Robust Fault Diagnosis in Dynamic Systems

Patton, Ron J.; Kang'ethe, S.M.

doi:10.1007/978-94-009-0429-3_10

Cited by 98 publications

(156 citation statements)

References 9 publications

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“…Two relevant tasks for predictive maintenance are failure detection and failure prediction. For the purpose of early failure detection, techniques from novelty, anomaly and outlier detection have been used (Basseville 1988;Basseville and Nikiforov 1993;Patton et al 2010;Papadimitropoulos et al 2007;Katipamula and Brambley 2005;Yilboga et al 2010).…”

Section: Related Workmentioning

confidence: 99%

Sequential anomalies: a study in the Railway Industry

2016

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Concerned with predicting equipment failures, predictive maintenance has a high impact both at a technical and at a financial level. Most modern equipments have logging systems that allow us to collect a diversity of data regarding their operation and health. Using data mining models for anomaly and novelty detection enables us to explore those datasets, building predictive systems that can detect and issue an alert when a failure starts evolving, avoiding the unknown development up to breakdown. In the present case, we use a failure detection system to predict train door breakdowns before they happen using data from their logging system. We use sensor data from pneumatic valves that control the open and close cycles of a door. Still, the failure of a cycle does not necessarily indicates a breakdown. A cycle might fail due to user interaction. The goal of this study is to detect structural failures in the automatic train door system, not when there is a cycle failure, but when there are sequences of cycle failures. We study three methods for such structural failure detection: outlier detection, anomaly detection and novelty detection, using different windowing strategies. We propose a two-stage approach, where the output of a point-anomaly algorithm is post-processed by a low-pass filter to obtain a subsequence-anomaly detection. The main result of the two-level architecture is a strong impact in the false alarm rate.

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Section: Related Workmentioning

confidence: 99%

Sequential anomalies: a study in the Railway Industry

2016

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“…To quantify the performance of a residual generator (25), the following definition is used. Table 5 Computed distinguishability of (2) Definition 5 (Fault to noise ratio) For a residual generator (25) where e is a stochastic vector with covariance Λ e .…”

Section: Relation To Residual Generatorsmentioning

confidence: 99%

“…Table 5 Computed distinguishability of (2) Definition 5 (Fault to noise ratio) For a residual generator (25) where e is a stochastic vector with covariance Λ e . The fault to noise ratio, FNR, for a given fault f i = θ, is defined as the ratio between the amplified fault time profile, λ(θ) = γN (H Fj ) F θ and the standard deviation of the noise σ as…”

Section: Relation To Residual Generatorsmentioning

confidence: 99%

“…Assumption (6) on the model (4) directly implies that (6) is fulfilled also for the residual generator (25). However, there is no guarantee that (25) fulfills (18) and the 3-step procedure after Theorem 1 must be used.…”

Section: Theorem 2 a Residual Generator (25) For A Model (4)mentioning

confidence: 99%

“…As technical systems have grown in complexity, the demand for functional safety and reliability has drawn significant research in model-based fault detection and isolation. The maturity of the research field is verified by the amount of existing reference literature, for example [16], [19], and [25].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Adaptive Filtering and Change Detection

Gustafsson¹

2001

602

699

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Robust Fault Diagnosis in Dynamic Systems

Cited by 98 publications

References 9 publications

Sequential anomalies: a study in the Railway Industry

Sequential anomalies: a study in the Railway Industry

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

Adaptive Filtering and Change Detection

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