Maintenance Action Recommendation Using Collaborative Filtering
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Abstract
The problem we were trying to solve in 2013 PHM Society Conference Data Challenge competition 1 is closely related to remote monitoring and diagnostics in industrial applications. This data was generated from an industrial piece of equipment with a sensor network to measure several parameters and an onboard condition monitoring system. The measured data goes through a control logic in order to monitor the equipment’s operating regime. At any time instant when some of these parameters meet a specific condition, the control system generates an unique event id/code. Each case is described by a set of event codes which characterize the atypical operating condition of the equipment. Some of these cases with specific event code combinations may be operationally significant and could be indicative of “Problem Types”, some of which are assumed to be known to the subject matter experts. As a response to these problems, domain experts recommend appropriate diagnostic measures (or maintenance actions) depending on the problem types. The goal of this data competition is to build an automated system that can recommend particular maintenance action(s) to mitigate these problem(s).
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filtering, diagnostics and prognostics, PHM, monitoring
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