Fault detection and isolation using parity relations

Gertler, János

doi:10.1016/s0967-0661(97)00047-6

Cited by 294 publications

(138 citation statements)

References 8 publications

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“…Now, there are three single-authored monographs in Gertler (1998), Chen and Patton (1999) and Mangoubi (1998). There are also several survey papers, of which we mention Isermann and Balle (1997), Isermann (1997) and Gertler (1997). The approaches to design suitable residuals are: parity space design (Chow and Willsky, 1984;Ding et al, 1999;Gertler, 1997), unknown input observer (Hong et al, 1997;Hou and Patton, 1998;Wang and Daley, 1996;Wunnenberg, 1990;Yang and Saif, 1998) and in the frequency domain (Frank and Ding, 199413;Sauter and Hamelin, 1999).…”

Section: Literaturementioning

confidence: 99%

Adaptive Filtering and Change Detection

Gustafsson¹

2001

595

699

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Section: Literaturementioning

confidence: 99%

Adaptive Filtering and Change Detection

Gustafsson¹

2001

595

699

View full text Add to dashboard Cite

“…This operation is also used, for example, in classical parity space approaches, see [15] and [32]. The input-output model can, in the general case, then be written as…”

Section: Reformulating the Modelmentioning

confidence: 99%

A method for quantitative fault diagnosability analysis of stochastic linear descriptor models

2013

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Analyzing fault diagnosability performance for a given model, before developing a diagnosis algorithm, can be used to answer questions like "How difficult is it to detect a fault fi?" or "How difficult is it to isolate a fault fi from a fault fj?". The main contributions are the derivation of a measure, distinguishability, and a method for analyzing fault diagnosability performance of discrete-time descriptor models. The method, based on the Kullback-Leibler divergence, utilizes a stochastic characterization of the different fault modes to quantify diagnosability performance. Another contribution is the relation between distinguishability and the fault to noise ratio of residual generators. It is also shown how to design residual generators with maximum fault to noise ratio if the noise is assumed to be i.i.d. Gaussian signals. Finally, the method is applied to a heavy duty diesel engine model to exemplify how to analyze diagnosability performance of non-linear dynamic models.

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“…Firstly, a precise mathematical model is required to accurately predict system performance as model-based methods require such a model of the supervised process [11]. For most systems, such models are often very difficult to obtain.…”

Section: Model-based Condition Monitoringmentioning

confidence: 99%

A general regression neural network model for gearbox fault detection using motor operating parameters

Baqqar

Wang

Ahmed

et al. 2012

Proceedings of 2012 UKACC International Conference on Control

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Abstract. Condition monitoring of a gearbox is a very important activity because of the importance of gearboxes in power transmission in many industrial processes. Thus there has always been a constant pressure to improve measuring techniques and analytical tools for early detection of faults in gearboxes. This study forces on developing gearbox monitoring methods based on operating parameters which are available in machine control processes rather than using additional measurements such as vibration and acoustics used in many studies. To utilise these parameters for gearbox monitoring, this paper examines a model based approach in which a data model has been developed using a General Regression Neural Network (GRNN) to captures the nonlinear connections between the electrical current of driving motor and control parameters such as load settings and temperatures based on a two stage helical gearbox power transmission system. Using the model a direct comparison can be made between the measured and predicted values to find abnormal gearbox conditions of different gear tooth breakages based on a threshold setup in developing the model.

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Fault detection and isolation using parity relations

Cited by 294 publications

References 8 publications

Adaptive Filtering and Change Detection

Adaptive Filtering and Change Detection

A method for quantitative fault diagnosability analysis of stochastic linear descriptor models

A general regression neural network model for gearbox fault detection using motor operating parameters

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