Future works: application in an industrial case study

In order to prove and validate the benefits of such an approach in practice, an industrial case study must be performed. Although theory is important, the verification of the results with the aid of practical cases and case studies is indispensable. In this final section, it is going to be described how to implement an asset management approach with predictive maintenance and a possible method to analyze the results. The

application and the conclusions extracted from this industrial case might enhance the conclusions obtained in this project.

The predictive maintenance technology is scalable, and can be applied on a few or several machines in a plant [44]. When implementing PdM, two facts must be considered. First, the equipment must incorporate in its design functions, IT architecture, and capabilities that provide predictive maintenance within the scope of the equipment hardware and software operation. Secondly, the equipment must generate and make data available on all of the equipment functions and operations as well as performance and health metrics.

Equipment PdM data

To meet the vision of PdM, equipment is supposed to generate, use, and make available a wide variety of data. The data must include all the sources of data that are available, as well as new data. The pant must make the effort of implementing a suitable IT platform and technologies to be sure data coming from multiple sources can be acquired, processed, cleansed and analyzed.

KPI

Choosing the most appropriate KPI to prove maintenance benefits in the industry plant is indispensable. Equipment data come from a variety of sources that produce data of different types. KPI should be generated at the lowest level. The communication of these data must use standardized via communication protocols and interfaces.

Prioritizing Equipment Implementation

“Equipment should be analyzed to determine the PdM capabilities and target subsystems or modules that will provide the greatest return and improvement to equipment predictability and productivity” [47]. It is strongly recommended to establish data-driven PdM implementation plan that includes demonstrating and quantifying results.

Factory Implementation

“PdM implementation at the plant’s systems level is expected to take a larger, systemic view of equipment predictability and use. The factory systems should consider the effects of process sensitivities or other factors outside the equipment on performance, predictability, and productivity” [44]. The factory systems should use the PdM data and health metrics from the equipment as one set of inputs to PdM systems and analysis.

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