Introduction to Predictive Models for Motor Dielectric Aging

Jones, Gavin; Frost, Nancy

doi:10.1109/eic47619.2020.9158730

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Thus, the most suitable investigation could only be achieved from the integrative interpretation analysis adequately correlated with visualization of all monitored parameters [10,11]. Some other maintenance principles are based on the insulation aging detection [12] or bearing fault signaling [13]. Even laboratory maintenance tests were developed [14] or such systems dedicated to photovoltaic applications [15].…”

Section: Introductionmentioning

confidence: 99%

Smart Predictive Maintenance Device for Critical In-Service Motors

2022

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The paper proposed an innovative predictive maintenance system, designated to monitor and diagnose critical electrical equipment (generally large power electric motors) within industrial electrical installations. A smart and minimally invasive system is designed and developed. Its scope is to evaluate continuously the essential operating parameters (electrical, thermal, and mechanical) of the investigated equipment. It manages to report the deviations of inspected machine operating parameters values from the rated ones. The system also suggests the potential cause of these abnormal variations along with possible means (if the defect is identified in a database, constantly updated with each appearance of a malfunction). The developed maintenance device generates an operating report of the analyzed equipment, in which the values of power quality and energy indicators are computed and interpreted. Additionally, real-time remote transmission of analyzed data is facilitated, making them accessible from any location. The proposed maintenance system is a low-cost device that is easy to install and use in comparison with similar existing devices and equipment. The designed maintenance system was tested on dedicated to low-voltage equipment up to 100 kW.

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Section: Introductionmentioning

confidence: 99%

Smart Predictive Maintenance Device for Critical In-Service Motors

2022

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Introduction to Predictive Models for Motor Dielectric Aging

Jones¹,

Frost²,

Mosier³

2022

2022 IEEE Electrical Insulation Conference (EIC)

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The Impact of Uncertainty Quantification and Sensitivity Analysis in CAE Simulation based Regulatory Compliance

Deshpande,

Mahajan,

Jones

2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Computer-aided engineering (CAE) is a routinely used technology for the design and testing of road vehicles, including the simulation of their response to an impact. To increase automotive industry competitiveness by reducing physical test-based type approval and to improve road safety, recent initiatives have been taken by both industry and public authorities to promote the use of virtual testing through numerical simulation as an alternative way to check regulatory compliance. [<span class="xref">1</span>]</div><div class="htmlview paragraph">To ensure acceptance of this alternative method, the accuracy of the simulation models and procedures needs to be assured and rated independently of the modelling process, software tools, and computing platform. Similarly, it is also imperative to understand the uncertainties emerging out of different component design parameters and analyze their sensitivity towards producing deviations in the reported results as per the requirements of the regulatory standard. Simulations are however deterministic in nature and do not in general consider the uncertainty in areas such as design, manufacturing, and use of the product. Using machine learning and uncertainty quantification, engineers can predict the range of possible outcomes for a given design by accounting for such uncertainties. Training emulators (aka machine learning models, predictive model, etc.) of simulations can allow for the efficient performance of the advanced analytics required.</div><div class="htmlview paragraph">Design parameters of a front under-run protection device (FUPD) were evaluated using computer simulation as per Automotive Indian Standard (AIS) 069 loading conditions [<span class="xref">2</span>]. The process involved the use of Design of Experiments (DOEs) to collect emulator training data and uses the trained emulator to perform uncertainty propagation of manufacturer part tolerances. A sensitivity analysis study was carried out to understand the dependence of output results on crucial parameters.</div><div class="htmlview paragraph">The results obtained from this study lend benefit towards understanding the uncertainties involved in CAE simulation-based product assessments. It will also be useful as a benchmark to quantify uncertainties involved in other load cases wherein the regulatory standard allows the use of virtual testing as an alternative method to physical type approval.</div></div>

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Introduction to Predictive Models for Motor Dielectric Aging

Cited by 3 publications

References 4 publications

Smart Predictive Maintenance Device for Critical In-Service Motors

Smart Predictive Maintenance Device for Critical In-Service Motors

Introduction to Predictive Models for Motor Dielectric Aging

The Impact of Uncertainty Quantification and Sensitivity Analysis in CAE Simulation based Regulatory Compliance

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