Case Based Model to enhance aircraft fleet management and equipment performance

Transcription

1 Case Based Model to enhance aircraft fleet management and equipment performance A. BEN ZAKOUR (a), E. RANDRIA (b) (a) University of Bordeaux, France (LaBRI) 2MoRO Solutions Bidart, France, (+33) (b) 2MoRO Inc., Montreal, Canada, 2MoRO Solutions Bidart, France, Abstract Several constraints and seven transformative trends dictate the design and the maintainability of future aircraft including unmanned aerial vehicles (UAVs) such as reducing design, maintenance and exploitation costs as well as optimizing aircraft availability without neglecting flight security, safety and the environment [Brown 07]. To meet business and ecological needs, we propose innovative models mainly based on the exploitation of in-service data to aircraft/engine/equipment manufacturers as well as maintenance centers. We are now working on retrieving data form heterogeneous information systems with aircraft manufacturers to improve our models on a large scale. Introduction In 2018, the Maintenance Repair and Overhaul (MRO) market is planned to reach $140 billion [Stewart 09] and [Michaels 08]. To gain market share, aircraft manufacturers will have to differ from concurrence by offering to their customers the way to fly more with the cheapest maintenance. Then, while considering the development of a new aircraft program and the duration of the aircraft in service phase (circa 30 years), manufacturers tend to look for the following values: Significantly shorten the development cycle to reduce costs and remain at the state of the art from the time between program initiation to the delivery of the first operational unit Reducing pollution for community acceptability Limit aircraft configurations or find a reliable way to track and trace configurations (as-design, as-built, as-maintained and as-flying) to obtain some degree of standardization among all customers and then reduce costs The supply of a vast and effective system for reducing aircraft downtime as well as supplying parts, technical assistance and training to aircraft operators Providing authorities with in time relevant data for the certification process Providing aircraft community with collaborative aircraft to learn from the others These values can be produced by retrieving and exchanging relevant information between all aircraft lifecycle stakeholders. Hence, manufacturers can obtain the return of experience from in service data to optimize the design, reduce costs and increase community acceptability. Proven technologies from predecessor programs can also be inherited to shorten the development cycle significantly. Moreover, ensuring the link between the design and in-service data can assist manufacturers in monitoring aircraft configurations and supply the right resources at the right places with minimum costs. 1

2 Considering this context, our innovative approach is based on heterogeneous data linked to the asset (maintenance manuals, on-board data, logbook, etc.) to generate added-value information to promote the enhancement of productivity (during design, development and in-service phases) and achieve significant levels of program cost rationalization. Our approach aims to take the best from several kinds of data to capitalize profitable feedback information to enhance fleet service and aircraft/engine/equipment design. Data used by our approach are described in Data description section. Then, Data organization section explains how data are handled and structured within databases. At last, Wear model usage section details how models are designed based on data mining to create value for aircraft/engine/equipment manufacturers as well as maintenance centers. Data description Firstly, the kinds of handy information are described. Then, the interaction and connection between those heterogeneous data and the main information mixture that creates worthwhile value are detailed. Heterogeneous data description Data handled is grouped by aircraft of same kind. It handles both historical aircraft in-service data and design referential data. Those different information types are described below: Aircraft Maintenance Manual data (AMM) are produced by manufacturers; it describes the frequency and conditions to apply precautionary maintenance tasks during asset life (characterized by the asset usage through counter's values growing). Tasks are described in details and linked to a set of instructions. The set of tasks is organized according to the complete aircraft part breakdown. This structure is available through the manufacturer technical data book, called Illustrated Parts Catalogue (IPC), also produced by manufacturers and complying with ATA Spec standard. It represents hierarchical tasks tree describing the different parts of the aircraft. The root node illustrates the entire aircraft when the leaf's nodes illustrate single task maintenance that can be performed to specific equipment. Majority of leaf's maintenance tasks are associated to a part and all of them have a frequency of execution. This frequency is established in hours or in number of flight cycles. In-service data are specific for each aircraft usage. It describes a set of flights by detailing both take-off and landing airports, flight duration, percentage of vehicle loading and fuel consumption. In-service data include also the maintenance tasks performed during the life of the vehicle: each performed task is linked to the associated equipment's part number (PN), to the equipment's counter values and to the whole aircraft's counter values. Relation between heterogeneous data The figure 1 below describes the different heterogeneous data that can be handled through in-service and reference data collection. The connection between data is performed chronologically and corresponds to aircraft status, flights, mission conditions (i.e. geographical zones where corrosion may appear, weather, etc.), maintenance tasks real execution (represented by white triangles) and scheduled maintenance tasks (represented by grey triangles). The gap between the two kinds of triangles in the graph represents the time lag between the planned time of a maintenance task and the time of its real execution. This time gap can be interpreted as an early wear of the part for which the maintenance task has to be performed earlier. Our work aims to build aircraft equipment wear model according to fleet in-service data. Actually, in aircraft maintenance, reaper and overhaul operations (MRO), each maintenance action is defined for a specific wear or disease caused on a particular equipment identifier (PN). According to this principal, we 2

3 can infer that possible correlation between equipment kind of usages and failure/ wear models can be considered as a correlation between equipment kind of usages and maintenance task associated to the failure/wear. The following section shows how historical raw data are organized to associate to each aircraft part subpart or equipment it's "specific" in-service data. Specific in-service data for an aircraft component are its usages between a couple of maintenance, reaper or replacement actions. Figure 1 Chronological alignment of in-service and reference data Data Organization According to the concept described below, our approach defines operational data structural granularity organization. It handles a hierarchy that allows organizing data according to the whole aircraft structure, to its different parts, sub-parts and terminal equipment. To implement this hierarchy, we adopt the standard manufacturers structure described in the AMM that strictly describes the material aircraft configuration. We add a down level that depicts distinctness of maintenance tasks execution recommendations. The choice of such a structure is motivated by the necessity of enabling a perfect compatibility between manufacturer distribution and fleet in-service data. Figure 2 shows an example of hierarchy according to a simplified ATA standard. 3

4 Figure 2: Aircraft hierarchy structure The appended level, identify criticality rushes of maintenance tasks executions. Effectively, from the different behaviours extracted by comparing in-service and reference data, we can bring out three kinds of maintenance task executions with regards to the time gap between its real execution and its planned time. These groups are identified by the criticality threshold and are: the high criticality group, the middle criticality group and the less criticality group. To each group, our approach associates a serial set of inservice data extracted from all same type aircraft of the fleet including: Data associated to maintenance task performed with a high criticality in the high criticality group. Data associated to maintenance task performed with a medium criticality in the medium criticality group. Data associated to maintenance task performed in the planned delay in the less criticality group. Each node of the tree structure contains in-service data occurred before each execution of a maintenance task or a group of a maintenance task performed to aircraft's parts described by the node. Extracting frequent behaviours is interesting and very useful to build wear model on each node. If a group of behaviours occurs several times on several aircraft (of the same kind) before the execution of maintenance task(s), a logical correlation between corresponding equipments wear and those behaviours can be established. Then, it becomes interesting to build for maintenance tasks a based wear model that provides kind of behaviours that cause corresponding wear and/or diseases. Those models are built by extracting frequent historic from in-service data. Patterns extraction is used in several domains to identify several kind of machine behaviours like in [Rabatel 06] for trains and in [Hirate 06] for astronomic robots. 4

5 Extraction method is adapted to extract complete patterns that represent relevant and worthwhile data facets taking into account the uselessness of rigorous succession of extracted events, the time configuration and its granularity and the importance of the execution of the maintenance tasks. Wear model usage Wear models are built on each node; they characterizes the kinds of usage that cause the execution of maintenance tasks with regards to the granularity of hierarchy level. Information are provided on time intervals sequences, it gives information about the chronological succession of events and specify time gaps between them. The use of interval temporal patterns defined in [B.Zakour 10] allows relaxing classical temporal patterns extraction to take into account the high data level. These models help in anticipating maintenance tasks execution according to their historic of usage: mission conditions, flights, reports and aircraft configuration, to their usages conditions: flight configuration, aircraft configuration, weather conditions, etc. On tree s leafs, model associates frequent usages to specific criticality of maintenance task execution. However, model extracted from intermediate nodes identifies typical uses that match with different maintenance tasks executed to the part concerned by the node. In the same way model of the root node extracts typical uses that can be associated to all maintenance tasks concerning aircraft. The aim of our method is to provide usage based models that can have a dual usefulness: First a contextualized and precise management of aircraft maintenance tasks, second a fleshed out model of wear and disease of aircraft equipment. For the aircraft fleet management the aims of frequent sequences extracted is to prognostic critical and lowest critical maintenance task execution. Typically, for a specific type of aircraft, when a flight plan is defined, verifying its match with appropriate wear model allows to have a good idea about the aircraft interruption scheduling. A matching process of the flight plan is applied to several patterns extracted on tree s nodes to predict maintenance tasks execution with an accurate time interval. The extraction model enables the selection of the kind of time parameter, which can either be real time or equipment life cycle counter and real time granularity. So the estimated maintenance management is more accurate than the theoretical maintenance manual. These estimations will enhance aircraft availabilities by acquiring a good knowledge of future downtime and then reduce maintenance and exploitation costs. Moreover, wear and diseases models are also a way to correlate in-service data and environmental conditions with the criticality of the execution of maintenance tasks in order to obtain relevant equipment behaviour profiles for specific environmental conditions. Hence based on forecasted aircraft orders, equipment profiles can be exploited by aircraft manufacturers during pre design analysis to adapt maintenance plans with the type of flight missions and environmental conditions foreseen by aircraft operators (airlines, etc.). [Brown 07] clearly shows that equipment usage behaviour analyses on pre design phase contribute to enhance some of design problems as vibration, blade containment or airframe installation. Thus, such pre design models enable to determine a specific aircraft configuration for the type of flight missions and environmental conditions with the goal of reducing maintenance cost and whole aircraft wears. The more various in-service data are the more accurate the profile is for the benefit of aircraft design. For example, taking into account environmental conditions of missions and configuration parameters in leaf's models enables to build a profile of equipment behaviour with regards to those parameters. Also equipment, sub part, or part profiles provide a real knowledge of their behaviours in their functional environment and hence contribute to valuable feedback for aircraft design (aircraft structure, engines, equipments and parts). Then more precise data can be integrated, for example several equipment values can make precise trends about equipment vibration and so on. 5

6 Also other organisation structure can be used to build heretical models of specific aircraft parts. For example, specifying the wear model of the hydraulic equipment part of an aircraft. Conclusion As can be seen above, worthwhile information is extracted by merging design and in-service data in a chronological connection to get out fashion trades and explicit their impacts on wear resistance and maintenance execution. In-service data includes flight, environmental, configurations parameters and maintenance task execution recommendations. Historical data are classified with regards to the hierarchical Aircraft Maintenance Manual structure such as each node of the tree contains an historical data base. A wear model is built from base on each node; it characterizes the kinds of usage that made the execution of maintenance tasks described in the node. These models serve to predict future aircraft failure with regards to environmental parameters and configuration. These predictions help enhancing aircraft availabilities. The use of environmental and configuration parameters in leaf s models make connection between those parameters and equipment failures. These connections can characterize equipment s configurations on flight resistance and enhance equipment design with minimum costs. What are the next steps? The next steps are to improve our models on a large scale by retrieving and integrating a high volume and variety of data from heterogeneous information systems. We work in close cooperation with aircraft/engine/equipment manufacturers as well as maintenance centers. Reference [B.Zakour 10] Ben Zakour A., Sistiaga M., Maabout S. and Mosbah M. Time Constraints Extention On frequent Sequentila Patterns, in International Conference on Knowledge Discovery and Information Retrieval (KDIR 2010) [Stewart 09] Stewart, D., AeroStrategy (2009). «The attractiveness of the aeronautics Maintenance Repair and Overhaul (MRO) and its segmentation» [Rabatel 09] Rabatel, J., S. Bringay, and P. Poncelet (2009). So_mad : «Sensor mining for anomaly detection in railway data», in Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects, Berlin, Heidelberg, pp Springer Verlag. [Michaels 08] Michaels, K., AeroStrategy (2008). «A glimpse into the future, in MRO 2008 Conference & Exhibition, Ft. Lauderdale. [Brown 07] Brown, R., AeroStrategy (2007). «An Industry commentary: Winds of change» in SpeedNews 8th Annual Aviation Industry Suppliers Conference in Toulouse. [Hirate 06] Hirate, Y. And Yamana H. (2006). «Generalized sequential pattern mining with item intervals Journal of Computers 1. 6