استفاده از شبکه های بیزی در پیش بینی برای یک مفهوم جدید مدیریت بهداشت و درمان مجتمع خودرو

The aeronautics industry is attempting to implement important changes to its maintenance strategy. The article presents a new framework for making final decision on aeroplane maintenance actions. It emphasizes on the use of prognostics within this global framework to replace corrective and Preventive Maintenance practise for a predictive maintenance to minimize the cost of the maintenance support and to increase aircraft/fleet operability. The main objective of the article is to show the Bayesian network model as a useful technique for prognosis. The specific use case for predicting brake wear on the plane is developed based on this technique. The network allows estimate brake wear from the aircraft operational plan. This model, together with other models to make predictions for various components of the aeroplane (that should be monitored) offers a forward-looking approach of the status of the plane, allowing later the evaluation of different operational plans based on operational risk assessment and economic cost of each one of them depending on the scheduled checks. Highlights ► Assessment of the weakness, needs and requirements of aviation industry. ► New framework for proactive aircraft line maintenance (future scenario). ► Aircraft line maintenance based on diagnostics and prognostics. ► Bayesian networks applied for diagnosis and prognosis. ► Bayesian networks as forecasting model in aeronautics within the new maintenance process.

At present, all airline operators strive to reduce both the amount and cost of aircraft engineering maintenance while at the same time ensuring aircraft safety and reliability. Aircraft manufacturers are continually providing more novel maintenance solutions with the use of new technologies. Nevertheless, current aircraft maintenance practice is still a heavy labor and unscheduled maintenance that remains a significant problem. Therefore, it is necessary to identify and explain the significance of the major weakness that impact on the maintenance practice, and then, based on these finding, make recommendations for aircraft manufacturers and airline operators so that the identified weakness may be minimized by considering the way in which they impact on the airline operating cost. These cost drivers is established by sub-dividing the whole of the aircraft maintenance process into three convenient areas explained below. Aircraft maintenance (from the operator’s perspective) requires that the plane needs to be sufficiently reliable and easy to maintain with the minimum impact to operations performed on it, it is vital issue. An analysis of the operational disruptions caused by technical problems identifies certain aircraft components (engine, air conditioning, compressed air systems, landing gear or hydraulics, etc.) weakness, taking into account their impact on maintenance costs. Maintenance execution and Maintenance Management are closely related components because the responsible for maintenance execution depends largely on the management (planning, training, spare parts, logistics, etc.) to ensure the ability to perform tasks safety and efficiently. An analysis of the impact of both in maintenance costs identifies the following weaknesses: the lack of technicians and engineers, and the high cost in recurrent training; the lack of integration in the machine system; poor management decisions, leading to the lack of spare parts, materials or pieces for the maintenance execution; mismanagement in complex situations, etc. All these weakness can be grouped into a major feature, the operability of the aeroplane, which involves ensuring operational reliability (the punctuality of the flights), maximizing availability (asset utilization) and reducing maintenance cost. Operational reliability identifies the percentage of scheduled flights which depart and arrive without falling into an operational interruption, in such a way that it would be necessary more robustness against defects, no maintenance during turn times and a rapid ground servicing. Availability implies the probability that the aircraft is available for the service at any time during its operational life and it requires a pro-active maintenance, more flexible maintenance scheduling and more robustness against defects. Finally, maintenance cost groups direct and indirect cost of maintenance activities such as check-ins, equipment, data and record keeping, planning, engineering, supervision, tooling, test equipment, facilities, logistics and administration, etc. These costs must be reduced as much as possible either through less fuel or low maintenance costs. The operability of the aircraft is linked with ‘on-aircraft’ maintenance concept. This concept includes aircraft line maintenance which refers to regularly scheduled maintenance and implies the proper maintenance actions between flights, ensuring the punctuality, availability and reliability for the aircraft. Aircraft line maintenance set if the aircraft is able to perform the next flight or on the contrary needs to be repaired and the flight should be delayed or cancelled. Final decision is based on a check of certain aircraft components within a ‘Minimum Equipment List’ (MMEL) carried out on the time interval ‘Turn Around Time’ (TAT) between two flights. Today, the current decision support carried out in the aircraft line maintenance is a reactive process, focused on unexpected or deferred maintenance activities, which represents a high percentage of the reduction in the operability. Fig. 1 represents the actual aircraft line (ramp) maintenance limited to a go or no-go decision for the aircraft’s next flight based on a pre-flight check on certain components of the aircraft, where failures not detected at early stage could be cause delays or cancellations in the next flight, affecting the operational plan of the aircraft/fleet. Full-size image (30 K) Fig. 1. Aircraft line maintenance execution. Figure options More flexible and opportunistic maintenance planning with support for decision making is required in order to achieve a suitable maintenance, to avoid potential disruptions in the operation of the aircraft, to maximize asset utilization and to reduce downtimes (maintenance opportunity times). In summary, a new type of maintenance (not corrective or preventive) is necessary in order to maximize the operability of the aircraft. The current maintenance should be covered with a new decision support system by means of a proactive maintenance based on prognostics. Nevertheless, although there are more and more new maintenance solutions thanks to developing technologies, maintenance problems are still unpredictable and this represents a significant obstacle. The article presents a new framework for making final decision on airplane maintenance actions, designed to aircraft line maintenance explained before. The aim of the article is to emphasize on the use of prognostics within this global framework to replace corrective and Preventive Maintenance based on time for a predictive maintenance based on condition and system/subsystem Remaining Useful Life estimation. Moreover, the article presents the Bayesian network model as a useful technique, among the models that could be used for prediction making, in cases where its implementation is feasible. The specific use case for predicting brake wear on the aeroplane base on Bayesian network is developed. This network allows estimate the degradation of the brake from the aircraft operational plan. This model, together with other predictive models for components of the MMEL offers a forward-looking approach of the status of the airplane, allowing the evaluation of different operational plans based on operational risk assessment and economic cost of each one of them depending on the scheduled checks (place, date, resources, …). The article is organized as follows. Section 1 provides an introduction that discusses the current weaknesses in the aeronautics industry. Section 2 presents various frameworks that have evolved to reach an effective maintenance, shows future scenarios and explains the general framework. Furthermore, the basics of Bayesian network and its principles are depicted and it concludes with a brief overview of different applications for which they have been used for diagnosis and prognosis in industrial sector. Next, Section 3 shows the new approach focuses on the integration of forecasting techniques for aircraft line maintenance. As a result, Section 4 presents the use of Bayesian network for the prediction of the aircraft brake wear, the use case integrated into the global system to support final decision making. Finally, Section 5 introduces the conclusions.

The maintenance strategies used by the aeronautics industry need to evolve into a proactive process based on the condition of the components/subsystems of the aircraft (predictive maintenance). This process allows the state of the aircraft to be defined and a global vision of the aircraft to be obtained with some degree of foresight, providing a framework for decision making. It helps to establish the most appropriate operational plan given the current state of the aircraft, and to define the most appropriate timescale in which to carry out maintenance. The process provides help in decision making, avoids delays and cancelations to flights due to failure or unexpected or unforeseen malfunction, and reduces the economic costs currently associated with maintenance. However, despite all the efforts that have been made, there currently exist maintenance tasks such as line and in hangar maintenance of the aircraft which, today, continue to be reactive processes with high associated costs. This article has described a global framework which allows current maintenance (corrective or preventive) to be transformed into proactive maintenance based on the predicted condition of the aircraft through the use of prognostic techniques. This framework is complex and includes a series of emerging or development-phase technologies which need to be integrated into the whole process: from the definition of standards (e.g. OSA-CBM) to the development of state detection and prediction models which allow us to define the RUL of the most critical components of the aircraft (e.g. the Bayesian network to define brake wear). The precision of these models establishes the degree of utility and efficiency of the new prediction-based maintenance. In this sense, the “Conditional View” module presented in the article fulfils a most important role within the global framework as it is this that is charged with implementing the necessary functionalities in the detection of the state and prediction in the most critical components of the aircraft (included in the MMEL). It establishes the degradation and the Remaining Useful Life of these components/subsystems throughout the operational plan of the aircraft. Its models must be developed by taking into account the increased quantity of information available, by treating uncertainty appropriately, so as to establish the confidence limits under which reliable predictions may be realized, and, furthermore, it should have the capacity to adapt/readjust to new input information. The article has shown the particular example of its use for detecting and predicting the wear on the brakes of the aircraft based on a Bayesian network model. This example of use covers part of the functionalities of the “Conditional View” module, and it is the part which should be highlighted in the current project due to the benefits and innovations of the Bayesian network as a predictive model. Firstly, the Bayesian network model permits the step from the diagnosis (detection of state) to the prognosis. The first BN model (PhysicalBN) imitates the behavior of the current physical model with an insignificant error, and permits a diagnosis of the current situation of the brake whilst the aircraft carries out its operational plan. Moreover, it is possible to extend the model to a second BN (OpBN), which is itself a prognostic model, by using information related to the operating plan (when the information relating to the values which the input parameters of the physical model take is unavailable). The input parameters of PhysicalBN (which simulate the behavior of the physical model) are estimated from the operational plan of the aircraft, and this, in turn, supplies the OpBN from which the RUL is calculated using the individual results of the brake wear in each flight realized. This process of calculating the RUL produces better results when the input parameter estimates of the PhysicalBN obtained from the aircraft operational plan are better fits. Likewise, the Bayesian network boasts a series of necessary properties for the production of good prediction: it allows confidence limits to be established, different types of uncertainty to be dealt with, and its behavior to be adapted and readjusted in the face of new input information. The Bayesian network provides all the necessary characteristics to enable the “Conditional View” module to be utilized as a prognostic model, and, therefore, further consideration combined with the provision of incentives for the use of this type of techniques (or similar techniques having the same characteristics) in implementing the different cases of use and so cover all the functionalities of the “Conditional View” module.