Predictive Maintenance based on Log Analysis: A Systematic Review

Abstract

In today’s industries, the Maintenance process of machines and assets implies a significant part of the total operating cost. Many efforts have been made to reduce this cost by optimizing the process and evolving methods that allow information collection on equipment status, avoiding redundant interventions, and predicting the exact moment to perform a maintenance intervention. Using “intelligent” systems that collect data from the operation and remote management systems allows us to gather all the data and apply some methodologies capable of identifying expected behaviors based on past operations. We present a survey of technologies, techniques, and methodologies to give the knowledge background to develop a framework to minimize the occurrence of failures and optimize the process of Predictive Maintenance (PdM) based on the analysis of Log files collected from the various industrial equipment. Generally, these logs contain many records, and many of these records do not directly contribute to evaluating the operation’s machine status. Most of the studies included in this survey use machine learning techniques and focus a significant part of their research on data preprocessing, uniformization and clarification.