Analysis of smearing-out in contribution plot based fault isolation for Statistical Process Control

Kerkhof, Pieter Van den; Vanlaer, Jef; Gins, Geert; Impe, Jan Van

doi:10.1016/j.ces.2013.08.007

Cited by 87 publications

(45 citation statements)

References 32 publications

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“…Fault identification for batch processes-if even discussed-mostly occurs after fault detection via analysis of contribution plots, despite their suffering from fault smearing, which possibly leading to incorrect diagnosis [82,77]. A few exceptions exist, such MKLFDA, where fault detection and identification occur simultaneously [95].…”

Section: Introductionmentioning

confidence: 97%

An extensive reference dataset for fault detection and identification in batch processes

Impe¹,

Gins²

2015

Chemometrics and Intelligent Laboratory Systems

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Close process monitoring (i.e., detection and identification of disturbances) is important to achieve high process efficiency and safety. The Tennessee Eastman process is an extensive benchmark dataset for fault detection and identification, but it is only representative for continuous processes because it does not contain the inherent nonstationarity that complicates monitoring of batch processes. Nevertheless, batch processes also play an important role in many types of industry. This paper therefore presents an extensive reference dataset for benchmarking data-driven methodologies for fault detection and identification in batch processes. The original Pensim model [10] is expanded with sensor noise. By changing the properties of the initial conditions and/or model parameters, four subsets of different complexity are generated, each containing 400 batches with normal operation. To correctly assess the fault detection and identification in batch processes, 15 faults are simulated with various amplitudes and onset times for a total of 22,200 faulty batches for each subset, or 90,400 batches in total. Analysis of the data indicates that the presented types of process faults and their various amplitudes in each of the four subsets present a suitable benchmark for fault detection and identification in batch processes. The dataset is freely available at http://cit.kuleuven.be/biotec/batchbenchmark.

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Section: Introductionmentioning

confidence: 97%

An extensive reference dataset for fault detection and identification in batch processes

Impe¹,

Gins²

2015

Chemometrics and Intelligent Laboratory Systems

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“…From (6) and (8), it is clear that a vector w that maximizes J (w) is equal to calculating the generalized eigenvectors of the eigenvalue problem…”

Section: A Fault Degradation-oriented Discriminant Analysismentioning

confidence: 99%

Critical-to-Fault-Degradation Variable Analysis and Direction Extraction for Online Fault Prognostic

Zhao

Gao

2017

IEEE Trans. Contr. Syst. Technol.

103

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Fault prognostic determines whether a failure is impending and estimates how soon an incident will occur; it is nowadays recognized as a key feature in maintenance strategies. For slowly time-varying autocorrelated fault process, the fault degradation process can be revealed for fault prognostic. Based on this assumption, a fault degradation modeling and online fault prognostic strategy is developed in this paper. A stability factor (SF) is defined to evaluate the changing characteristics of process status and a SF-based non-steady faulty variable identification method is developed to find critical-tofault-degradation variables. A fault degradation-oriented Fisher discriminant analysis is proposed on the selected variables to model the fault evolution process. Uninformative fault effects that do not present degradation are excluded, so that the critical fault degradation information can be focused on. The proposed method is verified by three cases, including a numerical case, cut-made process of cigarette, and the well-known Tennessee Eastman benchmark chemical process. Index Terms-Fault degradation, fault degradation-orientedFisher discriminant analysis (FDFDA), fault prognostic, nonsteady variable selection, stability factor (SF).

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“…They do not contain the key ingredient for full diagnosis and fault isolability: causality. This is why, when "inquired" about which variables are mostly contributing to a given change in the monitoring statistics (the purpose of contribution plot analysis), these methods are bound to put forward the set of variables that are correlated with the fault origin without distinguishing which variables are the cause of the fault and which variables are the result, leading to the well-known smearing-out effect; see more in references [8,[66][67][68][69][70]. This is a limiting feature of traditional "unstructured" approaches that can be circumvented by incorporating more causal-oriented structure or a priori process knowledge in IPM methodologies.…”

Section: From Unstructured To Structured Process Monitoringmentioning

confidence: 99%

Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

Reis

Gins²

2017

Processes

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224

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We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its introduction almost 100 years ago. Several evolution trends that have been structuring IPM developments over this extended period of time are briefly referred, with more focus on data-driven approaches. We also argue that, besides such trends, the research focus has also evolved. The initial period was centred on optimizing IPM detection performance. More recently, root cause analysis and diagnosis gained importance and a variety of approaches were proposed to expand IPM with this new and important monitoring dimension. We believe that, in the future, the emphasis will be to bring yet another dimension to IPM: prognosis. Some perspectives are put forward in this regard, including the strong interplay of the Process and Maintenance departments, hitherto managed as separated silos.

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Analysis of smearing-out in contribution plot based fault isolation for Statistical Process Control

Cited by 87 publications

References 32 publications

An extensive reference dataset for fault detection and identification in batch processes

An extensive reference dataset for fault detection and identification in batch processes

Critical-to-Fault-Degradation Variable Analysis and Direction Extraction for Online Fault Prognostic

Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

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