Clusteringmentioning

confidence: 99%

“…The reliable and safe operation of electrical machinery has been receiving more attention in recent years. A great deal of research has been conducted on diagnostics, the early identification of electrical machine defects, and the estimation of remaining life [1]. Within the realm of automotive engineering, the utilization of DC motors stands as a cornerstone, celebrated for their versatility, precise control capabilities, and robust torque output.…”

Section: Introductionmentioning

confidence: 99%

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

Hussain,

Zaidi

2024

Applied Sciences

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This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated.

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“…Various parts of a rotating electrical machine, such as the stator, rotor and rolling bearings, are susceptible to significant issues [46]. Notably, rolling bearing defects are among the most frequent types of failures in electrical motors, occurring at a rate of 30-40%.…”

Section: Bearing Fault Detection Workflow Problem Description and Reviewmentioning

confidence: 99%

A Study of Noise Effect in Electrical Machines Bearing Fault Detection and Diagnosis Considering Different Representative Feature Models

Moysidis,

Karatzinis,

Boutalis

et al. 2023

Machines

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As the field of fault diagnosis in electrical machines has significantly attracted the interest of the research community in recent years, several methods have arisen in the literature. Also, raw data signals can be acquired easily nowadays, and, thus, machine learning (ML) and deep learning (DL) are candidate tools for effective diagnosis. At the same time, a challenging task is to identify the presence and type of a bearing fault under noisy conditions, especially when relevant faults are at their incipient stage. Since, in real-world applications and especially in industrial processes, electrical machines operate in constantly noisy environments, a key to an effective approach lies in the preprocessing stage adopted. In this work, an evaluation study is conducted to find the most suitable signal preprocessing techniques and the most effective model for fault diagnosis of 16 conditions/classes, from a low-workload (computational burden) perspective using a well-known dataset. More specifically, the reliability and resiliency of conventional ML and DL models is investigated here, towards rolling bearing fault detection, simulating data that correspond to noisy industrial environments. Diverse preprocessing methods are applied in order to study the performance of different training methods from the feature extraction perspective. These feature extraction methods include statistical features in time-domain analysis (TDA); wavelet packet decomposition (WPD); continuous wavelet transform (CWT); and signal-to-image conversion (SIC), utilizing raw vibration signals acquired under varying load conditions. The noise effect is examined and thoroughly commented on. Finally, the paper provides accumulated usual practices in the sense of preferred preprocessing methods and training models under different load and noise conditions.

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State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

Cited by 9 publications

References 249 publications

Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining

Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

A Study of Noise Effect in Electrical Machines Bearing Fault Detection and Diagnosis Considering Different Representative Feature Models

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