In the field of predictive maintenance (PdM), machine learning (ML) has gained importance
over the last years. Accompanying this development, an increasing number of papers use …

The monitoring of rotating machinery is an essential task in today's production processes.
Currently, several machine learning and deep learning-based modules have achieved …

In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

This survey explores applications of explainable artificial intelligence in manufacturing and
industrial cyber–physical systems. As technological advancements continue to integrate …

Intelligent fault diagnosis (IFD) has experienced tremendous progress owing to a great deal
to deep learning (DL)-based methods over the decades. However, the “black box” nature of …

Due to the growing interest for increasing productivity and cost reduction in industrial
environment, new techniques for monitoring rotating machinery are emerging. Artificial …

Anomaly detection for industrial diesel generators, in which unexpected faults could lead to
severe consequences, is still challenged due to their complex structure and nonstationary …

Analyzing vibration data using deep neural networks is an effective way to detect damages
in rotating machinery at an early stage. However, the black-box approach of these methods …

The use of 3D information in industrial anomaly detection tasks has been shown to enhance
performance by uncovering unseen abnormal patterns in the RGB modality. Despite the …

With the proliferation of image-based applications in various domains, the need for accurate
and interpretable image similarity measures has become increasingly critical. Existing …