The Effects of Component Degradation on System-Level Prognostics for the Electric Powertrain System of UAVs

Darrah, Timothy; Kulkarni, Chetan S.; Biswas, Gautam

doi:10.2514/6.2020-1626

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…In our previous work [26] , we demonstrated that system performance degrades much faster than individual components due to the nonlinear effects of the joint interactions among the components and their degradation functions. However, we did not perform true RUL computations, and instead performed short-term forecasts via linear extrapolation, which only detected EOL events within the forecast horizon.…”

Section: Model Basedmentioning

confidence: 99%

“…The torque-load relationship model was derived via polynomial fitting of test data obtained from a publicly available dataset. The aerodynamics, DC motor, and continuous battery models were adapted from previous publications [26,[36][37][38][39] . The battery degradation model came from test data obtained from NASA' s data repository.…”

Section: Data Model Source Referencementioning

confidence: 99%

“…𝑗=1 𝑅 𝑗 is the equivalent electric resistance of the motor coils j of a 3-phase motor, 𝐾 𝑒 is the back electromotive force constant, 𝜔 𝑖 is the angular velocity of the 𝑖th motor, 𝑇 𝑓 is the static friction torque, 𝐷 𝑓 is the viscous damping coefficient that allows to estimate the dynamic friction torque (𝐷 𝑓 𝜔), 𝐽 𝑚 is the inertia of the motor, 𝑣 𝑚 is the input voltage control signal, 𝑖 𝑐 is the current demanded from the battery pack, and 𝑇 𝑝 represents the torque load generated by the propellers. The battery is modeled using an equivalent circuit representation [26,37] . The model of the UAV also includes a GPS that consumes an average of 690mA with an average power consumption rate of 8.26Wh [45] .…”

Section: System Descriptionmentioning

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: Model Basedmentioning

confidence: 99%

Section: Data Model Source Referencementioning

confidence: 99%

Section: System Descriptionmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The system used in our experiments is a generic octorotor modelled with parameters taken from (Osmić et al, 2016). In previous work (Darrah et al, 2020, a DJI Mavic Pro and DJI S-1000 were used, respec-tively. Detailed modeling and implementation can be found in those publications, however a brief overview is provided below.…”

Section: System Descriptionmentioning

confidence: 99%

“…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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Kalman Filter-Based Systems Approach for Prognostics and Health Management of Electric Motors

Park

Lee²,

Kim

et al. 2023

Springer Series in Reliability Engineering

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The Effects of Component Degradation on System-Level Prognostics for the Electric Powertrain System of UAVs

Cited by 5 publications

References 19 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

Kalman Filter-Based Systems Approach for Prognostics and Health Management of Electric Motors

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