Autonomous Decision Making for Planetary Rovers Using Diagnostic and Prognostic Information

Narasimhan, Sriram; Balaban, Edward; Daigle, Matthew; Roychoudhury, Indranil; Sweet, Adam; Celaya, José R.; Goebel, Kai

doi:10.3182/20120829-3-mx-2028.00243

Cited by 9 publications

(8 citation statements)

References 5 publications

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“…We do not provide the description for these systems. More details can be found in (Narasimhan, Balaban, Daigle, Roychoudhury, Sweet, Celaya, & Goebel, 2012) and (Poll, Patterson-Hine, Camisa, Garcia, Hall, Lee, Mengshoel, Neukom, Nishikawa, Ossenfort, Sweet, Yentus, Roychoudhury, Daigle, Biswas & Koutsoukos, 2007) for rover and ADAPT systems, respectively.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…The initial D-matrix of the rover system is generated via simulations with three faults and thirteen tests (see Table 1 and Table 2). Three fault scenarios viz., battery parasitic load, motor friction fault, and voltage sensor fault are simulated (Narasimhan et al, 2012) by injecting them in the rover test bed and altering the corresponding measurements with erroneous values. All these faults are injected at 50s.…”

Section: Example System 1: Rovermentioning

confidence: 99%

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A Framework to Debug Diagnostic Matrices

2013

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Diagnostics is an important concept in system health and monitoring of space operations. Many of the existing diagnostic algorithms utilize system knowledge in the form of diagnostic matrix (D-matrix, also popularly known as diagnostic dictionary, fault signature matrix or reachability matrix). The D-matrix maps tests on observed conditions to failures. This matrix is mostly gleaned from physical models during system development. But, sometimes, this may not be enough to obtain high diagnostic performance during operation due to system modifications and lag and noise in sensor measurements. In such a case, it is important to modify this D-matrix based on knowledge obtained from sources such as time-series data stream (simulated or maintenance data) within a framework that includes the diagnostic/inference algorithm. A systematic and sequential update procedure, diagnostic modeling evaluator (DME) is proposed to modify D-matrix and wrapper/test logic considering the least expensive update first. The user sets the diagnostic performance criteria. This iterative procedure includes conditions ranging from modifying 0’s and 1’s in the matrix, adding/removing the rows (failure sources)/columns (tests), or modifying test/wrapper logic used to determine test results. We will experiment this framework on ADAPT datasets from DX challenge 2009.

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Section: Simulations and Resultsmentioning

confidence: 99%

Section: Example System 1: Rovermentioning

confidence: 99%

A Framework to Debug Diagnostic Matrices

2013

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“…The step subsequent to prognostic health assessment, involving decision-making based on system health information, is the subject of ongoing research, with some of the preliminary results described in [12,13].…”

Section: Approachmentioning

confidence: 99%

Prognostic Health-Management System Development for Electromechanical Actuators

Balaban

Saxena

Narasimhan

et al. 2015

Journal of Aerospace Information Systems

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Electromechanical actuators have been gaining increased acceptance as safety-critical actuation devices in the next generation of aircraft and spacecraft. The aerospace manufacturers are not ready, however, to completely embrace electromechanical actuators for all applications due to apprehension with regard to some of the more critical fault modes. This work aims to help address these concerns by developing and testing a prognostic health-management system that diagnoses electromechanical actuator faults and employs prognostic algorithms to track fault progression and predict the actuator's remaining useful life. The diagnostic algorithm is implemented using a combined modelbased and data-driven reasoner. The prognostic algorithm, implemented using Gaussian process regression, estimates the remaining life of the faulted component. The paper also covers the selection of fault modes for coverage and methods developed for fault injection. Validation experiments were conducted in both laboratory and flight conditions using a flyable electromechanical actuator test stand. The stand allows test actuators to be subjected to realistic environmental and operating conditions while providing the capability to safely inject and monitor propagation of various fault modes. The paper covers both diagnostic and prognostic run-to-failure experiments, conducted in laboratory and flight conditions for several types of faults. The experiments demonstrated robust fault diagnosis on the selected set of component and sensor faults and high-accuracy predictions of failure time in prognostic scenarios.

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“…Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. In planetary rovers, a battery health management (BHM) system is especially important, due to its role in short-and long-term mission planning and decision-making [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…While much research has been carried out on battery modeling and battery state estimation, 978-1-4799-1622-1/14/$31.00 c 2014 IEEE. 1 IEEEAC Paper #2291, Version 2, Updated 12/01/2014. the prediction component, i.e., prognostics, has only recently begun to receive attention [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A battery health monitoring framework for planetary rovers

Daigle

Kulkarni

2014

2014 IEEE Aerospace Conference

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Batteries have seen an increased use in electric ground and air vehicles for commercial, military, and space applications as the primary energy source. An important aspect of using batteries in such contexts is battery health monitoring. Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. For planetary rovers, battery health estimation and prediction is critical to mission planning and decision-making. We develop a model-based approach utilizing computationally efficient and accurate electrochemistry models of batteries. An unscented Kalman filter yields state estimates, which are then used to predict the future behavior of the batteries and, specifically, end of discharge. The prediction algorithm accounts for possible future power demands on the rover batteries in order to provide meaningful results and an accurate representation of prediction uncertainty. The framework is demonstrated on a set of lithium-ion batteries powering a rover at NASA Ames Research Center using real experimental field test data.

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Autonomous Decision Making for Planetary Rovers Using Diagnostic and Prognostic Information

Cited by 9 publications

References 5 publications

A Framework to Debug Diagnostic Matrices

A Framework to Debug Diagnostic Matrices

Prognostic Health-Management System Development for Electromechanical Actuators

A battery health monitoring framework for planetary rovers

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