Optimal minimum variance estimation for non-linear discrete-time multichannel systems

Grimble, M.J.; Naz, Shamsher Ali

doi:10.1049/iet-spr.2009.0001

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…The simple solution that follows arises because Downloaded by [University of Chicago Library] at 08:14 20 November 2014 of the assumptions of linearity for the signal-generating model and the results obtained here involve only a leastsquares type of analysis (Grimble & Shamsher, 2010).…”

Section: Nmve-based Fault Detectionmentioning

confidence: 98%

Experimental application of nonlinear minimum variance estimation for fault detection systems

Alkaya

Grimble

2014

International Journal of Systems Science

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The purpose of this paper is to present an experimental design and application of a novel model-based fault detection technique by using a nonlinear minimum variance (NMV) estimator. The NMV estimation technique is used to generate a residual signal which is then used to detect faults in the system. The main advantage of the approach is the simplicity of the nonlinear estimator theory and the straightforward structure of the resulting solution. The proposed method is implemented and validated experimentally on DC servo system. Experimental results demonstrate that the technique can produce acceptable performance in terms of fault detection and false alarm.

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Section: Nmve-based Fault Detectionmentioning

confidence: 98%

Experimental application of nonlinear minimum variance estimation for fault detection systems

Alkaya

Grimble

2014

International Journal of Systems Science

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“…In nonlinear minimum-variance estimation, the nonlinearities are assumed to be in the signal channel or possibly in a noise channel representing the uncertainty. The simple solution that follows arises because of the assumptions of linearity for the signal generating model and the results obtained here involve only a least-squares type of analysis [19].…”

Section: Nmve Based Fault Detectionmentioning

confidence: 99%

“…In NMVE, the non-linearities are assumed to be in the signal channel or possibly in a noise channel representing the uncertainty. The simple solution that follows arises because of the assumptions of linearity for the signal generating model and the results obtained here involve only a least-squares type of analysis (Grimble and Shamsher, 2010). The FD techniques are often based on the generation of appropriate residual signals, which have to be sensitive to faults themselves but independent of disturbances.…”

Section: Nmve-based Fault Detectionmentioning

confidence: 99%

Non-linear minimum variance estimation for fault detection systems

Alkaya

Grimble

2014

Transactions of the Institute of Measurement and Control

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A novel model-based algorithm for fault detection in stochastic linear and non-linear systems is proposed. The non-linear minimum variance estimation technique is used to generate a residual signal, which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully

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“…The classic work of Rao and Whyte [1] presented such approach using decentralised Kalman filtering to accomplish globally optimal performance in the case where all sensors can communicate with all other sensors. Other published methods can be a sensor‐less approach [2, 3] or a derivative‐free filtering estimation [4], a least‐squares‐Kalman technique [5], a robot‐based autonomous estimation and detection [6], H ∞ filtering‐based estimation made for stochastic incomplete measurements [7], sequential Bayesian learning‐based dual estimation method [8], process noise identification‐based particle filter estimation [9], a non‐linear operator‐based estimation [10, 11], quantised measurements‐based state estimation [12], forward backward (FB)‐Kalman filter (KF)‐based estimation in fault diagnosis scheme [13], state estimation for static networks using weighted filters [14].…”

Section: Introductionmentioning

confidence: 99%

Enhanced distributed estimation based on prior information

Khalid¹,

Peng²,

Mahmoud

2015

IET signal process.

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In this paper, a distributed estimation algorithm using Bayesian-based forward backward Kalman filter (KF) is proposed for stochastic singular linear systems. The method incorporates generalised versions of KF for bounded cases with complete and incomplete prior information, followed by estimation fusion of these cases. The incorporated filters remain optimal given the cross-covariance of the local estimates. The proposed approach is validated on a coupled-tank system.

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Optimal minimum variance estimation for non-linear discrete-time multichannel systems

Cited by 6 publications

References 14 publications

Experimental application of nonlinear minimum variance estimation for fault detection systems

Experimental application of nonlinear minimum variance estimation for fault detection systems

Non-linear minimum variance estimation for fault detection systems

Enhanced distributed estimation based on prior information

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