Real-time equipment condition assessment for a class-imbalanced dataset based on heterogeneous ensemble learning

Prognostics and health management (PHM) is beneficial for daily operation and maintenance [21]. PHM covers condition assessment, fault diagnosis, remaining useful life (RUL) prediction, maintenance decision and other considerations. Condition assessment is a fundamental activity to identify the current condition/state of equipment. Equipment ages and degrades with time. When equipment degrades to a certain degree or pass a certain threshold, it cannot operate well, which results in unqualified products, system breakdown or even casualties. Since equipment’s reliability and stability are meaningful for ensuring the safe and continuous operation, effective equipment condition assessment is an important prerequisite. Moreover, condition assessment could provide a convenience for several subsequent activities, such as condition-based maintenance, planning and scheduling [35, 39]. Condition assessment could be performed through either removing a component from operation (off-line) or doing online monitoring. Considering the cost and complexity of installation and removal, real-time condition assessment with continuous on-line monitoring is more economical and feasible. Overall, there are three major categories, (i) criteria-based approaches [36, 41], (ii) statistical-based approaches [3, 13, 18, 20], and (iii) data-driven approaches. In criteria-based approaches, health indicators (i.e. main functions, reliability degree, working time, and deterioration degree) are proposed [37] to evaluate equipment condition. But these approaches have difficulties on indicators quantifying and Xiaohui Chen Zhiyao ZhAng Ze ZhAng