Abstract numerous diagnostic techniques targeted at increasing electrified drive powertrains
system (EDPS) dependability and durability have been proposed, motivated by the growing …

This paper provides an overview of the recent advances made in the field of fault diagnosis
of industrial machines operating under variable speed conditions. First, the shortcomings of …

This paper addresses the issue of artificial visual inspection being overly reliant on
subjective experience and the difficulty for the human eye to accurately identify dense and …

Detecting bearing faults is very important in preventing non-scheduled shutdowns,
catastrophic failures, and production losses. Localized faults on bearings are normally …

Fault detection is a crucial step for the safe operation of autonomous vehicles. Failure to
detect faults can result in component failure leading to the breakdown of the car or even …

Machine learning based fault diagnosis schemes have been intensively proposed to deal
with faults diagnosis of rotating machineries such as gearboxes, bearings, and electric …

Multiple fault identification in induction motors is essential in industrial processes due to the
high costs that unexpected failures can cause. In real cases, the motor could present …

This paper presents a novel procedure to detect the most frequent faults in inverter-fed
induction motors, ie stator short circuits and bearing defects, even in case of simultaneous …

Online condition monitoring and fault diagnosis systems are necessary to prevent
unexpected downtimes in critical electric powertrains. The machine learning algorithms …

This paper introduces a novel methodology for training Convolutional Neural Network
models to detect multiple faults in permanent magnet synchronous motors (PMSMs). The …