A Contextual Model for the Industrial Machine Failure Prediction

Failure prediction of industrial machinery remains a concern for numerous researchers and practitioners. In this research, the improvement of failure predictions was highlighted by including contextual factors due to the fact that machine operation is a socio-technical process influenced not only by historical data but also by machine operating conditions, maintenance policies, and environmental conditions, etc. The paper proposes a contextual framework for the prediction of failure, which involves the application of various quantitative and qualitative methods, such as statistical analysis, time-series, machine learning algorithms, interviews, and survey questionnaires. The results of the application of the model indicate that it is possible to obtain quantitative estimates of the time between failures (TBF) and the downtime (DT) by applying the aggregated expert assessments of contextual factors. The key findings of the research indicate that the development of TBF and DT prediction models, in a contextual sense, represents an important segment for the further planning of maintenance of machine systems.