Component-Based Data-Driven Predictive Maintenance to Reduce Unscheduled Maintenance Events

Costs associated with unscheduled and preventive maintenance can contribute significantly to an airline's expenditure. Reliability analysis can help to identify and plan for maintenance events. Reliability analysis in industry is often limited to statistically based approaches that incorporate failure times as the primary stochastic variable, with additional strict assumptions regarding independence of events and underlying distributions of failure phenomena. This foregoes the complex nature of aircraft operations, where a whole range of operational factors may influence the probability of occurrence of a maintenance event. The aim of this research is to identify operational factors affecting component reliability and to assess whether these can be used to reduce the number of unscheduled occurrences (i.e. failures). To do so, a data-driven approach is adopted where historical operational and maintenance data is gathered and analysed to identify operational factors with a measurable influence on maintenance event occurrence. Both time-independent and time-dependent Proportional Hazard Models (PHMs), models which incorporate operational factors as covariates, are employed to generate reliability estimates. Results obtained from analysing historical data of a set of ten components with respect to unscheduled removals indicates that adopting new maintenance schedules, derived from the proposed reliability models, could reduce the number of unscheduled occurrences by approximately 37%. The potential benefits of adopting the proposed strategy are extensive. Nonetheless, numerous assumptions have been introduced to overcome challenges imposed by the complex nature of the data. To overcome these challenges, recommendations are made for future development of the proposed approach.