Prognostics Based Decision Making for Safe and Optimal UAV Operations

Darrah, Timothy; Quiñones-Grueiro, Marcos; Biswas, Gautam; Kulkarni, Chetan S.

doi:10.2514/6.2021-0394

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…For each degradation parameter, 𝑄, 𝑅 0 , and 𝑅 𝑚 , we use a standard deviation of 2% of the mean value. Noise and uncertainty are also captured in the battery state estimator mentioned above, and for a more in-depth review, we direct the reader to ( [46][47][48] ).…”

Section: System Modelmentioning

confidence: 99%

“…As a result, throttle increase is required to compensate for these effects, thus increasing the battery' s rate of drainage. In previous experiments [26,39,48] , we only considered the degradation of one motor, which resulted in a consistent position error deviation with respect to the direction of flight and the direction of wind. In real life, all of the motors can degrade simultaneously, and this would result in unpredictable trajectory deviations.…”

Section: Degradation Models and Environment Conditionsmentioning

confidence: 99%

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A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

Darrah¹,

Biswas²,

Frank³

et al. 2022

J Surveill Secur Saf

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Maintaining safe operations in cyber physical systems is a complex task that must account for system degradation over time, since unexpected failures can result in the loss of life and property. Operational failures may be attributed to component degradation and disturbances in the environment that adversely impact system performance. Components in a CPS typically degrade at different rates, and, therefore, require continual monitoring to avoid unexpected failures. Moreover, the effects of multiple degrading components on system performance may be hard to predict. Developing and maintaining accurate physics-based system models can be expensive. Typically, it is infeasible to run a true system to failure, so researchers and practitioners have resorted to using data-driven techniques to better evaluate the effect of degrading components on overall system performance. However, sufficiently organized datasets of system operation are not readily available; the output of existing simulations is not organized to facilitate the use of data-driven machine learning techniques for prognostics. As a step toward addressing this problem, in this paper, we develop a data management framework and an end-to-end simulation testbed to generate such data. The framework facilitates the development and comparison of various system-level prognostics algorithms. We adopt a standard data-centered design methodology, combined with a model based engineering approach, to create a data management framework that address data integrity problems and facilitates the generation of reproducible results. We present an ontological design methodology centered around assets, processes, and data, and, as a proof of concept, develop an unmanned aerial vehicle (UAV) system operations database that captures operational data for UAVs with multiple degrading components operating in uncertain environments. Aim: The purpose of this work is to provide a systematic approach to data generation, curation, and storage that supports studies in fault management and system-level prognostics for real-world and simulated operations. We use a data-driven simulation-based approach to enable reliable and reproducible studies in system-level prognostics. This is accomplished with a data management methodology that enforces constraints on data types and interfaces, and decouples various parts of the simulation to enable proper links with related metadata. The goal is to provide a framework that facilitates data analysis and the development of data-driven models for prognostics using machine learning methods. We discuss the importance of systematic data management framework to support data generation with a simulation environment that generates operational data. We describe a standard framework for data management in the context of run-to-failure simulations, and develop a database schema and an API in MATLAB® and Python to support system-level prognostics analyses. Methods: A systematic approach to defining a data management framework for the study of prognostics applications is a central piece of this work. A second important contribution is the design of a Monte Carlo simulation environment to generate run to failure data for CPS with multiple degrading components. We adopt a bottom-up approach, starting with requirements and specifications, then move into functionality and constraints. With this framework, we use a Monte-Carlo simulation approach to generate data for developing and testing a variety of system-level prognostics algorithms. Results: We have developed a data management framework that can handle high dimensional and complex data generated from real or simulated systems for the study of prognostics. In this paper, we show the advantages of a well-organized data management framework for tracking high-fidelity data with high confidence for complex, dynamic CPS. Such frameworks impose data logging discipline and facilitate downstream uses for developing and comparing different data-driven monitoring, diagnostics, and system-level prognostics algorithms. Conclusions: In this paper, we demonstrate the design, development, and use of an asset, process, and data management framework for the research to develop prognostics & health management applications. This work helps fill a gap for system-level remaining useful life studies by providing a comprehensive simulation environment that can generate run to failure data, and a data management architecture that addresses the needs for system-level prognostics research. The framework is demonstrated with a Monte-Carlo simulation of a UAV system that operates multiple flights under different environmental conditions and degradation sources. This architecture for data management will enable researchers to conduct more complex experiments for a variety of cyber physical systems applications.

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Section: System Modelmentioning

confidence: 99%

Section: Degradation Models and Environment Conditionsmentioning

confidence: 99%

A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

Darrah¹,

Biswas²,

Frank³

et al. 2022

J Surveill Secur Saf

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is an abundance of research on the technical aspects of UAV systems: their design & implementation (Osmić et al, 2016); stability & risk analysis (Quiñones-Grueiro et al, 2021); decision making (Darrah et al, 2021); degradation (Gorospe et al, 2017;Darrah et al, 2020); and fault diagnostics (Moir & Seabridge, 2012). Prognostics and health management (PHM) technologies are of greater interest to us, which specifically addresses fault diagnosis and remaining useful life estimation in an effort to improve system reliability, safety, and maintainability.…”

Section: Introductionmentioning

confidence: 99%

Data Management Framework & UAV Simulation Testbed for the Study of System-level Prognostics Technologies

et al. 2021

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Prognostics-enabled technologies have emerged over the last few years, primarily for Condition Based Maintenance (CBM+) applications, which are used for maintenance and operational scheduling. However, due to the challenges that arise from real-world systems and safety concerns, they have not been adopted for operational decision making based on system end of life estimates. It is typically cost-prohibitive or highly unsafe to run a system to complete failure and, therefore, engineers turn to simulation studies for analyzing system performance. Prognostics research has matured to a point where we can start putting pieces together to be deployed on real systems, but this reveals new problems. First, a lack of standardization exists within this body of research that hinders our ability to compose various technologies or study their joint interactions when used together. The second hindrance lies in data management and creates hurdles when trying to reproduce results for validation or use the data as input to machine learning algorithms. We propose an end-to-end object-oriented data management framework & simulation testbed that can be used for a wide variety of applications. In this paper, we describe the requirements, design, and implementation of the framework and provide a detailed case study involving a stochastic data collection experiment.

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“…Zheng et al (2021) used FMECA to define the fault criticality and reduce the total cost incurred by misdiagnosis. Darra et al (2021) considered safe urban operations of UAVs and afforded to minimize the probability of in-flight failure and improve maintenance scheduling. They did not propose a maintenance plan and only explained safe flight by the control of the flight path.…”

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confidence: 99%

RBM-MCDM framework for optimization of maintenance and inspection intervals of small unmanned aircrafts

Farsi

2022

JQME

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PurposeUnmanned aircraft applications are quickly expanded in different fields. These systems are complex that include several subsystems with different types of technologies. Maintenance and inspection planning is necessary to obtain optimal performance and effectiveness. The failure rate in these systems is more than commercial and manned aircraft since they are usually cheaper. But maintenance and operation planning are difficult because we deal with a system that has multi-components, multi-failure models, and different dependencies between subsystems without any advanced health monitoring system. In this paper, this matter is considered and a framework to determine optimal maintenance and inspection plan for this type of system is proposed to improve system reliability and availability. The new criteria according to this field are proposed.Design/methodology/approachMaintenance of unmanned systems influences their readiness; also, according to the complexity of the system and different types of components, maintenance programming is a vital requirement. The plan should consider several criteria and disciplines; thus, multicriteria decision approaches may be useful. On another side, the reliability and safety of unmanned aircraft are the most important requirements in the design and operation phases. The authors consider these parameters and develop a framework based on risk-based maintenance to overcome the problems for unmanned systems. This framework consists of two stages: at the first stage, the critical components and failure modes are determined by FMEA, and in the second stage, the priority of maintenance tasks is determined by a fuzzy multicriteria weighted decision system. In this study, fourteen criteria with different levels of importance are developed and proposed to find the best plan for maintenance and inspection intervals. These criteria have been extracted from the literature review, the author's experience, and expert opinions.FindingsA novel framework for risk-based maintenance has been proposed. Risk determination and risk criteria are the most important factors in this framework. Risks are determined by FMEA, and new criteria are proposed that are used for decision-making. These criteria are proposed based on practical experience and experts' opinions for the maintenance process in the aeronautic industry. These are evaluated by industrial cases, and this framework capability has been demonstrated.Research limitations/implicationsThe proposed framework and criteria for small unmanned aircraft have been developed based on a practical point of view and expert opinion. Thus for implementation in other aeronautic industries, the framework may need a minor modification.Practical implicationsTwo important subsystems of an unmanned aircraft have been studied, and the capabilities of this method have been presented.Originality/valueThis research is original work to determine a maintenance program for unmanned aircraft that their application has rapidly grown up. Practical and design parameters have been considered in this work.

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Prognostics Based Decision Making for Safe and Optimal UAV Operations

Cited by 4 publications

References 11 publications

A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

Data Management Framework & UAV Simulation Testbed for the Study of System-level Prognostics Technologies

RBM-MCDM framework for optimization of maintenance and inspection intervals of small unmanned aircrafts

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