Optimization of design parameters in lstm model for predictive maintenance - Kim, Do Gyun; Choi, Jin Young (Applied Sciences (Switzerland), 2021)
Predictive maintenance conducts maintenance actions according to the prognostic state of machinery, which can be demonstrated by a model. Due to this characteristic, choosing a proper model for describing the state of machinery is important. Among various model-based approaches, we address an artificial intelligence (AI) model-based approach which uses AI models obtained from collected data. Specifically, we optimize design parameters of a predictive maintenance model based on long short-term memory (LSTM). To define an effective and efficient health indicator, we suggest a method for feature reduction based on correlation analysis and stepwise comparison of features. Then, hyperparameters determining the structure of LSTM are optimized by using genetic algorithm. Through numerical experiments, the performance of the suggested method is validated.
Keywords: Correlation analysis; Design parameters; Feature se-lection; Genetic algorithm; Long short-term memory; Maintenance; Predictive maintenance; Prognostics; Stepwise comparison