Fault detection, isolation and optimal control have long been applied to industry. These
techniques have proven various successful theoretical results and industrial applications …

Challenging design problems arise regularly in modern fault diagnosis systems.
Unfortunately, the classical analytical techniques often cannot provide acceptable solutions …

An unappealing characteristic of all real-world systems is the fact that they are vulnerable to
faults, malfunctions and, more generally, unexpected modes of-haviour. This explains why …

This book is the third one in my book series plan. While the first two are dedicated to model-
based and data-driven fault diagnosis respectively, this one addresses topics in both model …

Over the last ten years, we have seen a significant increase in industrial data, tremendous
improvement in computational power, and major theoretical advances in machine learning …

The injection of an auxiliary input signal for active fault diagnosis may cause the change of
system control performance in closed‐loop operation. This paper presents a novel …

Correct and timely fault detection is of major importance in the field of system engineering,
and constitutes a primary problem in a broad spectrum of cases, from industrial processes to …

Historical data collected from processes are readily available. This paper looks at recent
advances in the use of data-driven models built from such historical data for monitoring, fault …

As representation scheme can severely limit the window by which the system observes its
world, deep learning for fault diagnosis is put forward in this paper. It is a real time online …

Early fault detection, both in equipment and the products in process, is of paramount
importance in industrial processes to ensure the quality of the final product, avoid abnormal …