A Data-Driven Health Monitoring Method Using Multiobjective Optimization and Stacked Autoencoder Based Health Indicator

This article proposes a new data-driven health monitoring method, which uses multiobjective optimization and stacked autoencoder based health indicator. Specifically, the proposed method proposes an improved nondominated sorting genetic algorithm-II (NSGA-II) to perform multiobjective optimization on a large number of candidate features extracted from the sensor measurements. Then, a stacked autoencoder model is used to construct health indicators from the selected features. In the improved NSGA-II algorithm, the optimization goals of feature selection are defined as the minimum gap of health indicators between different states and the number of features. Comparisons between the proposed method and the state-of-the-art methods on simulation experiments show that the proposed method can accurately identify the status of the equipment and effectively limit the complexity of the diagnostic model.

Automated summary

This article introduces a new data-driven health monitoring method using multiobjective optimization and stacked autoencoder for health indicator construction. Through simulation experiments, the proposed method accurately identifies equipment status and effectively reduces the complexity of the diagnostic model.