A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant

Abstract

Power plants are required to supply the electric demand efficiently, and appropriate failure analysis is necessary for ensuring their reliability. This paper proposes a framework to extend the failure analysis: indeed, the outcomes traditionally carried out through techniques such as the Failure Mode and Effects Analysis (FMEA) are elaborated through data-driven methods. In detail, the Association Rule Mining (ARM) is applied in order to define the relationships among failure modes and related characteristics that are likely to occur concurrently. The Social Network Analysis (SNA) is then used to represent and analyze these relationships. The main novelty of this work is represented by support in the maintenance management process based not only on the traditional failure analysis but also on a data-driven approach. Moreover, the visual representation of the results provides valuable support in terms of comprehension of the context to implement appropriate actions. The proposed approach is applied to the case study of a hydroelectric power plant, using real-life data.

Keywords

• maintenance
• hydroelectric power plant
• reliability
• data-driven
• association rule
• data mining