A review of data-driven fault detection and diagnosis methods: applications in chemical process systems

2020 ◽  
Vol 36 (4) ◽  
pp. 513-553 ◽  
Author(s):  
Norazwan Md Nor ◽  
Che Rosmani Che Hassan ◽  
Mohd Azlan Hussain
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Chemical Process ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Learning Approaches ◽  
Multivariate Statistical ◽  
Process Systems ◽  
Detection And Diagnosis ◽  
Chemical Process Systems

AbstractFault detection and diagnosis (FDD) systems are developed to characterize normal variations and detect abnormal changes in a process plant. It is always important for early detection and diagnosis, especially in chemical process systems to prevent process disruptions, shutdowns, or even process failures. However, there have been only limited reviews of data-driven FDD methods published in the literature. Therefore, the aim of this review is to provide the state-of-the-art reference for chemical engineers and to promote the application of data-driven FDD methods in chemical process systems. In general, there are two different groups of data-driven FDD methods: the multivariate statistical analysis and the machine learning approaches, which are widely accepted and applied in various industrial processes, including chemicals, pharmaceuticals, and polymers. Many different multivariate statistical analysis methods have been proposed in the literature, such as principal component analysis, partial least squares, independent component analysis, and Fisher discriminant analysis, while the machine learning approaches include artificial neural networks, neuro-fuzzy methods, support vector machine, Gaussian mixture model, K-nearest neighbor, and Bayesian network. In the first part, this review intends to provide a comprehensive literature review on applications of data-driven methods in FDD systems for chemical process systems. In addition, the hybrid FDD frameworks have also been reviewed by discussing the distinct advantages and various constraints, with some applications as examples. However, the choice for the data-driven FDD methods is not a straightforward issue. Thus, in the second part, this paper provides a guideline for selecting the best possible data-driven method for FDD systems based on their faults. Finally, future directions of data-driven FDD methods are summarized with the intent to expand the use for the process monitoring community.

Uncertainty in chemical process systems engineering: a critical review

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Seyedmehdi Sharifian ◽  
Rahmat Sotudeh-Gharebagh ◽  
Reza Zarghami ◽  
Philippe Tanguy ◽  
Navid Mostoufi
Keyword(s):  
Systems Engineering ◽  
Chemical Process ◽  
Single Step ◽  
Fault Detection And Diagnosis ◽  
Future Research ◽  
Accurate Analysis ◽  
Process Systems ◽  
Analysis And Evaluation ◽  
Optimum Planning ◽  
Detection And Diagnosis

Abstract Uncertainty or error occurs as a result of a lack or misuse of knowledge about specific topics or situations. In this review, we recall the differences between error and uncertainty briefly, first, and then their probable sources. Then, their identifications and management in chemical process design, optimization, control, and fault detection and diagnosis are illustrated. Furthermore, because of the large amount of information that can be obtained in modern plants, accurate analysis and evaluation of those pieces of information have undeniable effects on the uncertainty in the system. Moreover, the origins of uncertainty and error in simulation and modeling are also presented. We show that in a multidisciplinary modeling approach, every single step can be a potential source of uncertainty, which can merge into each other and generate unreliable results. In addition, some uncertainty analysis and evaluation methods are briefly presented. Finally, guidelines for future research are proposed based on existing research gaps, which we believe will pave the way to innovative process designs based on more reliable, efficient, and feasible optimum planning.

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

2021 ◽  
Author(s):  
Syed Ali Ammar Taqvi ◽  
Haslinda Zabiri ◽  
Lemma Dendena Tufa ◽  
Fahim Uddin ◽  
Syeda Anmol Fatima ◽  
...  
Keyword(s):  
Fault Detection ◽  
Chemical Processes ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Learning Approaches ◽  
Detection And Diagnosis

scholarly journals Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

2021 ◽  
Vol 9 ◽  
Author(s):  
Guang Hu ◽  
Taotao Zhou ◽  
Qianfeng Liu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
State Of The Art ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Detection And Diagnosis

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.

scholarly journals Data-Driven Fault Diagnosis for Electric Drives: A Review

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4024
Author(s):  
David Gonzalez-Jimenez ◽  
Jon del-Olmo ◽  
Javier Poza ◽  
Fernando Garramiola ◽  
Patxi Madina
Keyword(s):  
Machine Learning ◽  
Industrial Sector ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Main Research ◽  
Electric Drives ◽  
Learning Techniques ◽  
Detection And Diagnosis ◽  
Supervision Strategies ◽  
Operational Data

The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.

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