Lead-acid battery maintenance using multilayer perceptron models
Predictive maintenance of components has the potential to
significantly reduce costs for maintenance and to reduce unexpected
failures. Failure prognostics for heavy-duty truck lead-acid batteries
is considered with a multilayer perceptron (MLP) predictive
model. Data used in the study contains information about how
approximately 46,000 vehicles have been operated starting from the
delivery date until the date when they come to the workshop. The model
estimates a reliability and lifetime probability function for a
vehicle entering a workshop. First, this work demonstrates how
heterogeneous data is handled, then the architectures of the MLP
models are discussed. Main contributions are a battery maintenance
planning method and predictive performance evaluation based on
reliability and lifetime functions, a new model for reliability
function when its true shape is unknown, the improved objective
function for training MLP models, and handling of imbalanced data and
comparison of performance of different neural network
architectures. Evaluation shows significant improvements of the model
compared to more simple, time-based maintenance plans.
Sergii Voronov, Erik Frisk and Mattias Krysander
2018
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