The increase of productivity and decrease of production loss is an important goal for modern
industry to stay economically competitive. For that, efficient fault management and quick …

Since the introduction of the industry 4.0 paradigm, manufacturing companies are investing
in the development of algorithmic diagnostic solutions for their industrial equipment, relying …

Fault diagnosis and prognosis in mechanical systems have been researched and
developed in the last few decades at a very rapid rate. However, owing to the high …

Artificial intelligence applications are increasing due to advances in data collection systems,
algorithms, and affordability of computing power. Within the manufacturing industry, machine …

In the era of Industry 4.0, highly complex production equipment is becoming increasingly
integrated and intelligent, posing new challenges for data-driven process monitoring and …

Artificial intelligence, big data, machine learning, cloud computing, and Internet of Things
(IoT) are terms which have driven the fourth industrial revolution. The digital revolution has …

Automated fault detection is an important part of a quality control system. It has the potential
to increase the overall quality of monitored products and processes. The fault detection of …

In large-scale industrial processes, fault diagnosis is of paramount importance, as faults
jeopardize the stability and performance of processes. However, effective fault diagnosis …

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 …

The recent advancements in the fields of artificial intelligence (AI) and machine learning
(ML) have affected several research fields, leading to improvements that could not have …