Development and application of maintenance decision-making support system for aircraft fleet

Highlights

• A new reliability evaluation method based on the real-time load information for aircraft structures.

• Minimize the maintenance cost and maximize the availability of a fleet.

• A multi-objective decision-making model based on condition-based-maintenance.

• A maintenance decision-making support system for integrating the process of data acquisition, data processing and maintenance decision-making.

Abstract

With the arrival of on-board monitoring systems for aircraft structures, maintainers have access to a near real-time assessment of component health. Due to the traditional reliability assessment methods for aircraft structures lack of the ability to make full use of real-time status information collected by the monitoring systems, a new reliability evaluation method based on the real-time load information is proposed for aircraft structural reliability. Condition-based-maintenance is a maintenance scheme which recommends maintenance decisions according to equipment status collected by monitoring systems over a period of time. As most researches on condition-based maintenance for aircraft focused on solely minimizing maintenance cost or maximizing the availability of single aircraft, a multi-objective decision-making model based on condition-based-maintenance concentrated on both minimizing the maintenance cost and maximizing the availability of a fleet (total number of available aircraft in fleet) is established for an aircraft fleet. Finally, a maintenance decision-making support system (MDMSS) is developed based on the proposed model. The system integrates the process of data acquisition (real-time status information of aircraft), data processing (reliability assessment of aircraft structures) as well as maintenance decision-making. Therefore, it simplifies the complex process of equipment management and takes a step further in engineering application.

Introduction

Structure fatigue is a critical problem for aircraft arising from their nature as multiple-component structures, subjected to random dynamic loads during flight [1], [2], [3], [4]. The structure is replaced or repaired when the reliability of structure reaches the threshold value given by designer. The reliability analysis methods of aircraft structure are known as stress-life, damage-tolerance, etc. [5]. Currently, the stress-life method is used by the U.S. Navy to account the fatigue damage in aircraft [1], which directly relates service loads to a safe operating life according to the linear cumulative damage [2], [3]. The time of structural failure is determined from full-scale fatigue tests, in which the expected service loads are simulated and applied to a specimen in a laboratory environment. To ensure that the structure do not exceed the fatigue life limits during their service, based on the laboratory defined S-N curve and loading cycles in the service spectrum, the fatigue cumulative damage (FCD) is calculated and linearly summed to determine when the structure fails. When FCD reaches 1, either the structure is replaced, or its life is extended by repair. Stress-life method is widely used to determine aircraft and other mechanical structure fatigue life because of its simplicity and practicality [6], [7], [8]. However, the exact reliability value of the structure and its components cannot be determined by stress-life method [6], [9]. To overcome this weakness, as well as considering that the factors which affect the structure fatigue life exhibit considerable scatter [10], [11], [12], [13], [14], the probability-damage-tolerance (PDT) methods are widely studied in recent years, due to its capability in both of handling high reliability problems and taking the stochastic factors into full consideration. Despite their advantages over deterministic analysis methods (e.g., stress-life method), design organizations have been reluctant to adopt even the standard PDT methods or to include them as part of their risk analysis capability. Reason cited include: the complexity of failure modes; lack of available damage data; and safety issues [15].

Lots of studies relating to aircraft reliability have been discussed, the importance of structural and non-structural reliability of aircraft has been extensively studied as well [16], [17], [18], [19], [20]. It is difficult to establish a comprehensive reliability model which can take the whole aircraft reliability into consideration, since aircraft is a complex system involving mechanical, electrical and hydraulic sub-systems. A practical way is to establish different reliability models for different sub-systems. Each of these sub-systems contains components of the same type (e.g., structural components and non-structural components). Due to the research concerning maintenance of aircraft structure subjected to fatigue loads is insufficient. In addition, more and more advanced fatigue monitoring sensors are applied to collect the loading information of the key structures and the collected information are further used to determine the failure times of structures. Thus, this work discussed the reliability of structures subjected to fatigue loads. With improvement to technology and the increase of the Army's information infrastructure, many aircraft have been outfitted with Health and Usage Monitoring Systems (HUMS). Fatigue monitoring of airframes has developed over decades to the stage where it is now incumbent for aircraft to be fitted with an airborne fatigue monitoring systems [21], [22], [23]. With the arrival of these on-board monitoring systems, maintainers have access to a near real-time assessment of component health [24], [25], [26], [27], [28]. Gao et al. [29] proposed a new deep quantum inspired neural network to fault diagnosis for aircraft fuel system, which aimed not only to improve flight security, but also to reduce the huge cost due to regular maintenance. Medina-Oliva et al. [30] presented a knowledge structuring scheme of fleets in the marine domain based on ontologies for diagnostic purposes. Al-Dahidi et al. [31] proposed a remaining useful life estimation method based on condition monitoring data for large fleets of similar equipment. Xia et al. [32] proposed a condition-based maintenance policy for intelligent monitored multi-unit series systems with independent machine failure modes. Li et al. [33] used a general expression of stochastic process models depending on both the system age and the system state to describe the degradation processes of systems. In this work, we discussed a way to establish a new reliability evaluation method, in which the linear cumulative damage of stress-life method and crack propagation life model of PDT method are combined to make full use of the real-time load information collected by HUMS.

HUMS utilizes condition-based maintenance (CBM) concepts to minimize unscheduled failures and maintenance costs. CBM is a maintenance scheme that recommends maintenance decisions based on the information collected by condition monitors [34]. It mainly includes three steps: data acquisition, data processing and maintenance decision-making [35], [36], [37]. CBM attempts to avoid unnecessary maintenance tasks by taking maintenance actions only when there is evidence of abnormal behaviors of a physical asset or a system (e.g., structures) reaches its failure threshold level. A CBM program, if properly established and effectively implemented, can significantly reduce maintenance cost by reducing the number of unnecessary scheduled preventive maintenance operations or making full use of the system's design life (i.e., maintenance operations are applied only when the system reaches its failure threshold level) [38]. In a CBM framework, maintenance policies will be optimized to minimize the operational costs or maximize the availability of systems. As most existing researches on CBM for aircraft solely focused on reducing maintenance cost or maximizing the availability of single aircraft [39], [40], [41], [42], and there are few researches aimed both to minimize cost and to maximize the availability from the perspective of a fleet [43], [44], [45]. Feng et al. [46] developed a two-stage dynamic decision-making model aimed to minimize the maintenance cost for an aircraft fleet. Wijk [47] established a cost-effective optimization model for a phase-out scenario of an aircraft fleet. Al-Thani et al. [48] proposed an exact mixed integer programming model focused on maximizing availability for a specific homogeneous fleet type. In this work, a multi-objective decision-making model based on CBM (MODM-CBM) which concentrates on both reducing the maintenance cost and maximizing the availability of a fleet is established for an aircraft fleet. Sun et al. [49] proposed a ordering decision-making methods on spare parts for a new aircraft fleet based on a two-sample prediction.

The engineering application of maintenance decision for aircraft is being widely researched. The United States has the world's most advanced Integrated Maintenance Information System (IMIS) which mainly includes three parts: a Portable Maintenance Aids (PMA), a base-level Maintenance Information Workstations (MIWs) and a theater-level Maintenance Information Processing Center (MIPC) [50]. The employment of IMIS helps both to ensure the reliability of an aircraft fleet and to reduce the maintenance costs. In the US Air Force, a Core Automation Maintenance System (CAMS) [51] and a Reliability and Maintainability Information System (REMIS) [52] are developed to improve the efficiency of aviation equipment management, moreover, the two systems can help collect and analyze the information obtained during the maintenance process of aviation equipment. Additionally, the Pakistan Air Force developed a Logistics Management Information System (LMIS) to manage the real-time information of aviation equipment, LMIS can track the status of each aircraft in fleet and enhance the ability of maintenance support.

In light of the above studies and software development experience obtained from existing researches [53], [54], [55], this work developed a maintenance decision-making support system (MDMSS) for an aircraft fleet. MDMSS integrates the process of data acquisition (real-time status information of aircraft), data processing (reliability assessment of aircraft structures) and maintenance decision-making, moreover, it simplifies the complex process of equipment management.

The innovation points and improvements of this paper are listed as follows:

• A new reliability evaluation method based on the real-time load information is proposed for aircraft structural reliability, to make full use of real-time status information collected by the monitoring systems, as well as to handling the high reliability problems of aircraft structures. The new reliability evaluation method not only combines the practicality of the stress-life methods and the ability of handling high reliability problems of PDT methods, but also avoids the disadvantages of traditional stress-life methods and PDT methods (the real-time status information cannot be applied to help determine the reliability of aircraft structures).

• A multi-objective decision-making model based on CBM (MODM-CBM) is established for an aircraft fleet. MODM-CBM concentrates on both reducing the maintenance cost and maximizing the availability of a fleet. Thus, it overcomes the shortcomings of the traditional CBM research which focuses on single aircraft state prognostic and maintenance time.

• A maintenance decision-making support system (MDMSS) is developed. MDMSS integrates the process of data acquisition (real-time status information of aircraft), data processing (reliability assessment of aircraft structures) and maintenance decision-making, moreover, it simplifies the complex process of equipment management as well as takes a step further in engineering application.

The remainder of this paper is organized as follows. Section 2 presents the real-time status information processing. Section 3 introduces the proposed reliability evaluation method based on the real-time load information. Section 4 introduces the proposed multi-objective decision-making model based on CBM. Section 5 illustrates the development of maintenance decision-making support system, MDMSS. Section 6 introduces the system application of MDMSS. Section 7 concludes this paper.