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Dear Colleagues,

This Special Issue intends to expand the existing knowledge on advanced condition monitoring methodologies and the inclusion of computational techniques for effective monitoring. The majority of the electrical apparatus involved have high voltages, high costs, and possible risks of failures. Failures of an electrical apparatus often occur due to vulnerable operating conditions, insulation failures, and electrical and thermal stresses. Thus, it is essential to adopt efficient condition monitoring techniques (both online and offline) for the successful operation of the electrical power network. Starting from generating stations, grid parameters, distribution aspects, and utilization, condition monitoring is of very high importance in engineering. Some of these are very complex (high dimensional/ambiguity) and it is challenging to handle them and make decisions regarding their prognosis. Thus, adopting artificial intelligence and machine learning (AIML) techniques, sensor technologies, and advanced diagnostic approaches is a potential avenue of research for future grid operations. Therefore, we invite contributions on technical developments, regular research problems, critical reviews, and industrial case studies from the electrical engineering community. Studies pertining to condition monitoring, insulation failures, intelligent monitoring ideas, and AIML for precise monitoring are invited.

Dr. U. Mohan Rao
Prof. Dr. Issouf Fofana
Guest Editors

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Keywords

condition monitoring (online/offline)
intelligent monitoring techniques
diagnostic testing
sensor and signal processing.

Published Papers (1 paper)

Research

20 pages, 2300 KiB

Open AccessArticle

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

by Eoghan T. Chelmiah, Violeta I. McLoone and Darren F. Kavanagh

Energies 2023, 16(14), 5312; https://doi.org/10.3390/en16145312 - 11 Jul 2023

Viewed by 864

Abstract

Improving the reliability and performance of electric and rotating machines is crucial to many industrial applications. This will lead to improved robustness, efficiency, and eco-sustainability, as well as mitigate significant health and safety concerns regarding sudden catastrophic failure modes. Bearing degradation is the most significant cause of machine failure and has been reported to cause up to 75% of low-voltage machine failures. This paper introduces a low complexity machine learning (ML) approach to estimate the remaining useful life (RUL) of rolling element bearings using real vibration signals. This work explores different ML recipes using novel feature engineering coupled with various k-Nearest Neighbour (k-NN), and Support Vector Machines (SVM) kernel and weighting functions in order to optimise this RUL approach. Original non-linear wear state models and feature sets are investigated, the latter are derived from Short-time Fourier Transform (STFT) and Hilbert Marginal Spectrum (HMS). These feature sets incorporate one-third octave band filtering for low complexity multivariate feature subspace compression. Our proposed ML algorithm stage has employed two robust supervised ML approaches: weighted k-NN and SVM. Real vibration data were drawn from the Pronostia platform to test and validate this prognostic monitoring approach. The results clearly demonstrate the effectiveness of this approach, with classification accuracy results of up to 82.8% achieved. This work contributes to the field by introducing a robust and computationally inexpensive method for accurate monitoring of machine health using low-cost vibration-based sensing. Full article

(This article belongs to the Special Issue Condition Monitoring and Machine Learning Strategies for Electrical Apparatus 2022–2023)

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