Influence Matrix Approach to Fault Diagnosis of Parameters in Dynamic Systems

Ono, Toshihiko; Kumamaru, Toshio; Maeda, Akihiro; Sagara, Setsuo; Kumamaru, Kousuke

doi:10.1109/tie.1987.350967

Cited by 38 publications

(10 citation statements)

References 11 publications

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“…This is because the transformation in (15) is irreversible. Thus different from the existing parameter estimation-based fault diagnosis methods including [9,30], the model parameter…”

Section: Fault Diagnosis Algorithm Based On the Feature Parameter Idementioning

confidence: 96%

“…Since physical faults need to be determined based on the estimated faulty model parameters, a known relationship between the faulty model parameters and the physical faults is normally required [9]. Thus, an extra assumption is made.…”

Section: Assumptionmentioning

confidence: 99%

“…Thus in contrast to [9,30], we adopt the direction change ofθ i instead of the direction change ofθ i to distinguish which physical fault has occurred. For better understanding, the basic idea of fault isolation is first given.…”

Section: Fault Diagnosis Algorithm Based On the Feature Parameter Idementioning

confidence: 99%

“…A comprehensive review of the early works on parameter estimation‐based fault diagnosis is provided in [8]. In [9], the influence matrix is adopted to detect physical faults by identifying model parameters. Some extension results based on [9] have been reported such as [10, 11].…”

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis based on parameter estimation in closed‐loop systems

Zhai

Wang

2015

IET Control Theory & Applications

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In this study, parameter faults in a class of non-identifiable closed-loop multiple-input multiple-output systems are considered. A new parameter estimation-based fault diagnosis method is proposed. It is known that in open-loop systems, the system parameters can be identified directly and the on-line identification results can be used for fault detection and isolation. However, as the closed-loop system is non-identifiable because of the correlation introduced by the controller, unique optimal parameter estimation solution cannot be obtained. To address such an issue, a new method to detect and isolate parameter faults of closed-loop systems without persistent excitation condition is proposed. A reduced-order model is firstly constructed, which is the projection of the original model onto the orthogonal direction of the controller. By doing this, the aforementioned correlation can be successfully removed. The parameters of the newly constructed model, called as feature parameters, are then identified. The physical faults are finally detected and isolated based on the on-line identification results of the feature parameters, the projection direction and the known influence matrix. Simulation results are given to show the effectiveness of the proposed method.

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“…This is because the transformation in (15) is irreversible. Thus different from the existing parameter estimation-based fault diagnosis methods including [9,30], the model parameter…”

Section: Fault Diagnosis Algorithm Based On the Feature Parameter Idementioning

confidence: 96%

Section: Assumptionmentioning

confidence: 99%

Section: Fault Diagnosis Algorithm Based On the Feature Parameter Idementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault diagnosis based on parameter estimation in closed‐loop systems

Zhai

Wang

2015

IET Control Theory & Applications

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“…A fault is detected if the cluster centroid falls outside the elliptical confidence region defined by (7). E. Fault isolation based on influence matrix method A method for fault diagnosis based on an influence matrix (IMX) was first developed by Ono and co-workers, [10], In (8), the partial derivative of the feature vector, 0, with respect to the ith physical parameter, Yi, is termed the ith influence vector, denoted by D2i. For each physical parameter, YJ , the associated influence vector is defined as: nom dffr f2j = 121=ynom ;forj =l,...,p…”

Section: Fault Detection and Diagnosis Approachmentioning

confidence: 99%

Diagnosis of Parametric Faults Based on Identification and Statistical Methods

Palma

Coito

Silva³

Proceedings of the 44th IEEE Conference on Decision and Control

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This paper proposes a fault diagnosis approach for detection and diagnosis of parametric faults in linear dynamical systems. It is assumed that the system is modeled by an auto-regressive (ARX) model, and a recursive least-squares (RLS) algorithm with variable forgetting factor is used for online parameter identification. The fault detection task is done using a statistical principal component analysis (PCA) method applied to ARX parameters. The fault isolation is done using an influence matrix method. The proposed methodology is applied to a simulation model of a DC motor under closed-loop control, and the performance is discussed.

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Design of a real-time knowledge-based controller with applications in hydraulic turbine generator systems

Jiang¹,

Doraiswami

1989

J Intell Robot Syst

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Influence Matrix Approach to Fault Diagnosis of Parameters in Dynamic Systems

Cited by 38 publications

References 11 publications

Fault diagnosis based on parameter estimation in closed‐loop systems

Fault diagnosis based on parameter estimation in closed‐loop systems

Diagnosis of Parametric Faults Based on Identification and Statistical Methods

Design of a real-time knowledge-based controller with applications in hydraulic turbine generator systems

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