Integration of Simulation and State Observers for Online Fault Detection of Nonlinear Continuous Systems

Bregón, Aníbal; Alonso-González, Carlos J.; Pulido, Belarmino

doi:10.1109/tsmc.2014.2322581

Cited by 30 publications

(14 citation statements)

References 29 publications

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“…In these papers, attention is paid to the fact that the path of fault propagation in SDG must agree with the recorded symptoms sequences. Another approach is applying Temporal Causal Graph to transcend system in diagnostics [10,[31][32][33][34]. Temporal information has also been included in discrete event systems [13,35,36].…”

Section: Related Workmentioning

confidence: 99%

Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence

2021

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The paper analyses the research problem of conducting diagnostic reasoning for dynamic objects to eliminate the possibility of formulating false diagnoses resulting from different delays of the symptoms related to a particular fault while simultaneously striving to obtain high distinguishability. The research aimed to develop a new diagnostic inference method robust to symptom delays and characterised by high accuracy of generated diagnosis. Known methods ensuring the correctness of inference in the case of symptom delays but at the cost of reducing distinguishability of faults have been characterised. A new inference method was developed, which uses the three-valued residual evaluation and knowledge regarding elementary symptom sequences. A formal description of the diagnosing system and the proposed method are given. The method of obtaining the knowledge about the order of symptoms based on a cause-and-effect graph and was characterised. The method’s effectiveness was presented in simulation studies on the example of diagnosing a set of serially connected tanks. The comparison of the fault distinguishability obtained using the proposed method and other approaches illustrates the new method’s advantages.

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Section: Related Workmentioning

confidence: 99%

Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence

2021

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“…The above definition refers to the causal interpretation of differential constraints in a bipartite graph and has been studied by, e.g., Blanke et al (2016), Bregon et al (2014) or Frisk et al (2012). Note that a causal matching in the present context is equivalent to derivative causality in the mentioned references.…”

Section: 3mentioning

confidence: 99%

“…However, they can be applied only if the system is observable and, thus, makes it possible to reconstruct the internal variables for the measured pair (u(t), y(t)) of input and output signals. Structural analysis and observer-based fault diagnosis are two research topics which have been tackled completely separately in the past, with few exceptions like, e.g., the work of Bregon et al (2014). For applications, however, it is necessary to find a way for applying observer-based techniques to those systems that have been shown to be structurally diagnosable.…”

Section: Introductionmentioning

confidence: 99%

From Structural Analysis to Observer–Based Residual Generation for Fault Detection

Pröll

Lunze

Jarmolowitz

2018

International Journal of Applied Mathematics and Computer Science

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This paper combines methods for the structural analysis of bipartite graphs with observer-based residual generation. The analysis of bipartite structure graphs leads to over-determined subsets of equations within a system model, which make it possible to compute residuals for fault detection. In observer-based diagnosis, by contrast, an observability analysis finds observable subsystems, for which residuals can be generated by state observers. This paper reveals a fundamental relationship between these two graph-theoretic approaches to diagnosability analysis and shows that for linear systems the structurally over-determined set of model equations equals the output connected part of the system. Moreover, a condition is proved which allows us to verify structural observability of a system by means of the corresponding bipartite graph. An important consequence of this result is a comprehensive approach to fault detection systems, which starts with finding the over-determined part of a given system by means of a bipartite structure graph and continues with designing an observerbased residual generator for the fault-detectable subsystem found in the first step.

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“…To fully understand the fault mechanism and obtain agreeable fault detection results, many researchers performed numerical simulations to analyze faults. To detect faults online in nonlinear continuous systems, Bregon et al [10] used the simulation and state observer models to obtain the final fault diagnosis results. To detect the abnormal conditions of structures, two kinds of wavelet-based numerical simulation models were carried out to calculate the dynamic responses of a shaft [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Liu

Huang

Xiang

2020

Sensors

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Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.

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Integration of Simulation and State Observers for Online Fault Detection of Nonlinear Continuous Systems

Cited by 30 publications

References 29 publications

Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence

Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence

From Structural Analysis to Observer–Based Residual Generation for Fault Detection

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

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