Data-driven and adaptive statistical residual evaluation for fault detection with an automotive application

Svärd, Carl; Nyberg, Mattias; Frisk, Erik; Krysander, Mattias

doi:10.1016/j.ymssp.2013.11.002

Cited by 37 publications

(15 citation statements)

References 58 publications

(116 reference statements)

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“…Model-based methods are typically used if mathematical models of the process and faults are available, whereas data-driven methods use historical data of complex systems to determine occurring faults, see, e.g., [24]. Although complex, the drilling process is limited in size and can be divided into two subsystems separated by the drill bit.…”

Section: Model-based Fault Detection and Isolationmentioning

confidence: 99%

Incident detection and isolation in drilling using analytical redundancy relations

Willersrud

Blanke

Imsland

2015

Control Engineering Practice

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Early diagnosis of incidents that could delay or endanger a drilling operation for oil or gas is essential to limit field development costs. Warnings about downhole incidents should come early enough to allow intervention before it develops to a threat, but this is difficult, since false alarms must be avoided. This paper employs model-based diagnosis using analytical redundancy relations to obtain residuals which are affected differently by the different incidents. Residuals are found to be non-Gaussian -they follow a multivariate t-distribution -hence, a dedicated generalized likelihood ratio test is applied for change detection. Data from a 1400 meter horizontal flow loop test facility is used to assess the diagnosis method. Diagnosis properties of the method are investigated assuming either with available downhole pressure sensors through wired drill pipe or with only topside measurements available. In the latter case, isolation capability is shown to be reduced to group-wise isolation, but the method would still detect all serious events with the prescribed false alarm probability.

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Section: Model-based Fault Detection and Isolationmentioning

confidence: 99%

Incident detection and isolation in drilling using analytical redundancy relations

Willersrud

Blanke

Imsland

2015

Control Engineering Practice

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“…Such methods were covered in the survey by [31] and a study of data driven diagnosis showed design for isolability of different types of faults in automotive engines [32]. The same authors suggested an explicit comparison of the probability distribution of the residual, estimated online using current data, with no-fault residual distributions in [33] and found that tiny faults, i.e. also incipient ones, could be diagnosed in conditions where traditional methods would fall short.…”

Section: Detection Behaviour For Incipient Faultsmentioning

confidence: 99%

Mooring system diagnosis and structural reliability control for position moored vessels

Fang

Blanke

Leira

2015

Control Engineering Practice

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Early diagnosis and fault-tolerant control are essential for safe operation of floating platforms where mooring systems maintain vessel position and must withstand environmental loads. This paper considers two critical faults, line breakage and loss of a buoyancy element and employs vector statistical change detection for timely diagnosis of faults. Diagnosis design is scrutinized and a procedure is proposed based on specified false alarm probability and estimation of the distribution of the test statistics on which change detection is based. A structural reliability index is applied for monitoring the safety level of each mooring line and, a set-point chasing algorithm accommodates the effects of line failure, as an integral part of the reliabilitybased set-point chasing control algorithm. The feasibility of the diagnosis and of the fault-tolerant control strategy is verified in model basin tests.

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“…p fi = p NF . The Kullback-Leibler divergence itself has also been used as a hypothesis test for fault detection in, for example, (Zeng et al, 2014;Svärd et al, 2014) and (Bittencourt et al, 2014). An interesting analysis of the Kullback-Leibler divergence using the Neyman-Pearson lemma is found in (Eguchi and Copas, 2006).…”

Section: Distinguishabilitymentioning

confidence: 99%

Diagnosability performance analysis of models and fault detectors

Jung

2015

Linköping Studies in Science and Technology. Dissertations

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The cover: Graphical illustration of the proposed quantitative fault detectability and isolability performance measure, called distinguishability (Di,j(θ)). Typeset with L A T E X 2εPrinted by LiU-Tryck, Linköping, Sweden 2015 To Ylva i Abstract Model-based diagnosis compares observations from a system with predictions using a mathematical model to detect and isolate faulty components. Analyzing which faults that can be detected and isolated given the model gives useful information when designing a diagnosis system. This information can be used, for example, to determine which residual generators can be generated or to select a sufficient set of sensors that can be used to detect and isolate the faults. With more information about the system taken into consideration during such an analysis, more accurate estimations can be computed of how good fault detectability and isolability that can be achieved.Model uncertainties and measurement noise are the main reasons for reduced fault detection and isolation performance and can make it difficult to design a diagnosis system that fulfills given performance requirements. By taking information about different uncertainties into consideration early in the development process of a diagnosis system, it is possible to predict how good performance can be achieved by a diagnosis system and avoid bad design choices. This thesis deals with quantitative analysis of fault detectability and isolability performance when taking model uncertainties and measurement noise into consideration. The goal is to analyze fault detectability and isolability performance given a mathematical model of the monitored system before a diagnosis system is developed.A quantitative measure of fault detectability and isolability performance for a given model, called distinguishability, is proposed based on the Kullback-Leibler divergence. The distinguishability measure answers questions like "How difficult is it to isolate a fault f i from another fault f j ?". Different properties of the distinguishability measure are analyzed. It is shown for example, that for linear descriptor models with Gaussian noise, distinguishability gives an upper limit for the fault to noise ratio of any linear residual generator. The proposed measure is used for quantitative analysis of a nonlinear mean value model of gas flows in a heavy-duty diesel engine to analyze how fault diagnosability performance varies for different operating points. It is also used to formulate the sensor selection problem, i.e., to find a cheapest set of available sensors that should be used in a system to achieve required fault diagnosability performance.As a case study, quantitative fault diagnosability analysis is used during the design of an engine misfire detection algorithm based on the crankshaft angular velocity measured at the flywheel. Decisions during the development of the misfire detection algorithm are motivated using quantitative analysis of the misfire detectability performance showing, for example, varying detection performance a...

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Data-driven and adaptive statistical residual evaluation for fault detection with an automotive application

Cited by 37 publications

References 58 publications

Incident detection and isolation in drilling using analytical redundancy relations

Incident detection and isolation in drilling using analytical redundancy relations

Mooring system diagnosis and structural reliability control for position moored vessels

Diagnosability performance analysis of models and fault detectors

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