To fulfil the main ReMAP objective, six main technical goals were defined. These technical goals are listed below, with a summary of the work carried out towards their achievement.
Objective 1 – develop an integrated approach for CBM
The integration work has been developed in the last period. Eight PHM solutions and a couple of maintenance planning models were integrated into the IT platform. This integration was tested and validated in the successful 6-month Demonstration Exercise.
Objective 2 − explore and optimize the use of different sensing technologies for structural health management (SHM).
The work performed involved the design, the specifications definition, the manufacture, and preliminary tests of four sensing technologies to be used for SHM. In particular, the equipment of 40 L1 (level 1) coupons and 8 L2 (level 2) coupons with piezoelectric elements, optical fibre sensors, and acoustic emissions sensors, together with the associated software for laboratory tests.
Objective 3 − develop data-driven probabilistic algorithms for aerostructures damage monitoring (diagnosis) and remaining useful life (RUL) estimation (prognosis)
An unprecedented lab test campaign was performed during the project. Several diagnostic methodologies targeting the various levels of SHM (i.e. detect, locate, quantify damage) were developed and successfully tested in lab-scale scenarios (details in several ReMAP publications). Anomalies are detected with success rates from 95.2% to 100%, based on methodologies leveraging strain readings with optical fibre or distributed sensing, acoustic emissions, or lamb waves. Results from Lamb wave testing show up to 10% of the Mean Absolute Percentage Error in the localization and up to 15% in the sizing of impact damage or disbond in the skin/stringer interface.
Objective 4 − develop a hybrid approach, combining machine-learning-based data analytics algorithms and physics-based models for diagnostics, prognostics and health management (PHM) of dissimilar aircraft systems.
The consortium developed data-driven PHM algorithms that have been trained and tested in 10 aircraft systems from four aircraft types. Together, one conceptual algorithm using the Federated Learning concept takes advantage of the platform architecture. An initial physics-based model concept was developed to improve route cause analysis of failure prediction from data-driven methods. Develop user interfaces to interpret the results obtained by some of the developed algorithms.
Objective 5 − develop an efficient maintenance packaging and schedule optimisation algorithm for real-time adaptive fleet maintenance management.
The consortium has developed a set of machine-learning algorithms, and scheduling models were developed to produce fast schedules. In addition, a prototype graphical user interface (GUI) was developed to help maintenance schedulers to interpret the prognostics and the schedule solutions. Results show that the ground time for maintenance can be reduced by 20%.
Objective 6 − develop a quantitative safety risk assessment methodology for CBM.
An agent-based model of the maintenance process was produced. Using stochastic simulations, the produced model allows the simulation of maintenance strategies and computes safety indicators, such as the probability of undesired events. Furthermore, a case study considering the break wear was performed.
