From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis

TLDR

Fault detection and diagnosis (FDD) systems rely on data processing, information redundancy, and both explicit and implicit human understanding through modeling, signal processing, and intelligence computation. The paper reviews FDD techniques within a unified data‑processing framework to provide a comprehensive overview and future outlook for industrial automation. FDD methods are classified into model‑based online data‑driven, signal‑based, and knowledge‑based history data‑driven categories according to data type and processing. The review highlights future directions for FDD, including hybrid and networked approaches, as key developments in industrial automation.

Abstract

This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a data-processing system on the basis of information redundancy, in which the data and human's understanding of the data are two fundamental elements. Human's understanding may be an explicit input–output model representing the relationship among the system's variables. It may also be represented as knowledge implicitly (e.g., the connection weights of a neural network). Therefore, FDD is done through some kind of modeling, signal processing, and intelligence computation. In this paper, a variety of FDD techniques are reviewed within the unified data-processing framework to give a full picture of FDD and achieve a new level of understanding. According to the types of data and how the data are processed, the FDD methods are classified into three categories: model-based online data-driven methods, signal-based methods, and knowledge-based history data-driven methods. An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are also presented to reveal the future development direction in this field.