Fault detection and diagnosis for process plants has been an active area of research for
many years. This review presents a concise overview on supervised and unsupervised data …

The use of robots in our daily lives is increasing. Different types of robots perform different
tasks that are too dangerous or too dull to be done by humans. These sophisticated …

The building industry consumes the most energy globally, making it a priority in energy
efficiency initiatives. Heating, ventilation, and air conditioning (HVAC) systems create the …

Permanent magnet synchronous motor and power electronics-based three-phase inverter
are the major components in the modern industrial electric drive system, such as electrical …

The fault diagnosis of gas turbines plays a vital role in engine reliability and availability. The
data-driven diagnostic model has been verified useful for identifying and characterizing …

For complex industrial processes with multiple operating conditions, the traditional
multivariate process monitoring techniques such as principal component analysis (PCA) and …

A dynamic Bayesian network (DBN) based fault detection, root cause diagnosis, and fault
propagation pathway identification scheme is proposed. The proposed methodology …

A generic intelligent fault detection and diagnosis (FDD) strategy is proposed in this study to
simulate the actual diagnostic thinking of chiller experts. A three-layer Diagnostic Bayesian …

Among the greatest challenges in soft sensor development for bioprocesses are variable
process lengths, multiple process phases, and erroneous model inputs due to sensor faults …

Abnormal events of varying magnitudes result in incipient faults, near-misses, incidents, and
accidents in chemical plants. Their detection and diagnosis has been an active area of …