Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Nawaz, Muhammad; Maulud, Abdulhalim Shah; Zabiri, Haslinda; Taqvi, Syed Ali Ammar; Idris, Ahmad Kamal

doi:10.1016/j.cjche.2020.08.035

Cited by 38 publications

(23 citation statements)

References 54 publications

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“…The process monitoring based on the process specification can only detect the outliers marked by the red dashed line, but cannot detect the abnormal interval marked by the black dashed line. These abnormal intervals characterized by diverse scales and types 20) are hard to be recognized via traditional algorithms. At present, expert experience is relied on to identify abnormal intervals, which immensely restricts the inclusion analysis.…”

Section: Abnormal Interval Detection Based On Multi-scalementioning

confidence: 99%

Application of Time Series Data Anomaly Detection Based on Deep Learning in Continuous Casting Process

Zhou

et al. 2022

ISIJ Int.

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Section: Abnormal Interval Detection Based On Multi-scalementioning

confidence: 99%

Application of Time Series Data Anomaly Detection Based on Deep Learning in Continuous Casting Process

Zhou

et al. 2022

ISIJ Int.

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“…Advanced supervision, fault identification and fault diagnosis methods are becoming increasingly essential for many technological and industrial processes to ensure reliable and safe performance. Fault detection and diagnosis have been carried out for various chemical processes such as the Tennessee Eastman process (TEP) 16, 17, reactor system 18, 19, distillation column 20–27, bearing faults 28, crude and gas mixture pipelines 7, 29, industrial gas turbine 30, heating furnace 31, water‐cooled centrifugal chiller 32, biochemical wastewater treatment plant 33, controlled two‐tank system 34, and fluid catalytic cracking unit 35.…”

Section: Introductionmentioning

confidence: 99%

A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

et al. 2021

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Fault detection and diagnosis for process plants has been an active area of research for many years. This review presents a concise overview on supervised and unsupervised data‐driven approaches for fault detection and diagnosis in chemical processes. Methods based on supervised and unsupervised data‐driven techniques are reviewed, and the challenges in the field of fault detection and diagnosis have also been highlighted. It is observed that most of the data‐driven approaches are application specific, in that no single method can be used to obtain a generalized solution for nearly all purposes. The methods reviewed differ significantly from one to the other, and hence it is difficult to generalize any key similarity. The majority of the works proposed in the literature focused mainly on single fault detection, and do not cover the root‐cause diagnosis of the detected faults. In cases where both detection and diagnosis are performed, the focus is mainly for a single fault. In addition, majority of the articles do not extend to the diagnosis of the root cause for multiple and simultaneous faults.

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“…Recently, process data have been studied and applied for monitoring (Ge, 2017;Li et al, 2020a), control (Jose et al, 2019), optimization (Xie et al, 2021), prediction (Zhang et al, 2019c(Zhang et al, , 2020b, and so on. In the field of process monitoring, multivariate statistical analysis methods (MSAM), such as principal component analysis (PCA) and partial least squares (PLS) which do not require precise modeling of the process, have played an important role (Nawaz et al, 2021;Zhang et al, 2020c).…”

Section: Introductionmentioning

confidence: 99%

A multimode process monitoring strategy via improved variational inference Gaussian mixture model based on locality preserving projections

Guo

Liu

Shi

et al. 2021

Transactions of the Institute of Measurement and Control

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For multimode process monitoring, accurate mode information is difficult to be obtained, and each mode is monitored separately, which increases the complexity of the system. This paper proposes a multimode process monitoring strategy via improved variational inference Gaussian mixture model based on locality preserving projections (IVIGMM-LPP). First, the raw data are projected to the feature space where samples still maintain the original neighbor structure. Second, a new discriminant condition is introduced to reduce the influence of the initial category parameter on the iteration results in the VIGMM model. Then, the data are updated utilizing modal information, so that the scales of different modes are adjusted to the same level. Next, the deviation vector is introduced to eliminate the multi-center structure of data. Finally, the statistic is built to monitor the process. IVIGMM-LPP establishes one model for monitoring the premise of knowing the mode information, which reduces the complexity of the monitoring process and improves the fault detection rate. The experimental results of a numerical case and the Tennessee Eastman (TE) process verify the effectiveness of IVIGMM-LPP.

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Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Cited by 38 publications

References 54 publications

Application of Time Series Data Anomaly Detection Based on Deep Learning in Continuous Casting Process

Application of Time Series Data Anomaly Detection Based on Deep Learning in Continuous Casting Process

A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

A multimode process monitoring strategy via improved variational inference Gaussian mixture model based on locality preserving projections

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