Stratified statistical monitoring strategy for a multi-product manufacturing facility with early detection approach

Suman, Shankar; Das, Anupam

doi:10.1016/j.cie.2019.03.018

Cited by 8 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Preliminary Diagnosis highlights the broad area of the root cause(s) for the detected fault(s) whereas the Specific Diagnosis pinpoints the exact or the specific cause(s) within the broad area as determined by Preliminary Diagnosis. Diagnostic Statistic [18] is being used for estimating the contribution of the underlying process and feedstock characteristics to a detected fault represented by out-of-control observation(s). The Fault Diagnostic Statistic is based on the loadings of the PCA model, the expression for which has been sighted in equation (18).…”

Section: Fault Diagnosis Phasementioning

confidence: 99%

See 1 more Smart Citation

A nonlinear process monitoring strategy with a 2-phase fault diagnosis approach

Das

2022

Eng. Res. Express

Self Cite

View full text Add to dashboard Cite

The article delves into the development of Statistical Fault Detection and Diagnosis Strategies for an Integrated Steel Plant (ISP) taking into account the nonlinear relationship amongst the monitored Process and Feedstock Characteristics. The strategies being devised are based on Neural Network Fitting model cum Principal Component Analysis based technique (NNF-PCA) and Kernel Principal Component Analysis (KPCA) based technique. For detection of fault(s) Hotelling T2chart based on PCA and KPCA scores were employed. The article also proposes a 2-phase Fault Diagnosis approach christened as Preliminary Diagnosis phase and Specific Diagnosis phase. The Preliminary Diagnosis phase is based on Pattern Analysis of the control chart monitoring statistic observations and the Specific Diagnosis phase is based on the employment of appropriate Fault Diagnostic Statistic. The Preliminary Diagnosis reveals the broader source of assignable cause for the onset of the fault(s) and the Specific Diagnosis reveals the relative contribution of the individual Process and Feedstock characteristics. An in-depth comparative analysis between the NNF-PCA based strategy and KPCA based strategy w.r.t. three comparative aspects and four comparative parameters were carried out with their findings being duly highlighted which revealed the slight effectiveness of the KPCA based strategy with respect to the NNF-PCA based counterpart.

show abstract

Section: Fault Diagnosis Phasementioning

confidence: 99%

“…Diagnostic Statistic [18] is being used for estimating the contribution of the underlying process and feedstock characteristics to a detected fault represented by out-of-control observation(s). The Fault Diagnostic Statistic is based on the loadings of the PCA model, the expression for which has been sighted in equation (18).…”

Section: Fault Diagnosis Phasementioning

confidence: 99%