The Experts below are selected from a list of 18846 Experts worldwide ranked by ideXlab platform
Claude Delpha - One of the best experts on this subject based on the ideXlab platform.
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Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults
Energies, 2019Co-Authors: David Mba, Demba Diallo, Claude DelphaAbstract:This study puts forward a novel diagnostic approach based on canonical variate residuals (CVR) to implement incipient fault diagnosis for dynamic process monitoring. The conventional canonical variate analysis (CVA) fault Detection approach is extended to form a new monitoring index based on Hotelling’s T2 , Q and a CVR-based monitoring index, Td . A CVR-based contribution plot approach is also proposed based on Q and Td statistics. Two performance metrics: (1) false alarm rate and (2) Missed Detection rate are used to assess the effectiveness of the proposed approach. The CVR diagnostic approach was validated on incipient faults in a continuous stirred tank reactor (CSTR) system and an operational centrifugal compressor.
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an optimal fault Detection threshold for early Detection using kullback leibler divergence for unknown distribution data
Signal Processing, 2016Co-Authors: Abdulrahman Youssef, Claude Delpha, Demba DialloAbstract:The incipient fault Detection in industrial processes with unknown distribution of measurements signals and unknown changed parameters is an important problem which has received much attention these last decades. However most of the Detection methods (online and offline) need a priori knowledge on the signal distribution, changed parameters, and the change amplitude (Likelihood ratio test, Cusum, etc.). In this paper, an incipient fault Detection method that does not need any a priori knowledge on the signals distribution or the changed parameters is proposed. This method is based on the analysis of the Kullback-Leibler Divergence (KLD) of probability distribution functions. However, the performance of the technique is highly dependent on the setting of a Detection threshold and the environment noise level described through Signal to Noise Ratio (SNR) and Fault to Noise Ratio (FNR). In this paper, we develop an analytical model of the fault Detection performances (False Alarm Probability and Missed Detection Probability). Thanks to this model, an optimisation procedure is applied to optimally set the fault Detection threshold depending on the SNR and the fault severity. Compared to the usual settings, through simulation results and experimental data, the optimised threshold leads to higher efficiency for incipient fault Detection in noisy environment. HighlightsWe propose an incipient fault Detection method that does not need any a priori information on the signals distribution or the changed parameters.We show that the performance of the technique is highly dependent on the setting of a Detection threshold and the environment noise level.We develop an analytical model of the fault Detection performances (False Alarm Probability and Missed Detection Probability).Based on the aforementioned model, an optimisation procedure is applied to optimally set the fault Detection threshold depending on the noise and the fault severity.Compared to the usual settings, a performed validation of this approach with through simulation results and experimental data is given.
Demba Diallo - One of the best experts on this subject based on the ideXlab platform.
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Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults
Energies, 2019Co-Authors: David Mba, Demba Diallo, Claude DelphaAbstract:This study puts forward a novel diagnostic approach based on canonical variate residuals (CVR) to implement incipient fault diagnosis for dynamic process monitoring. The conventional canonical variate analysis (CVA) fault Detection approach is extended to form a new monitoring index based on Hotelling’s T2 , Q and a CVR-based monitoring index, Td . A CVR-based contribution plot approach is also proposed based on Q and Td statistics. Two performance metrics: (1) false alarm rate and (2) Missed Detection rate are used to assess the effectiveness of the proposed approach. The CVR diagnostic approach was validated on incipient faults in a continuous stirred tank reactor (CSTR) system and an operational centrifugal compressor.
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an optimal fault Detection threshold for early Detection using kullback leibler divergence for unknown distribution data
Signal Processing, 2016Co-Authors: Abdulrahman Youssef, Claude Delpha, Demba DialloAbstract:The incipient fault Detection in industrial processes with unknown distribution of measurements signals and unknown changed parameters is an important problem which has received much attention these last decades. However most of the Detection methods (online and offline) need a priori knowledge on the signal distribution, changed parameters, and the change amplitude (Likelihood ratio test, Cusum, etc.). In this paper, an incipient fault Detection method that does not need any a priori knowledge on the signals distribution or the changed parameters is proposed. This method is based on the analysis of the Kullback-Leibler Divergence (KLD) of probability distribution functions. However, the performance of the technique is highly dependent on the setting of a Detection threshold and the environment noise level described through Signal to Noise Ratio (SNR) and Fault to Noise Ratio (FNR). In this paper, we develop an analytical model of the fault Detection performances (False Alarm Probability and Missed Detection Probability). Thanks to this model, an optimisation procedure is applied to optimally set the fault Detection threshold depending on the SNR and the fault severity. Compared to the usual settings, through simulation results and experimental data, the optimised threshold leads to higher efficiency for incipient fault Detection in noisy environment. HighlightsWe propose an incipient fault Detection method that does not need any a priori information on the signals distribution or the changed parameters.We show that the performance of the technique is highly dependent on the setting of a Detection threshold and the environment noise level.We develop an analytical model of the fault Detection performances (False Alarm Probability and Missed Detection Probability).Based on the aforementioned model, an optimisation procedure is applied to optimally set the fault Detection threshold depending on the noise and the fault severity.Compared to the usual settings, a performed validation of this approach with through simulation results and experimental data is given.
Rajagopalan Srinivasan - One of the best experts on this subject based on the ideXlab platform.
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optimal variable selection for effective statistical process monitoring
Computers & Chemical Engineering, 2014Co-Authors: Kaushik Ghosh, Manojkumar Ramteke, Rajagopalan SrinivasanAbstract:Abstract In a typical large-scale chemical process, hundreds of variables are measured. Since statistical process monitoring techniques typically involve dimensionality reduction, all measured variables are often provided as input without weeding out variables. Here, we demonstrate that incorporating measured variables that do not provide any additional information about faults degrades monitoring performance. We propose a stochastic optimization-based method to identify an optimal subset of measured variables for process monitoring. The benefits of the reduced monitoring model in terms of improved false alarm rate, Missed Detection rate, and Detection delay is demonstrated through PCA based monitoring of the benchmark Tennessee Eastman Challenge problem.
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optimal variable selection for effective statistical process monitoring
Computers & Chemical Engineering, 2014Co-Authors: Kaushik Ghosh, Manojkumar Ramteke, Rajagopalan SrinivasanAbstract:Abstract In a typical large-scale chemical process, hundreds of variables are measured. Since statistical process monitoring techniques typically involve dimensionality reduction, all measured variables are often provided as input without weeding out variables. Here, we demonstrate that incorporating measured variables that do not provide any additional information about faults degrades monitoring performance. We propose a stochastic optimization-based method to identify an optimal subset of measured variables for process monitoring. The benefits of the reduced monitoring model in terms of improved false alarm rate, Missed Detection rate, and Detection delay is demonstrated through PCA based monitoring of the benchmark Tennessee Eastman Challenge problem.
Dongho Cho - One of the best experts on this subject based on the ideXlab platform.
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enhanced spectrum sensing scheme in cognitive radio systems with mimo antennae
IEEE Transactions on Vehicular Technology, 2011Co-Authors: Woongsup Lee, Dongho ChoAbstract:Cognitive radio (CR) is a promising technology for overcoming the lack of available communication bands. In CR technology, spectrum sensing is an important issue, which has recently been extensively studied. We provide a solution to the spectrum-sensing problem for multiple cognitive terminals (CTs) that takes into account the difference among CTs with respect to the probabilities of a false Detection and a Missed Detection. We optimize the spectrum-sensing performance by differentiating the number of spectrum-sensing operations that each CT performs. This has not previously been proposed in the literature. Moreover, we use a simultaneous spectrum-sensing and data transmission scheme that utilizes multiple-input-multiple-output (MIMO) antenna technology. As a result, the degradation of quality of service (QoS) that is caused by spectrum sensing can be reduced, and the throughput of CR systems can be increased, while maintaining the accuracy of spectrum sensing. Through performance analysis, we show that our proposed scheme can achieve the desired levels of performance with respect to the probabilities of a false Detection and a Missed Detection and improve the performance of the CR systems with respect to throughput and delay.
Swagat Kumar - One of the best experts on this subject based on the ideXlab platform.
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adaptive gaussian mixture probability hypothesis density based multi sensor multi target tracking
European Control Conference, 2019Co-Authors: Chinmay Shinde, Kaushik Das, Rolif Lima, Madhu Babu Vankadari, Swagat KumarAbstract:This paper addresses a novel multiple target tracking (MTT) problem in a decentralized sensors network (DSN) framework. The algorithm jointly estimates the number of targets and the states of the targets from a noisy measurement in the presence of data association uncertainty and Missed Detection. The standard GM-PHD filters estimate the multi-targets in a cluttered environment with an assumption that the target birth intensity is known or homogeneous. It results in inefficient tracking for new, occluded or Missed targets. The issue is addressed by the proposed adaptive Gaussian birth components based estimation. A method based on covariance intersection fusion is proposed to address inter-sensor target data association.
