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.

随着物联网和智能传感技术的兴起，预测和健康管理（PHM）系统收集了大量的监测数据。在许多行业中，根据监测数据预测机械部件的剩余使用寿命 (RUL) 一直是一项具有挑战性的任务，但准确确定 RUL 被认为是 PHM 系统最需要的结果之一。在本研究中，提出了一种用于 RUL 预测的多目标优化集成深度学习 (EDL-MO) 方法。结合准确性和多样性，设计了一种用于 RUL 预测的新型集成深度学习算法。通过引入多样性，在每次迭代中都会产生不相关的误差，并且通过进化深层网络来提高预测性能。所提出的 EDL-MO 采用进化优化来优化两个相互冲突的目标，即多样性和准确性。为了验证所提出的算法，在恒定负载下进行了轴承运行至故障实验。记录振动信号并利用所提出的 EDL-MO 方法以及其他现有方法进行性能比较来预测 RUL。分析了 EDL-MO 的有效性和优越性，其在预测旋转机械 RUL 方面优于现有算法。