Use of PHM Information and System Architecture for Optimized Aircraft Maintenance Planning

Rodrigues, Leonardo Ramos; Gomes, João P. P.; Ferri, Felipe; Medeiros, Ivo Paixão de; Galvão, Roberto Kawakami Harrop; Nascimento, Cairo Lúcio

doi:10.1109/jsyst.2014.2343752

Cited by 61 publications

(27 citation statements)

References 19 publications

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“…Finally, the expected number of cycles that the system will operate until it reaches the safety level SL if a maintenance activity with scope S is carried out can be obtained according to Eq. (25).…”

Section: ) Computation Of L(s)mentioning

confidence: 99%

A Chaotic Grey Wolf Optimizer Applied to Condition-Based Maintenance Optimization

Rodrigues¹,

Gomes²

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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The Grey Wolf Optimizer (GWO) algorithm is a nature-inspired population-based metaheuristic that simulates the social behavior observed in a grey wolf pack. GWO has been successfully applied to different optimization problems. In this paper, we propose a chaotic version of GWO, denoted by CGWO, that uses a chaotic variable to define the number of wolves in the pack that will act as leaders, i.e. the number of wolves that guide the hunting process in each iteration of the algorithm. The proposed algorithm is used to find the optimal maintenance scope for a series-parallel system. We assume that a Prognostics and Health Management (PHM) system is available and provides the degradation level and the Remaining Useful Life (RUL) prediction for each component. The goal is to find the maintenance scope that minimizes the expected total cost per cycle until the next maintenance intervention. The performance of the proposed model is compared with the performance of the original GWO and the well-studied Ant Colony Optimization algorithm (ACO). Different chaotic maps were tested. The results show that the proposed model presented a competitive performance.

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Section: ) Computation Of L(s)mentioning

confidence: 99%

A Chaotic Grey Wolf Optimizer Applied to Condition-Based Maintenance Optimization

Rodrigues¹,

Gomes²

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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“…Formally, the task assignment problem consists in computing a mapping W V T → : , which assigns a task for each vehicle to visit. So that, the goal is to compute the map T , which minimizes the total weighted service time over all the tasks: (4) where i t is the wait time before task i is performed by a vehicle.…”

Section: Phm-based Task Assignmentmentioning

confidence: 99%

“…An alternative to deal with systems containing a large number of components consists of using metaheuristics to find near-optimal solutions in a reasonable time, as in [4]. …”

Section: Phm-based Task Assignmentmentioning

confidence: 99%

Integrated task assignment and maintenance recommendation based on system architecture and PHM information for UAVs

Medeiros

Rodrigues

Kern

et al. 2015

2015 Annual IEEE Systems Conference (SysCon) Proceedings

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PHM (Prognostics and Health Monitoring) can be defined as the capability of assessing the health condition, forecasting impending failures and the expected RUL (Remaining Useful Life) of a component based on a set of measurements collected from systems. Additionally, an important concept that could stem from PHM is IVHM (Integrate Vehicle Health Management); that is the unified capability of integrating PHM within a framework of available resources and operational demand. Therefore, this work aims to integrate task assignment and maintenance recommendation, both based on PHM information, for UAVs (Unmanned Aerial Vehicle) Swarm. Task assignment is the problem of assigning a vehicle to a task. This paper uses a PHM-based task assignment solution; this solution takes into account mission time, task priority and vehicle's health condition. Maintenance recommendation is the operation of defining which component should receive maintenance action, using an algorithm that takes into account PHM information, system architecture and safety margins. Both task assignment and maintenance recomendations take advantage of a combination of PHM information and system architecture to compute the UAV's health condition, referred as S-RUL (System Level Remaining Useful Life). The S-RUL provides information related to the time when the whole system will stop working. In the case study, a simplified pitch control system is used to illustrate the application of the proposed method to UAVs Swarm.

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“…Advances in prognostics and health monitoring (PHM) of system components (Zhang et al 2009;Bizarria and Yoneyama 2009;Vachtsevanos et al 2005) have made it possible to estimate the condition of actuators, as well as to predict their remaining useful life (RUL) under the present working conditions. Such information may be of value within a condition-based maintenance scheme to schedule the next maintenance intervention for a suitable time (Rodrigues et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Predictive Control for Systems with Loss of Actuator Effectiveness Resulting from Degradation Effects

Vieira

Galvão

Yoneyama

2015

J Control Autom Electr Syst

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Predictive control is a promising strategy for the mitigation of faults that result in more stringent operational constraints on the inputs and outputs of a system. In this context, the present work proposes a predictive control approach for accommodation of faults associated with the loss of actuator effectiveness. More specifically, it is assumed that the actuator is subject to degradation effects with progress rate proportional to the control effort. The loss of effectiveness occurs when the degradation reaches an intermediate threshold between the nominal operating condition and the total failure of the actuator. The resulting predictive control law involves the solution of a mixed-integer programming problem to be solved at each sampling instant. By imposing a suitable terminal constraint, convergence to equilibrium and recursive feasibility of the input and state constraints are ensured, provided that the optimization problem is initially feasible. Simulation results are presented for illustration.

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Use of PHM Information and System Architecture for Optimized Aircraft Maintenance Planning

Cited by 61 publications

References 19 publications

A Chaotic Grey Wolf Optimizer Applied to Condition-Based Maintenance Optimization

A Chaotic Grey Wolf Optimizer Applied to Condition-Based Maintenance Optimization

Integrated task assignment and maintenance recommendation based on system architecture and PHM information for UAVs

Predictive Control for Systems with Loss of Actuator Effectiveness Resulting from Degradation Effects

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