Fault Detection and Diagnosis Methods for Sensors Systems: a Scientific Literature Review

Abstract

Innovation technology in industrial devices allows enhancing process monitoring and control and have a significant impact on communication and interfacing systems within all production contexts, both in the process industries and in the manufacturing sector. Asset status and performances can be monitored by traditional sensors and intelligent devices, wired and wireless, able to collect data and processing them or make them available to specific processing algorithms. Reliability and maintenance researchers and practitioners are focusing on models, tools, and techniques for in field and real-time detection of process anomalies and equipment fault detection. Most analyses start from the assumption that sensors and monitoring systems guarantee reliability and operational availability and that they are always able to provide timely and correct data. Therefore, malfunctions of such devices could affect trust on data-driven analysis and related decision-making process on asset management and workers’ safety. In recent years, different approaches were explored, aimed at recognizing failure mechanisms in industrial measuring devices and sensors systems, both using traditional methodologies or implementing intelligent algorithms, to accurately prevent and predict the anomalies and identify their nature and position before they generate malfunctioning or interruption of plant's operations. This paper proposes a systematic analysis of the scientific literature related to fault/failure detection and diagnosis in sensors and monitoring systems, to obtain an updated state-of-the-art and identify the most promising approaches and research challenges on this topic. A particular focus is dedicated to the usability of soft sensors and artificial intelligence algorithms, in data-driven models.