ArticleComputers & chemical engineering2018
Big Data Approach to Batch Process Monitoring: Simultaneous Fault Detection and Diagnosis Using Nonlinear Support Vector Machine-based Feature Selection.
Article in Computers & chemical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem.Sensors (Basel, Switzerland) · 2021Article
- Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry.Sensors (Basel, Switzerland) · 2021Article
- A data-driven optimization algorithm for differential algebraic equations with numerical infeasibilities.AIChE journal. American Institute of Chemical Engineers · 2020Article
- Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms.PLoS computational biology · 2020Article
- Integrated Data-Driven Process Monitoring and Explicit Fault-Tolerant Multiparametric Control.Industrial & engineering chemistry research · 2020Article
- Permissible Area Analyses of Measurement Errors with Required Fault Diagnosability Performance.Sensors (Basel, Switzerland) · 2019Article
- A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process.AIChE journal. American Institute of Chemical Engineers · 2019Article
- Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and visualization.PloS one · 2019Article
- Development of the Texas A&M Superfund Research Program Computational Platform for Data Integration, Visualization, and Analysis.ESCAPE. European Symposium on Computer Aided Process Engineering · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark dataset which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the prealigned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.