Introduction to Predictive Models for Motor Dielectric Aging

• Main Authors: Jones, Gavin, Frost, Nancy, Mosier, Aaron
• Format: Conference Proceeding
• Language: English
• Subjects: Aging; Data models; dielectrics; Insulation; Machine learning; materials; motors; predictive analytics; Predictive models; reliability; statistical modeling; testing; Training data; Uncertainty
• ISSN: 2576-6791
• DOI: 10.1109/EIC51169.2022.9833207

The traditional method for gaining knowledge on the state of a motor is to take test data in time increments and plot the progression of the machine parameters, looking for trending data. This leads to a reactive reliability mode, where one performs maintenance or takes corrective action once data have been collected that indicates cause for concern. Standard test methods are generally employed and over time one may become a knowledgeable expert on the condition of the motor and when to repair it prior to failure.Emulation and Uncertainty Quantification (UQ) can be used as a machine learning powered approach to improve upon traditional predictive maintenance practices, allowing for corrective action to be taken prior to the real-time data itself indicating concern. Machine Learning strategies also take the potentially unreliable human guess work out of the intuition-based approach to predicting failure based on prior experience and knowledge.Emulators (aka predictive models) are statistical models trained using advanced analytics and machine learning algorithms to learn the input-output relationships of an underlying data set, often called the training data. Once trained, the key strength of the emulator is the ability to rapidly make predictions of the output of a system for input combinations not contained in the training data, eliminating the need for further direct data collection to perform any desired analyses.Data collected from a motor can be used to train an emulator capable of predicting the future performance of that motor. If these predictions indicate a need for corrective action, the predictive speed of the emulator can be used to check the outcomes of different "what-if" scenarios to determine the best course of action. UQ tools can be used along with the statistical prediction process to place error bounds on the various outcomes.This paper will present the above concepts in more detail. This will include the steps of emulator training, beginning with a design of experiments to select appropriate training data to be collected from the motor, validation of the emulator's predictive accuracy, and its use for predictive maintenance and UQ, including sensitivity analyses of inputs on the output(s) of interest, uncertainty propagation, and optimization.