Abstract
Estimating the lifetime of enameled wires using the conventional/standard test method requires a significant amount of time that can endure up to thousands of testing hours, which could considerably delay the time-to-market of a new product. This paper presents a new approach that estimates the insulation lifetime of enameled wire, employed in electrical machines, using curve fitting models whose computation is rapid and accurate. Three curve fit models are adopted to predict the insulation resistance of double-coated enameled magnet wire samples, with respect to their aging time. The samples' mean time-to-failure is estimated, and performance of the models is apprised through a comparison against the conventional `standard method' of lifetime estimation of the enameled wires. The best prediction accuracy is achieved by a logarithmic curve fit approach, which gives an error of 0.95% and 1.62% when its thermal index is compared with the conventional method and manufacturer claim respectively. The proposed approach provides a time-saving of 67% (83 days) when its computation time is compared with respect to the `standard method' of lifetime estimation.
Highlights
For safety-critical applications, like aircraft and marine, the electrical machines such as actuator motors and startergenerators) need to be highly reliable
To enhance the statistical significance of the predicted time-to-failure, the Standard in [23] recommends that the number of aging cycles must be considered between 10 and 20 or higher [23]. In this Standard, the thermal exposure is obtained by using (9), where TF is the aging temperature considered during the test procedure, Ti is the insulation thermal index, j is the number of desired aging cycles and 20,000 represents the life of insulation at thermal index as defined by the manufacturer
Time that has been saved using Curve Fitting (CF) model is 1680h and 312h at aging temperature of 250◦C and 270◦C respectively which saved about 1992h (83 days). This has resulted in a time-saving of 57.4% and 67.1% when the prediction was performed by the Neural networks (NN) and CF approaches respectively, compared to the conventional ‘standard method’ of lifetime estimation [3], [4]
Summary
For safety-critical applications, like aircraft and marine, the electrical machines such as actuator motors and startergenerators) need to be highly reliable. Khowja et al.: Lifetime Estimation of Enameled Wires Under Accelerated Thermal Aging Using Curve Fitting Methods marketplace analysis, signal modelling and power system load-forecasting. This is done by training them in a supervised manner [8] that can approximate any continuous function as closely as required [9]. The value of differential IC at which MTTF is reached is taken for other aging temperatures to predict the MTTF at 250◦C and 270◦C thermal exposures This method has shown to result in an overall time-savings of about 1560 hours which is about 68% for the presented case-study. N is the error signal for node in the output layer and can be represented by (5)
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