A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks

Cai, Baoping; Liu, Hanlin; Xie, Min

doi:10.1016/j.ymssp.2016.04.019

Cited by 245 publications

(85 citation statements)

References 34 publications

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“…Normally, the computer software locates the liquid level by extracting the reflected pulse, so for an algorithm, the ability of identifying the acoustic pulse is what we are concerned about. In order to quantitatively reveal the identification ability of FFT and ACF, the Crest Factor is introduced as an evaluation index [46,47]. Crest Factor (aka Peak-to-Average Ratio) is defined as peak value divided by the effective value of a signal [48]:…”

Section: Experimental Schemesmentioning

confidence: 99%

A Novel Acoustic Liquid Level Determination Method for Coal Seam Gas Wells Based on Autocorrelation Analysis

Zhang

Fan

et al. 2017

Energies

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Abstract:In coal seam gas (CSG) wells, water is periodically removed from the wellbore in order to keep the bottom-hole flowing pressure at low levels, facilitating the desorption of methane gas from the coal bed. In order to calculate gas flow rate and further optimize well performance, it is necessary to accurately monitor the liquid level in real-time. This paper presents a novel method based on autocorrelation function (ACF) analysis for determining the liquid level in CSG wells under intense noise conditions. The method involves the calculation of the acoustic travel time in the annulus and processing the autocorrelation signal in order to extract the weak echo under high background noise. In contrast to previous works, the non-linear dependence of the acoustic velocity on temperature and pressure is taken into account. To locate the liquid level of a coal seam gas well the travel time is computed iteratively with the non-linear velocity model. Afterwards, the proposed method is validated using experimental laboratory investigations that have been developed for liquid level detection under two scenarios, representing the combination of low pressure, weak signal, and intense noise generated by gas flowing and leakage. By adopting an evaluation indicator called Crest Factor, the results have shown the superiority of the ACF-based method compared to Fourier filtering (FFT). In the two scenarios, the maximal measurement error from the proposed method was 0.34% and 0.50%, respectively. The latent periodic characteristic of the reflected signal can be extracted by the ACF-based method even when the noise is larger than 1.42 Pa, which is impossible for FFT-based de-noising. A case study focused on a specific CSG well is presented to illustrate the feasibility of the proposed approach, and also to demonstrate that signal processing with autocorrelation analysis can improve the sensitivity of the detection system.

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Section: Experimental Schemesmentioning

confidence: 99%

A Novel Acoustic Liquid Level Determination Method for Coal Seam Gas Wells Based on Autocorrelation Analysis

Zhang

Fan

et al. 2017

Energies

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“…However, the task of model estimation and validation for various combinations of cardinality and local dimensionality could be tedious. Bayesian estimation can simplify the task as it penalizes complex models and allows for model selection without cross-validation [16][17]. However, a complete Bayesian analysis for MCCA may be infeasible.…”

Section: Introductionmentioning

confidence: 99%

A Mixture of Variational Canonical Correlation Analysis for Nonlinear and Quality-Relevant Process Monitoring

Liu

Zhao

et al. 2018

IEEE Trans. Ind. Electron.

Self Cite

115

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Proper monitoring of quality-related variables in industrial processes is nowadays one of the main worldwide challenges with significant safety and efficiency implications. Variational Bayesian mixture of Canonical correlation analysis (VBMCCA)-based process monitoring method was proposed in this paper to predict and diagnose these hard-to-measure quality-related variables simultaneously. Use of Student's t-distribution, rather than Gaussian distribution, in the VBMCCA model makes the proposed process monitoring scheme insensitive to disturbances, measurement noises and model discrepancies. A sequential perturbation method together with derived parameter distribution of VBMCCA is employed to approach the uncertainty levels, which is able to provide confidence interval around the predicted values and give additional control line, rather than just a certain absolute control limit, for process monitoring. The proposed process monitoring framework has been validated in a Wastewater Treatment Plant (WWTP) simulated by Benchmark Simulation Model (BSM) with abrupt changes imposing on a sensor and a real WWTP with filamentous sludge bulking. The results show that the proposed methodology is capable of detecting sensor faults and process faults with satisfactory accuracy.

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“…Bayesian network is a commonly used tool in probabilistic reasoning of uncertainty in industrial processes [3]. Cai et al [4][5][6] proposed a multisource information fusion based fault diagnosis methodology using Bayesian network, which can increase the fault diagnostic accuracy for single fault.…”

Section: Introductionmentioning

confidence: 99%

A Maximum Entropy Multisource Information Fusion Method to Evaluate the MTBF of Low-Voltage Switchgear

Wang

Zhang

Wang

et al. 2018

Discrete Dynamics in Nature and Society

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When analyzing the reliability of low-voltage switchgear by Bayesian method, the maximum entropy multisource information fusion method was proposed to obtain the prior information of low-voltage switchgear and then evaluate the reliability. The historical data of low-voltage switchgear was collected and organized from a manufacturer. According to the expert experience and the data, the creditability analysis and the compatibility test were presented by the Smirnov test method. Based on the high creditability and compatibility, the result of the maximum entropy multisource information fusion method is the determination of prior information. Therefore, the distribution type of the prior information was confirmed by using the maximum entropy method, and the parameter of the prior information was received by bootstrap method with MATLAB. Then the posterior distribution was obtained to evaluate the MTBF of low-voltage switchgear. Finally, the historical data of years from 2007 to 2010 was taken as prior information to illustrate the maximum entropy multisource information fusion method and to get the MTBF of lowvoltage switchgear. The evaluation result reduces the experimental period and test cost, which is an improvement for the reliability evaluation and management of low-voltage switchgear and also an improvement for other systems with simple sample data. Compared with traditional Bayesian networks, the proposed method can fuse experts experience and historical data and has advantages for the use of prior information effectively.Hindawi

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A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks

Cited by 245 publications

References 34 publications

A Novel Acoustic Liquid Level Determination Method for Coal Seam Gas Wells Based on Autocorrelation Analysis

A Novel Acoustic Liquid Level Determination Method for Coal Seam Gas Wells Based on Autocorrelation Analysis

A Mixture of Variational Canonical Correlation Analysis for Nonlinear and Quality-Relevant Process Monitoring

A Maximum Entropy Multisource Information Fusion Method to Evaluate the MTBF of Low-Voltage Switchgear

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