Intelligent Bearing Fault Diagnosis Using Artificial Neural Networks and IoT for Maintenance 4.0 Implementation

The importance of monitoring and locating faults, and detecting potential problems in industrial systems, has become increasingly critical in modern industry to minimize breakdowns, and unplanned downtime, and maximize production efficiency. The design and implementation of 4.0 technologies for detecting and monitoring rotating machinery have attracted increased interest from industrial researchers, particularly with the advent of processing algorithms and the digitization of processes. In this context, Maintenance 4.0 has emerged as an innovative approach to improve energy efficiency, productivity, reduce downtime and maintenance costs, and increase the useful life of the equipment. The proposed solution involves the integration of a technology ecosystem, including artificial intelligence, the Internet of Things, and other advanced technologies, to transition toward Maintenance 4.0. To validate the reliability of this proposed method, we conducted laboratory experiments on a rotating machinery with several defects. The obtained results encouraged us to pursue a technological transfer to the future industry.