Towards the implementation of a predictive maintenance strategy: A case study within a waste processing plant

Owen Gebler, Ben Hicks, Andrew Harrison, Matt Barker, and Pete Stirling
Submission Type: 
Full Paper
phmec_16_026.pdf424.23 KBJuly 11, 2016 - 2:10am

Successful implementations of predictive approaches to maintenance are seeing an increasing level of reporting in literature. While much of this relates to high value manufacturing industries, such as Aerospace, the potential of these maintenance approaches within low-value processing industries, such as Waste Management, where system availability is also critical, is receiving increasing interest. However, these industries vary significantly in terms of the asset volumes handled, life costs, safety criticality and the sophistication of equipment/plant. Consequentially, the implementation of predictive maintenance in these contexts is likely to present different requirements. In order to understand the potential differences, this paper seeks to explore the potential issues/challenges for predictive maintenance in this context through a qualitative study of maintenance personnel within the Waste Management industry and reflections on the implementation o f a prototype condition monitoring system. The findings from these two aspects provide the basis for elicitation of a set of potentially generalizable issues for the processing industries, which it is proposed must be addressed before successful implementation of the technology can be realised. The main issues highlighted with predictive maintenance implementation within Waste Management plant concern industry characterisation, socio-technical and technical considerations, perceptions of value in predictive maintenance, measurement of maintenance performance and barriers to both implementing and realising beneficial and sustainable success of predictive maintenance.

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Submission Keywords: 
predictive maintenance
Data Acquisition
test rig development
Submission Topic Areas: 
CBM and informed logistics
Component-level PHM
Data-driven methods for fault detection, diagnosis, and prognosis
Industrial applications
Model-based methods for fault detection, diagnostics, and prognosis
Systems and platform applications
Verification and validation
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