System-level operational diagnosability analysis in quasi real-time fault diagnosis: The probabilistic approach

Cui, Yiqian; Shi, Junyou; Wang, Zili

doi:10.1016/j.jprocont.2014.06.014

Cited by 26 publications

(7 citation statements)

References 31 publications

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“…Fault detectability and isolability can be quantified for a given model by using performance metrics that quantifies the separation between measurements from different faults (Cui et al, 2014), for example the Kullback-Leibler divergence (Basseville, 2001). Comments regarding the paper (Basseville, 2001) are presented in (Kinnaert et al, 2001) where Nyberg suggests that the KullbackLeibler divergence can be a suitable candidate measure to quantify isolability performance for a given model when using stochastic representations of each fault mode.…”

Section: Diagnosability Analysis Of Uncertain Systemsmentioning

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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Section: Diagnosability Analysis Of Uncertain Systemsmentioning

confidence: 99%

Diagnosability performance analysis of models and fault detectors

Jung

2015

Linköping Studies in Science and Technology. Dissertations

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“…Fault feature analysis is the premise of fault diagnosis. The common fault analysis methods include domain analysis, frequency domain analysis and time frequency domain analysis [6]. Time domain analysis is the earliest method used in the mechanical fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Zhang

Jiang

et al. 2019

Chin. J. Mech. Eng.

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Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

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“…In contrast to static models, dynamic models provide more useful information and ensure that more and smaller faults can be detected [20], which is critical for fault diagnosis. For the dynamic models, Cui et al [21] extend the component‐level information to system‐level monitoring data analysis via control relations, and the system‐level operational diagnosability analysis is given to provide useful information for the fault diagnosis process. However, this paper considers neither process noise nor measurement noise, which may lead to incorrect results in practice.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven method for the quantitative fault diagnosability analysis of dynamic systems

Wang

et al. 2019

IET Control Theory & Applications

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System-level operational diagnosability analysis in quasi real-time fault diagnosis: The probabilistic approach

Cited by 26 publications

References 31 publications

Diagnosability performance analysis of models and fault detectors

Diagnosability performance analysis of models and fault detectors

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Data‐driven method for the quantitative fault diagnosability analysis of dynamic systems

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