Development of software and a methodology for the detection of severe faults and diagnosis in bearings and gears through vibrational analysis (PHASE 2)
In the industry, unscheduled stops must be avoided and one of the tools to avoid is monitoring and diagnosing failures slightly at initial or so-called incipient stages. The machines transmit energy generating mechanical vibrations and acoustic emissions, that give information on the state of their internal components. The bearings and gears are mechanisms that are present in various industrial processes, so the detection of incipient and severe failures in such elements are of special importance to avoid unplanned stops.
The present research work intends to continue with the second phase of the project with "Development of a software and methodology for the detection of severe faults and diagnosis in bearings and gears through vibrational analysis". Additionally, it is intended to develop software for classifying faults of gears and bearings, through vibrational analysis and artificial intelligence methods. The application of artificial intelligence in the subject of predictive maintenance of rotating machines, leads to a new dimension of accuracy of classification failures of industrial systems.
In this context, this work will focus on developing a software platform (Matlab) by applying digital signal processing to detect faults and diagnose of rotating equipment.
Grover Zurita, PhD