Multiway discrete hidden Markov model‐based approach for dynamic batch process monitoring and fault classification

Yu, Jie

doi:10.1002/aic.12794

Cited by 35 publications

(29 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HMM is a kind of probabilistic model extended from Markov chains to generate the statistically inferential information on a series of state sequences [27]. In general, it contains finite numbers of hidden states, where each state outputs an observation at a certain time point.…”

Section: Hidden Markov Modelmentioning

confidence: 99%

Hidden Markov model-based approach for multimode process monitoring

Wang

Tan

Shi

2015

Chemometrics and Intelligent Laboratory Systems

View full text Add to dashboard Cite

Section: Hidden Markov Modelmentioning

confidence: 99%

Hidden Markov model-based approach for multimode process monitoring

Wang

Tan

Shi

2015

Chemometrics and Intelligent Laboratory Systems

View full text Add to dashboard Cite

“…For this reason several models of novelty detection have been proposed performing well on different types of data [14][15][16][17][18][19][20]. On the other hand it is clearly evident that there is no single best model for novelty detection and that the success depends not only on the type of the method used but also on the statistical properties of the data handled.…”

Section: Introductionmentioning

confidence: 99%

An on-line SAX and HMM-based anomaly detection and visualization tool for early disturbance discovery in a dynamic industrial process

Yiakopoulos

Gryllias

Chioua³

et al. 2016

Journal of Process Control

View full text Add to dashboard Cite

a b s t r a c tIn order to achieve an optimum and successful operation of an industrial process, it is important firstly to detect upsets, equipment malfunctions or other abnormal events as early as possible and secondly to identify and remove the cause of those events. Univariate and multivariate statistical process control methods have been widely applied in process industries for early fault detection and localization.The primary objective of the proposed research is the design of an anomaly detection and visualization tool that is able to present to the shift operator -and to the various levels of plant operation and company management -an early, global, accurate and consolidated presentation of the operation of major subgroups or of the whole plant, aided by a graphical form.Piecewise Aggregate Approximation (PAA) and Symbolic Aggregate Approximation (SAX) are considered as two of the most popular representations for time series data mining, including clustering, classification, pattern discovery and visualization in time series datasets. However SAX is preferred since it is able to transform a time series into a set of discrete symbols, e.g. into alphabet letters, being thus far more appropriate for a graphical representation of the corresponding information, especially for the shift operator. The methods are applied on individual time records of each process variable, as well as on entire groups of time records of process variables in combination with Hidden Markov Models. In this way, the proposed visualization tool is not only associated with a process defect, but it allows also identifying which specific abnormal situation occurred and if this has also occurred in the past. Case studies based on the benchmark Tennessee Eastman process demonstrate the effectiveness of the proposed approach. The results indicate that the proposed visualization tool captures meaningful information hidden in the observations and shows superior monitoring performance.

show abstract

“…35 Multiway hidden Markov model has been used for batch process monitoring, but fault diagnosis is achieved through the classification of different fault types. 36 Following a different technical pathway, hidden semi-Markov model (HSMM) is combined with MPCA for process monitoring where HSMM is used to model the multiphase batch operation by representing each phase as a state and then developing localized MPCA models. 37 Although the multiplicity of operating phases can be accounted for using the HSMM technique, the local MPCA models may not extract the non-Gaussian features within each operating phase for reliable fault detection and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Multiway independent component analysis mixture model and mutual information based fault detection and diagnosis approach of multiphase batch processes

Chen

Rashid

2013

AIChE Journal

Self Cite

View full text Add to dashboard Cite

Batch process monitoring is a challenging task, because conventional methods are not well suited to handle the inherent multiphase operation. In this study, a novel multiway independent component analysis (MICA) mixture model and mutual information based fault detection and diagnosis approach is proposed. The multiple operating phases in batch processes are characterized by non-Gaussian independent component mixture models. Then, the posterior probability of the monitored sample is maximized to identify the operating phase that the sample belongs to, and, thus, the localized MICA model is developed for process fault detection. Moreover, the detected faulty samples are projected onto the residual subspace, and the mutual information based non-Gaussian contribution index is established to evaluate the statistical dependency between the projection and the measurement along each process variable. Such contribution index is used to diagnose the major faulty variables responsible for process abnormalities. The effectiveness of the proposed approach is demonstrated using the fed-batch penicillin fermentation process, and the results are compared to those of the multiway principal component analysis mixture model and regular MICA method. The case study demonstrates that the proposed approach is able to detect the abnormal events over different phases as well as diagnose the faulty variables with high accuracy.

show abstract

Multiway discrete hidden Markov model‐based approach for dynamic batch process monitoring and fault classification

Cited by 35 publications

References 49 publications

Hidden Markov model-based approach for multimode process monitoring

Hidden Markov model-based approach for multimode process monitoring

An on-line SAX and HMM-based anomaly detection and visualization tool for early disturbance discovery in a dynamic industrial process

Multiway independent component analysis mixture model and mutual information based fault detection and diagnosis approach of multiphase batch processes

Contact Info

Product

Resources

About