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Development of a software and a methodology for the detection of severe failures and diagnosis in bearings and gears through vibrational analysis (PHASE I)

Environmental regulations are increasingly demanding in relation to noise and vibration reduction, and to meet these requirements, one of the needs is to increase knowledge of vibrational analysis techniques. The difficulties, generally, are the failures that cause unplanned shutdowns of the machinery. In this context, it is necessary to predict possible failures to anticipate them and be able to plan their change "just before it fails", thus avoiding unplanned unemployment and in turn maximizing the use of components.

Vibrational Analysis is constantly expanding, especially due to its use for detecting faults and diagnosing machines.
In an industrial production system, unscheduled shutdowns must be avoided and one of the tools to avoid them is to detect system failures at their slightly initial stage or called incipient. Machines that transmit energy, generate mechanical vibrations and acoustic emissions that give information about the state of their internal components. Bearings and gears are mechanisms that are present in various industrial processes, so the detection of incipient failures in such elements is of special importance to avoid unplanned stops.
In this context, this work will focus on developing a software platform (Matlab) applying digital signal processing for fault detection and diagnosis of rotating equipment.

Project leader:
Grover Zurita, PhD