Periodic Reporting for period 3 - PACMAN (Prognostics And Computer Aided Maintenance)
Reporting period: 2019-10-01 to 2020-09-30
The key objective of the project was to demonstrate benefits of aircraft system prognostics and Computer Aided Maintenance solutions integrated into an airline End-to-End maintenance operational context. This was achieved by development of following innovative components:
• a novel prognostic architecture that includes both on-board and ground elements. The architecture was demonstrated using a Large Passenger Aircraft selected Aircraft System;
• specific prognostics capabilities such as data collection, data processing, symptom generation, failure mode identification and predictive trending;
• an advanced Augmented Reality mobile tools as speech recognition and Near-to-Eye displays that aids maintenance execution by bringing the necessary information directly to the engineer at remote repair sites.
The first work package “System prognostics” was aimed at research and development of the novel prognostics architecture and specific prognostics capabilities for selected aircraft system use cases for the prognostics architecture implementation and demonstration. Focus was given to application of physical model-based prognostics for two different cases. First, physical models were used to improve prognostics performance by compensation of aircraft system manufacturing variability and varying operation conditions. Second, physical models provided synthetized data for the prognostics development, validation and testing for the aircraft systems with limited availability of field data. Efficiency of the prognostics was demonstrated and evaluated after deployment on two aircraft system use cases including APU 131-9A hot section and engine Hydromechanical Unit Electrohydraulic Servo Valve failure prediction. The main result of the of the work package is Prognostics Prototype 2. The final demonstration prototype was built on the APU hot section use case. Honeywell Diagnostic Reasoner (HDR) tool was used to build the final demonstrator application. For the purpose, architecture of the HDR was enhanced to account for prognostic indicators generated by the prognostic algorithms and the system failure prediction was integrated into the HDR graphical user interface.
The second work package “Mobile Tools for Maintenance Execution Enhancement” addressed development of Augmented Reality mobile tools as speech recognition and Near-to-Eye displays that aid maintenance execution by bringing the necessary information directly to the maintenance engineer, e.g. electronic manuals and step-by-step instructions, relevant information and guidance right on time, and online help from (remote) experts. The main result of the work package is CAM demonstrator v3.0 that is smart glass-independent and can display required job-related information to its user (a maintenance technician). All information can be accessed and interacted with using several different modalities (both handsfree – speech commands or gestures – or tactile – HW buttons) that allow to control the demonstrator in any situation. This demonstrator was tested in real environment in TAP hangar, where it successfully helped a maintenance technician to replace a wing anti-icing valve; and also in a Honeywell internal facility, where it was being actively used for 5 months and resulted in an average time saving of more than 10% on a selected use case.
The third work package “Integration into Health Management System” was the major interface between project PACMAN and the higher-level Clean Sky 2 project ADVANCE. Prognostics Prototype 1 was developed to provide inputs to the integrated E2E maintenance concept demonstration in ADVANCE project. To assure compatibility with the ADVANCE demonstrator, the demonstration prototype was designed to shares prognostic results in defined XML format in agreed structure provided by ADVANCE project coordinator. The performance of the prognostic solution was verified with respect to the defined evaluation metric on the testing set of 20 APUs capturing various operators with various operating conditions.
Project results were disseminated at various international events including conferences, workshops and exhibitions:
• Annual conference of the prognostics and health management society, Scottsdale, Arizona, September 21 - 26, 2019
• ADVANCEd Aircraft Maintenance (ADVANCE project final dissemination event), Hamburg, 19th - 20th November 2019
• 2018 National Business Aviation Association - Business Aviation Convention & Exhibition
Exploitation of the project results encompasses following planned activities:
• Exploitation of system prognostic algorithms developed in PACMAN is planned in Honeywell commercial product called Honeywell Forge Connected Maintenance (https://aerospace.honeywell.com/.../connected-maintenance) which is a Honeywell connected aircraft solution for maintenance.
• Besides APUs, the model-based prognostics developed in PACMAN is planned to be exploited in Honeywell’s engine family as a part of new connected engine advance analytics services.
• There is an increasing interest in testing CAM both internally and externally by several other MROs. There are currently several negotiations in progress with these entities. Honeywell is also considering bringing key components from CAM demonstrator in its other (non-AR) software offerings.
• Further exploitation of the developed model-based system prognostics and CAM know-how is planned within future R&D projects under next EU (Clean Sky 3) and Czech national calls.
Reduction of operational disruption caused by unscheduled maintenance for the European legacy fleet and short-term derivatives
• Robust and reliable prognostic solution for aircraft systems developed in the project will allow significant reduction of unscheduled maintenance.
• Implementation of advanced set of CAM tools for maintenance execution enhancement will allow fast and efficient troubleshooting to minimize operational disruption times.
Integration of both system prognostic solutions and CAM tools will contribute to the maximization of assets utilization by means of:
• Executing maintenance actions only when it is needed with efficient maintenance schedule planning;
• Increasing A/C availability by reduction of operating disruption times;
• More effective maintenance processes utilizing advanced tools of CAM, i.e. replacement paper notes by electronic check lists, remote assistance to maintenance engineers etc.
Exploration of the impact of new services on the way of working for maintenance actors
• Advanced CAM tools are significant enabler for deployment of new maintenance services and concepts.
• The system prognostic solution will allow broad exploration of predictive maintenance planning that will lead to further optimization of the way of working.