Implementation of a proper maintenance strategy plays an important role in increasing the overall efficiency of plants in industries. Naturally, the faults in assets may occur with age-related or random (independent of the asset age) failure rates [1]. It should be noticed that time-based strategies such as preventive maintenance (PM) could not deal with the random nature faults, appropriately. In contrast, the condition-based maintenance (CBM) strategy can manage the addressed faults by analyzing the historical information and monitored data.
Basically, the CBM procedure includes three main steps; condition monitoring, diagnostics, and prognostics. The condition monitoring deal with anomaly detection of the machine. In diagnostics, the degradation element or the source of the fault in the machine is discovered. In prognostics, the condition monitoring data is utilized to predict the remaining useful life (RUL) of the components. The confidence level (CL) estimation of predictions in the aforementioned steps is important for decision making and planning the maintenance activities.
The rolling element bearings (REBs) are the most widely used components in the rotating machinery, which their failure is the cause of almost 50% of the machine break down [2]. Therefore, from the reliability point of view, REBs are considered the critical components and their appropriate functionality lead to availability increase in the rotating machinery. On the other hand, REBs are usually degradate gradually and the defects in the elements of the REBs generate specific indicators in the vibration signals. It makes it possible for predicting degradation and RUL of them and many researchers have developed various approaches to this aim. Developing software for analyzing the vibration condition monitoring (VCM) data for diagnostics as well as predicting the RUL of REBs is interesting and applicable for industrial maintenance teams.
[1] K. Knutsen, G. Manno, and B. Vartdal, "Beyond Condition Monitoring in the Maritime Industry," DNV GL Strategic Research & Inovation Position Paper, 2014.
[2] A. Rai and S. Upadhyay, "A Review on Signal Processing Techniques Utilized in the Fault Diagnosis of Rolling Element Bearings," Tribology International, vol. 96, pp. 289-306, 2016.