Challenges in predictive maintenance – A review

Abstract

Predictive maintenance (PdM) aims the reduction of costs to increase the competitive strength of the enterprises. It uses sensor data together with analytics techniques to optimize the schedule of maintenance interventions. The application of such maintenance strategy requires the cooperation of several agents and involves knowledge and skills in distinct fields, since it encompasses from the averaging of relevant signals in the shop-floor to its processing, transmission, storage, and analysis in order to extract meaningful knowledge. PdM is a broad topic, making it impossible to address all its subtopics in the same paper. Having this into consideration, this paper focuses on the main challenges that hinder the development of a generalized data-driven system for PdM, namely: the existence of noisy or erroneous sensor data in a real industrial environment; the necessity to collect, transmit and process high volumes of data in a timely manner; and the fact that current approaches for PdM are specific for a part or equipment rather than global. This paper connects three different perspectives: anomaly detection, which allows the removal of noisy or erroneous data and the detection of relevant events that can be used to improve the prognostics methods; prognostics methods, which address the models to forecast the condition of industrial equipment; and the architectures, which may allow the deployment of the anomaly detection and prognostics methods in real-time and in different industrial scenarios. Furthermore, the last trends, current challenges and opportunities of each perspective are discussed over the paper.