Fault diagnosis of multivariate systems using pattern recognition and multisensor data analysis technique

Akbaryan, Fardin; Bishnoi, P. R.

doi:10.1016/s0098-1354(01)00701-3

Cited by 59 publications

(32 citation statements)

References 22 publications

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“…Maurya et al [31] also proposed an interval-halving method for trend extraction and a fuzzy matching based method for similarity estimation and inferences. Akbarya and bishnoi [32] used wavelet-based method to extract features and binary decision tree to classify them. All the above, QTA-based methods require training data, while Singhal and Seborg [33] proposed a pattern-matching-strategy requires no training data but a huge amount of historical data.…”

Section: Related Work For Tep Fault Diagnosismentioning

confidence: 99%

Fault diagnosis of Tennessee Eastman process using signal geometry matching technique

Xiao

2011

EURASIP J. Adv. Signal Process.

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This article employs adaptive rank-order morphological filter to develop a pattern classification algorithm for fault diagnosis in benchmark chemical process: Tennessee Eastman process. Rank-order filtering possesses desirable properties of dealing with nonlinearities and preserving details in complex processes. Based on these benefits, the proposed algorithm achieves pattern matching through adopting one-dimensional adaptive rank-order morphological filter to process unrecognized signals under supervision of different standard signal patterns. The matching degree is characterized by the evaluation of error between standard signal and filter output signal. Initial parameter settings of the algorithm are subject to random choices and further tuned adaptively to make output approach standard signal as closely as possible. Data fusion technique is also utilized to combine diagnostic results from multiple sources. Different fault types in Tennessee Eastman process are studied to manifest the effectiveness and advantages of the proposed method. The results show that compared with many typical multivariate statistics based methods, the proposed algorithm performs better on the deterministic faults diagnosis.

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Section: Related Work For Tep Fault Diagnosismentioning

confidence: 99%

Fault diagnosis of Tennessee Eastman process using signal geometry matching technique

Xiao

2011

EURASIP J. Adv. Signal Process.

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“…Studies on applications of PCA to process industries can be found in Akbaryan and Bishnoi (2001), Amand et al (2001), Kano et al (2001Kano et al ( , 2002, Kruger et al (2001), McAvoy (2002), Ü ndey and Cinar (2002), Wong and Wang (2003), Lee et al (2004), Miletic et al (2004).…”

Section: Principal Component Analysismentioning

confidence: 99%

Artificial intelligence for monitoring and supervisory control of process systems

Uraikul

Chan

Tontiwachwuthikul

2007

Engineering Applications of Artificial Intelligence

174

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“…Therefore, the performances of many Qualitative Trend Analysis (QTA) approaches could be affected because of their traditional feature extraction and matching techniques. For example, for two temporal sequences showing the same trend but different twisted waveforms, employing Euclidean distance metric to measure the similarity can cause undesirable performance [4][5].…”

Section: Introductionmentioning

confidence: 99%

Application of Morphological Filtering and Dynamic Time Warping in Fault Diagnosis of Complex System

Han¹,

Xie²

2014

IJCA

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Fault diagnosis of multivariate systems using pattern recognition and multisensor data analysis technique

Cited by 59 publications

References 22 publications

Fault diagnosis of Tennessee Eastman process using signal geometry matching technique

Fault diagnosis of Tennessee Eastman process using signal geometry matching technique

Artificial intelligence for monitoring and supervisory control of process systems

Application of Morphological Filtering and Dynamic Time Warping in Fault Diagnosis of Complex System

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