Failure Part Mining Using an Association Rules Mining by FP-Growth and Apriori Algorithms: Case of ATM Maintenance in Thailand

Safety is an important concern for critical assets, such as aircrafts. E-maintenance strategies have long been explored for maintenance decision support and optimizing the operational availability of aircraft assets. Data-driven tools are an important influencer of day-to-day maintenance processes, which if optimally used may support practitioners to design more effective maintenance strategies. Recent trends show a correlation between e-maintenance strategies and enhanced use of data-driven tools for optimally managing technical assets. However, using data-driven tools for designing e-maintenance strategies is challenging because of aspects such as data-readiness and modelling-related challenges. This chapter presents a data-exploration approach for aiding root cause analysis of aircraft systems. The approach embeds algorithms for data preparation, text mining, and association rule mining and is validated in a use-case of maintenance of aircraft equipment, discussed in this chapter.

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Failure Part Mining Using an Association Rules Mining by FP-Growth and Apriori Algorithms: Case of ATM Maintenance in Thailand

Cited by 3 publications

References 9 publications

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