Ensemble deep learning with multi-objective optimization for prognosis of rotating machinery

With the emerging of Internet of Things and smart sensing techniques, enormous monitoring data has been collected by prognostics and health management (PHM) systems. Predicting the Remaining useful life (RUL) of mechanical components from monitoring data has always been a challenging task in many industries, yet determining RUL accurately is identified as one of the most demanded outcomes of PHM systems. In this study, an ensemble deep learning with multi-objective optimization (EDL-MO) method is proposed for RUL prediction. A novel ensemble deep learning algorithm for RUL prediction is designed by combining accuracy and diversity. By introducing the diversity, uncorrelated error is produced in each individual iteration, and performance of prediction will be improved by evolving deep networks. The presented EDL-MO employs evolutionary optimization to optimize the two conflicting objectives, that is, diversity and accuracy. To validate the proposed algorithm, bearing run-to-failure experiments were carried out under constant load. The vibration signals are recorded and utilized to predict the RUL by using the proposed EDL-MO method, as well as other existing methods for performance comparison. The effectiveness and superiority of EDL-MO are analyzed, which outperforms the current algorithms in predicting RUL on rotation machineries.