Abstract
Condition monitoring has been widely employed to monitor critical components for drivetrains of machinery, whose malfunctioning will inevitably cause unexpected downtime and an increase of maintenance costs. This paper presents a practical condition monitoring technique, which uses a procedure consisting of a baseline definition process, similarity analysis collated with bathtub curve and maintenance decision-making support to enhance the reliability of the machinery. The proposed condition monitoring procedure features the benefits of being scalable and adaptable to multiple sensory technologies including Vibration, Acoustic Emission and Audible Acoustics. Validation of the convergence methodology is performed on a low-speed rotating mechanism. The outcome defines a process of baseline generation and identification of deviations from normal operating conditions.
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