Predictive maintenance has received considerable attention in the aviation industry where costs, system availability and reliability are major concerns. In spite of recent advances, effective health monitoring and prognostics for the scheduling of condition-based maintenance operations is still very challenging. The increasing availability of maintenance and operational data along with recent progress made in machine learning has boosted the development of data-driven prognostics and health management (PHM) models. In this paper, we describe the data workflow in place at an airline for the maintenance of an aircraft system and highlight the difficulties related to a proper labelling of the health status of such systems, resulting in a poor suitability of supervised learning techniques. We focus on investigating the feasibility and the potential of semi-supervised anomaly detection methods for the health monitoring of a real aircraft system. Proposed methods are evaluated on large volumes of real sensor data from a cooling unit system on a modern wide body aircraft from a major European airline. For the sake of confidentiality, data has been anonymized and only few technical and operational details about the system had been made available. We trained several deep neural network autoencoder architectures on nominal data and used the anomaly scores to calculate a health indicator. Results suggest that high anomaly scores are correlated with identified failures in the maintenance logs. Also, some situations see an increase in the anomaly score for several flights prior to the system’s failure, which paves a natural way for early fault identification.

Condition-Based Maintenance (CBM) is a policy that uses information about the health condition of systems and structures to identify optimal maintenance interventions over time, increasing the efficiency of maintenance operations. Despite CBM being a well-established concept in academic research, the practical uptake in aviation needs to catch up to expectations. This research aims to identify challenges, limitations, solution directions, and policy implications related to adopting CBM in aviation. We use a generalizable and holistic assessment framework to achieve this aim, following a process-oriented view of CBM development as an aircraft lifecycle management policy. Based on various inputs from industry and academia, we identified several major sets of challenges and suggested three primary solution categories. These address data quantity and quality, CBM implementation, and the integration of CBM with future technologies, highlighting future research and practice directions.

The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We identify new hazards associated with the introduction of data-driven technologies into aircraft maintenance using a structured brainstorming conducted with a panel of maintenance experts. This brainstorming is facilitated by a prior modeling of the aircraft maintenance process as an agent-based model. As a result, we identify 20 hazards associated with data-driven predictive aircraft maintenance. We validate these hazards in the context of maintenance-related aircraft incidents that occurred between 2008 and 2013. Based on our findings, the main challenges identified for data-driven predictive maintenance are: (i) improving the reliability of the condition monitoring systems and diagnostics/prognostics algorithms, (ii) ensuring timely and accurate communication between the agents, and (iii) building the stakeholders’ trust in the new data-driven technologies.

We investigate the performance of three different data-driven prognostic methodologies towards the Remaining Useful Life estimation of commercial aircraft brakes being continuously monitored for wear. The first approach utilizes a probabilistic multi-state deterioration mathematical model i.e. a Hidden Semi Markov model whilst the second utilizes a nonlinear regression approach through classical Artificial Neural Networks in a Bootstrap fashion in order to obtain prediction intervals to accompany the mean remaining life estimates. The third approach attempts to leverage the highly linear degradation data over time and uses a simple linear regression in a Bayesian framework. All methodologies, when properly trained with historical degradation data, achieve excellent performance in terms of early and accurate prediction of the remaining useful flights that the monitored set of brakes can safely serve. The paper presents a real-world application where it is demonstrated that even in non-complex linear degradation data the inherent data stochasticity prohibits the use of a simple mathematical approaches and asks for methodologies with uncertainty quantification.

By anticipating impending failures and addressing those with preventive replacements, Condition-Based Maintenance (CBM) can provide several economic benefits to aircraft operators. While Prognostics and Health Monitoring (PHM) methods are widely available, scheduling of tasks originating from those methods is a relatively new challenge. To avoid incurring extra cost and ground-time, these maintenance tasks are typically scheduled during already existing (conventional) maintenance slots such as periodic checks. Following this strategy, an aircraft component would have to be replaced if a failure precursor is detected that is expected to result in failure between the next two checks. The decision following from that detection depends on the chosen decision threshold of the prognostic or diagnostic model, which can be represented by a point on the Receiver Operating Characteristic curve. Selecting the optimal operating point for each maintenance check is challenging, as it depends on the age-dependent reliability of the component, the performance of the prognostic (diagnostic) model, the interval of the periodic check and the cost of (corrective and preventive) maintenance. This paper presents an innovative method for selecting optimal operating points for all periodic checks throughout the lifetime of an aircraft component. This is done by means of a numerical optimization model that finds operating points that minimize the component’s total maintenance cost per flight hour. A case study on a compressor of a wide-body aircraft is presented, which shows that by using this method, additional economic value from existing PHM can be realized without the need for additional investments.