An ensemble-learning model for failure rate prediction

In the Industry 4.0 era, Preventive Maintenance (PM) is still an attractive solution to prevent breakdowns and failures and to reduce maintenance and failure costs. A PM program is part of both the Total Productive Maintenance (TPM) philosophy and the Reliability Centered Maintenance (RCM) process. A prerequisite to carry out effective PM activities is the availability of a reliable estimate of the equipment failure rate. Assessing it may be a hard task, as it requires analysing a large set of maintenance data, which includes both quantitative and qualitative variables. To this aim, it is possible to exploit advanced data analysis techniques that permit extracting information and knowledge from big datasets. This paper presents an ensemble-learning model to estimate the failure rate of equipment subject to different operating conditions. At the same time, the method permits to identify the most important working parameters affecting the failure rate. An industrial application is considered to show the potentialities and the effectiveness of the proposed method. In particular, a sample of 143 centrifugal pumps installed in an oil refinery plant is analysed