Fault Detection, Isolation, and Accommodation Control in Robotic Systems

Huang, Sunan; Tan, K. K.

doi:10.1109/tase.2008.917009

Cited by 47 publications

(20 citation statements)

References 11 publications

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“…(13). This means that the value of the state vector which is calculated in the prediction stage is the result of the weighted averaging of the state vectors which were calculated after running the state equation, starting from the N previous values of the state vectors ξ i k−1 .…”

Section: A the Particle Approximation Of Probability Density Functionsmentioning

confidence: 99%

“…The a-posteriori probability density is found using Eq. (13). Now a new position measurement y(k) is obtained and the objective is to calculate the corrected probability density…”

Section: A the Particle Approximation Of Probability Density Functionsmentioning

confidence: 99%

“…The Kalman Filter assumes linear model representation and Gaussian measurement noise whereas the Particle Filter is suitable for nonlinear models and does not make any assumption on the measurement noise distribution [10][11][12]. To perform fault isolation, multiple state observers can be designed, each one estimating the system's output which corresponds to a certain fault [13][14]. Fault isolation is implemented by checking each residual generated by the observer state estimation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Particle and Kalman filtering for fault diagnosis in DC motors

Rigatos

2009

2009 IEEE Vehicle Power and Propulsion Conference

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Fault diagnosis is a major problem in industrial systems, and is of primary interest for mobile and industrial robotics where electric motors are used. In this paper fault diagnosis with the use of the Kalman Filter is compared to fault diagnosis based on Particle Filter. The Kalman Filter assumes linear model representation and Gaussian measurement noise whereas the Particle Filter is suitable for nonlinear models and does not make any assumption on the measurement noise distribution. The performance of the proposed methodology is tested through simulation experiments.

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Section: A the Particle Approximation Of Probability Density Functionsmentioning

confidence: 99%

“…The a-posteriori probability density is found using Eq. (13). Now a new position measurement y(k) is obtained and the objective is to calculate the corrected probability density…”

Section: A the Particle Approximation Of Probability Density Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Particle and Kalman filtering for fault diagnosis in DC motors

Rigatos

2009

2009 IEEE Vehicle Power and Propulsion Conference

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“…Fruitful theoretical results and increasing applications in industrial practical, such as in power plant coal mills and robotic systems can be found in Zhong, Ye and Shi (2005), Gomes and Silva (2008), Huang and Tan (2008). Considering the rapid development of communication networks, a great amount of effort has been devoted to the problems of FDI for NCS (Zhang, Ding, Frank and Sader 2004;Zheng, Fang, and Wang 2006;Zhong and Han 2006;Fang, Ye, and Zhong 2007;Mao, Jiang, and Shi 2007;He, Wang, Ji and Zhou 2008a;He, Wang and Zhou 2008b;Mao, Jiang, and Ding 2009;Zhang, Chen, Sheng and Hesketh 2009).…”

Section: Introductionmentioning

confidence: 99%

Delay-distribution-dependent fault detection of networked control systems with stochastic quality of services

Chen

Yue

Yang

2010

International Journal of Systems Science

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Network-induced delay in networked control systems (NCS) is inherent non-uniform distribution and behaves with multifractal nature. However, such network characteristics have not been well considered in the fault detection and isolation of NCS. Making use of those statistical characteristics of NCS network-induced delay, a delay-distribution-based fault detection model is proposed, which is greatly different from the existing Markov switching process model. From this model together with a tighter bounding technique for cross-product terms, a delay-distribution-dependent H 1 fault detection filter is carried out with significantly improved results. A numerical example is given to demonstrate the effectiveness of the proposed method.

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“…It is not always easy to determine system failure because some faults may not lead to functionality loss; therefore, definition and characterization of faults should be performed in the failure detection process. Fault isolation is the determination of the type and location of a fault, and it follows fault detection [4] [5]. One or more performance parameters may reflect different systems' failure modes.…”

Section: Introductionmentioning

confidence: 99%

A residual estimation based approach for isolating faulty parameters

Kumar

Dolev

Pecht

et al. 2009

2009 IEEE Aerospace Conference

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This paper presents a new residual estimation based diagnostic approach that includes detection and fault isolation using the Mahalanobis distance (MD). The faulty performance parameter isolation approach is based on the analysis of residual MD values. The residual value is calculated by taking the difference between MD values estimated in two different scenarios: first, when a performance parameter is present, and second, when that performance parameter is absent. The residual of the MD values for each parameter is obtained by using training data from several experiments as part of the training data analysis planned by the design-of-experiment concept to analyze the impact of each parameter. The distribution of residual MD values for each parameter is analyzed and a 95% probabilistic range is established. This range represents the expected contribution by parameters toward a healthy system's MDs, and it is used to identify the parameters that are responsible for the anomalous behavior of a system. Parameters that fall below the lower bound of the 95% probabilistic range are considered candidates for the anomalous behavior, and the parameter that has the lowest residual value is isolated as the faulty parameter. A case study on computers is presented to demonstrate and test the suggested new approach's ability to isolate faulty parameters. 1, 2

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Fault Detection, Isolation, and Accommodation Control in Robotic Systems

Cited by 47 publications

References 11 publications

Particle and Kalman filtering for fault diagnosis in DC motors

Particle and Kalman filtering for fault diagnosis in DC motors

Delay-distribution-dependent fault detection of networked control systems with stochastic quality of services

A residual estimation based approach for isolating faulty parameters

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