Leonardo Ramos Rodrigues

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

Remaining useful life (RUL) estimations obtained from a prognostics and health monitoring (PHM) system can be used to plan in advance for the repair of components before a failure occurs. However, when system architecture is not taken into account, the use of PHM information may lead the operator to rush to replace a component that would not affect immediately the operation of the system under consideration. This paper presents a methodology for decision support in maintenance planning with application in aeronautical systems. The proposed methodology combines system architecture information and RUL estimations for all components in the system under study, allowing the estimation of an overall system-level RUL (S-RUL). The S-RUL information can be used to support maintenance decisions regarding the replacement of multiple components. For this purpose, the decision problem can be cast into an optimization framework involving the minimization of the component replacement cost under a safety constraint. Two case studies are used to illustrate the S-RUL concept, as well as the proposed optimization methodology.

Health monitoring and remaining useful life estimation of lithium-ion aeronautical batteries

Batteries are essential components of any aircraft electrical system. They are used to start the aircraft propulsion engines and to provide power during electrical emergencies. As is the case with most aircraft components, batteries exhibit aging and health degradation during operation. Therefore, the correctly estimation of the battery state-of-health (SoH) and of the remaining useful life (RUL) is important to aircraft operators. Failure to do so can result in underutilization of the equipment (if it is removed before the end of its life cycle) or unpredicted failure events during operation (when the battery SoH is overestimated). The consequences can range from increased operation costs to reduced flight safety. This article first presents the life cycle of lithium-ion aeronautical batteries. Then a method is proposed to generate discharge, capacity and health monitoring models during the battery life cycle. It is shown how these models are used to estimate the battery SoH and RUL. The method is validated using data from the NASA Ames Prognostics Data Repository. The models are implemented using MATLAB/Simulink and used to simulate a typical battery in different operational conditions.

Combining PHM information and system architecture to support aircraft maintenance planning

Aircraft are highly valuable assets and large budgets are spent in preventive and predictive maintenance programs. The application of PHM (Prognostics and Health Management) technologies can be a powerful decision support tool to help maintenance planners. The RUL (Remaining Useful Life) estimations obtained from a PHM system can be used in order to plan in advance for the repair of components before a failure occurs. However, when system architecture is not taken into account, the use of PHM information may lead the operator to rush to replace a component that would not affect immediately the operation of the system under consideration. This paper presents a methodology for decision support in maintenance planning with application in aeronautical systems. The methodology combines system architecture information and RUL estimations for all components comprised in the system under study, allowing the estimation of a RUL value for the whole system. This system level RUL (S-RUL) can be used as support information for identifying the best moment to repair a component. Also, when several components present high degradation levels, the proposed methodology can be used to define a set of components that, when repaired, will bring the whole system to a safe degradation level with lowest cost. A case study is used to illustrate the application of the methodology in a simplified aircraft electrical system.

Proton Exchange Membrane Fuel Cells (PEMFC) impedance estimation using regression analysis

This paper describes the application of the PHM concept to assess the State of Health (SoH) of a Proton Exchange Membrane Fuel Cell (PEMFC) as part of the IEEE PHM 2014 Data Challenge. Two regression approaches are used as health monitoring algorithms to estimate the impedance of the PEMFC. One was a linear regression and the other was a higher order polynomial regression combined with other function found on the literature. The linear regression presented the best results compared to the other method.

PHM-based Multi-UAV task assignment

This paper is relating to the application of Integrated Vehicle Health Management (IVHM) concepts based on Prognostics and Health Monitoring (PHM) techniques to Multi-UAV systems. Considering UAV as a mission critical system, it is expected and required to accomplish its operational objectives with minimal unscheduled interruptions. So that, it does make sense for UAV to take advantage of those techniques as enablers for the readiness of multi-UAV. The main goal of this paper is to apply information from a PHM system to support decision making through an IVHM framework. PHM system information, in this case, comprises UAV remaining useful life (RUL) estimations. UAV RUL is computed by means of a fault tree analysis that it is fed by a distribution function from a probability density function relating time and failure probability for each UAV critical components. The IVHM framework, in this case, it is the task assignment based on UAV health condition (RUL information) using the Receding Horizon Task Assignment (RHTA) algorithm. The study case was developed considering a team of electrical small UAVs and pitch control system was chosen as the critical system.

How aircraft operators can benefit from PHM techniques

Prognostics and Health Management (PHM) techniques can provide a wide range of benefits to aircraft operators. Since the primary goal of PHM systems is to estimate the health state of components and equipments, as well as forecasting their Remaining Useful Life (RUL), they are often closely associated with the reduction in the number of unscheduled maintenance tasks. Indeed, the avoidance of unscheduled maintenance is a very important factor, but this technology may potentially lead to considerable further savings in other fields. Scheduled maintenance planning, improved troubleshooting, inventory management optimization and intelligent aircraft allocation to routes are other examples of how the operators can benefit from PHM techniques. These benefits may lead to important competitive advantages such as reduction in operational cost and increase in fleet reliability. The purpose of this work is to enumerate and explore qualitatively some of these benefits in terms of the mentioned competitive advantages to aircraft operators. Although PHM systems can offer benefits to other members of the aeronautical sector such as aircraft manufacturers and Original Equipment Manufacturers (OEM), this work will focus on the aircraft operators point of view.

Troubleshooting optimization using multi-start simulated annealing

A troubleshooting strategy is a sequence of actions that must be carried out in order to solve a problem. Some troubleshooting strategies consist of a combination of actions and questions. In such cases, each possible answer for a question may lead to a different set of troubleshooting actions (or a different sequence of troubleshooting actions). In many applications, the set of all possible actions and questions are known. Then, the troubleshooting problem can be defined as finding the optimal sequence of actions and questions, which can be modeled as a combinatorial optimization problem. This paper describes an optimization method to minimize the expected cost of repair (ECR) of a single failure troubleshooting model, considering both dependent and independent actions, questions and cost clusters. The proposed method uses a combination of simulated annealing and multi start search to solve the troubleshooting problem. Numerical examples are presented to illustrate the application of the proposed method in troubleshooting models with different complexity levels.

Maintenance cost optimization for multiple components using a condition based method

Since maintenance planning directly affect the availability and the lifecycle cost of components and systems, it has become a topic of great interest among researchers and industry practitioners in recent years. Preventive maintenance techniques can be adopted in order to determine a convenient maintenance schedule, reducing the number of unexpected failure events. The implementation of a preventive maintenance approach may also provide other benefits such as increase in equipment availability, reduction in maintenance costs and increase in equipment lifetime. In this scenario, the application of PHM (Prognostics and Health Monitoring) techniques can be thought as a powerful tool to support the implementation of a CBM (Condition Based Maintenance) approach. The problem of CBM optimization can be formulated as finding the optimum maintenance schedule for a set of components so that the average maintenance cost per unit of time is minimized. In this paper, a maintenance cost optimization method for multiple components is presented. The proposed method uses information on the health condition of each component and takes into account the economic benefits of repairing multiple components at the same time instead of scheduling maintenance interventions for different components in different time instants, based on individual optimization recommendations. A numerical example is presented to illustrate the application of the proposed method.

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

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 vehicles 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 UAVs 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.