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Signal Processing and Machine Learning for Asset Management and Condition...
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Signal Processing and Machine Learning for Asset Management and Condition Monitoring

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 1764

Special Issue Editors

Dr. Shady S. Refaat

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar
Interests: machine learning; IoT device
Dr. Majdi Mansouri

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar
Interests: statistical signal processing; machine learning; forecasting; renewable energies

Special Issue Information

Dear Colleagues,

Signal processing is a tool to capture, interpret and extract meaningful information from physical phenomena. Electrical power assets are usually overstrained and suffer from overaged conditions, including atmospheric instability. The electric equipment performance degrades over time. It requires effective evaluation of performance degradation to ensure long-term operations, avoid unplanned production downtime and maximize asset lifetime for greater resiliency. Asset management strategies support monitoring various parameters of the condition of those electric assets and also identify the primary degradation mechanisms, estimate the aging, predict the remaining useful lifetime, allow for optimal performance, reduce unplanned shutdowns and identify the causes of failures at early stages. Different machine learning and artificial intelligence methods are used for asset management and condition monitoring.

In order to pursue this goal in a practical and efficacious way, many aspects must be considered, with reference to many elements:

  • Equipment reliability
  • Asset-management solutions for electrical equipment
  • Uncertainty measurement assessment for electrical equipment
  • Signal-processing techniques for condition monitoring of electrical equipment
  • Electrical asset protection
  • Electrical asset reliability
  • Machine Learning in support of electric distribution Asset
  • Lifetime estimation of the electrical equipment
  • More topics will also be considered if they are coherent with this theme.

Dr. Shady S. Refaat
Dr. Majdi Mansouri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Signals is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • signal processing
  • machine learning
  • asset management
  • condition monitoring

Published Papers (1 paper)

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Research

20 pages, 905 KiB  
Article
Enhanced Neural Network Method-Based Multiscale PCA for Fault Diagnosis: Application to Grid-Connected PV Systems
by Khadija Attouri, Majdi Mansouri, Mansour Hajji, Abdelmalek Kouadri, Kais Bouzrara and Hazem Nounou
Signals 2023, 4(2), 381-400; https://doi.org/10.3390/signals4020020 - 30 May 2023
Viewed by 1235
Abstract
In this work, an effective Fault Detection and Diagnosis (FDD) strategy designed to increase the performance and accuracy of fault diagnosis in grid-connected photovoltaic (GCPV) systems is developed. The evolved approach is threefold: first, a pre-processing of the training dataset is applied using [...] Read more.
In this work, an effective Fault Detection and Diagnosis (FDD) strategy designed to increase the performance and accuracy of fault diagnosis in grid-connected photovoltaic (GCPV) systems is developed. The evolved approach is threefold: first, a pre-processing of the training dataset is applied using a multiscale scheme that decomposes the data at multiple scales using high-pass/low-pass filters to separate the noise from the informative attributes and prevent the stochastic samples. Second, a principal component analysis (PCA) technique is applied to the newly obtained data to select, extract, and preserve only the more relevant, informative, and uncorrelated attributes; and finally, to distinguish between the diverse conditions, the extracted attributes are utilized to train the NNs classifiers. In this study, an effort is made to take into consideration all potential and frequent faults that might occur in PV systems. Thus, twenty-one faulty scenarios (line-to-line, line-to-ground, connectivity faults, and faults that can affect the normal operation of the bay-pass diodes) have been introduced and treated at different levels and locations; each scenario comprises various and diverse conditions, including the occurrence of simple faults in the PV1 array, simple faults in the PV2 array, multiple faults in PV1, multiple faults in PV2, and mixed faults in both PV arrays, in order to ensure a complete and global analysis, thereby reducing the loss of generated energy and maintaining the reliability and efficiency of such systems. The obtained outcomes demonstrate that the proposed approach not only achieves good accuracies but also reduces runtimes during the diagnosis process by avoiding noisy and stochastic data, thereby removing irrelevant and correlated samples from the original dataset. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Asset Management and Condition Monitoring)
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