4.5 Article

A modified Bayesian network to handle cyclic loops in root cause diagnosis of process faults in the chemical process industry

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Improving Bayesian inference efficiency for sensory anomaly detection and recovery in mobile robots

Manuel Castellano-Quero et al.

Summary: For mobile robots to operate effectively in real environments, a novel methodology based on Bayesian networks has been proposed to model complex relationships among sensors, detect anomalies, and enhance system robustness and performance.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Automation & Control Systems

OASIS-P: Operable Adaptive Sparse Identification of Systems for fault Prognosis of chemical processes

Bhavana Bhadriraju et al.

Summary: With the integration of OASIS, risk assessment, and contribution plots, the 'OASIS-P' framework can provide early fault prediction by adapting to initial fault symptoms and isolating faulty variables using contribution plots. This proactive monitoring approach is demonstrated in a case study of a reactor-separator system for fault prognosis.

JOURNAL OF PROCESS CONTROL (2021)

Article Engineering, Chemical

Development of parametric reduced-order model for consequence estimation of rare events

Pallavi Kumari et al.

Summary: Computational fluid dynamics (CFD) models are widely used in the chemical process industry to analyze high-consequence rare events. However, existing computationally efficient models are static and do not represent system evolution with time, posing a challenge for consequence modeling. To address this, a k-nearest neighbor (kNN) based parametric reduced-order model (PROM) is proposed to enhance numerical robustness with respect to parameter change.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2021)

Article Computer Science, Interdisciplinary Applications

Risk-based fault prediction of chemical processes using operable adaptive sparse identification of systems (OASIS)

Bhavana Bhadriraju et al.

Summary: Fault prediction is a monitoring strategy that predicts abnormal events based on current symptoms, data-driven modeling techniques are widely used but offline models have limitations in capturing dynamic process behavior. An adaptive modeling technique called OASIS is proposed to address this issue for risk assessment and fault prediction.

COMPUTERS & CHEMICAL ENGINEERING (2021)

Article Engineering, Chemical

A Novel Approach to Alarm Causality Analysis Using Active Dynamic Transfer Entropy

Yi Luo et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2020)

Article Engineering, Chemical

Root Cause Analysis of Key Process Variable Deviation for Rare Events in the Chemical Process Industry

Pallavi Kumari et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2020)

Article Engineering, Chemical

Novel Multimodule Bayesian Network with Cyclic Structures for Root Cause Analysis: Application to Complex Chemical Processes

Qun-Xiong Zhu et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2020)

Article Engineering, Chemical

Process Monitoring and Fault Diagnosis Based on a Regular Vine and Bayesian Network

Qiong Jia et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2020)

Article Computer Science, Artificial Intelligence

The Cubic Dynamic Uncertain Causality Graph: A Methodology for Temporal Process Modeling and Diagnostic Logic Inference

Chunling Dong et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Engineering, Chemical

Fault detection and pathway analysis using a dynamic Bayesian network

Md Tanjin Amin et al.

CHEMICAL ENGINEERING SCIENCE (2019)

Article Computer Science, Interdisciplinary Applications

A hierarchical approach for causal modeling of process systems

Resmi Suresh et al.

COMPUTERS & CHEMICAL ENGINEERING (2019)

Article Energy & Fuels

Understanding wellhead ignition as a blowout response

Prashanth Siddhamshetty et al.

Article Engineering, Chemical

Dynamic process fault detection and diagnosis based on a combined approach of hidden Markov and Bayesian network model

Mihiran Galagedarage Don et al.

CHEMICAL ENGINEERING SCIENCE (2019)

Article Engineering, Chemical

Application of safety triad in preparation for climate extremes affecting the process industries

Trent Parker et al.

PROCESS SAFETY PROGRESS (2019)

Article Engineering, Chemical

Process system fault detection and diagnosis using a hybrid technique

Md Tanjin Amin et al.

CHEMICAL ENGINEERING SCIENCE (2018)

Article Engineering, Chemical

Fault Detection and Diagnosis Based on Sparse PCA and Two-Level Contribution Plots

Lijia Luo et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2017)

Article Engineering, Chemical

Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network

H. Gharahbagheri et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2017)

Article Computer Science, Information Systems

A hidden Markov model with dependence jumps for predictive modeling of multidimensional time-series

Anastasios Petropoulos et al.

INFORMATION SCIENCES (2017)

Article Public, Environmental & Occupational Health

A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents

Hongyang Yu et al.

RISK ANALYSIS (2017)

Article Engineering, Chemical

SEMIPARAMETRIC PCA AND BETWEEN BAYESIAN NETWORK BASED PROCESS FAULT DIAGNOSIS TECHNIQUE

Yazhen Wang et al.

CANADIAN JOURNAL OF CHEMICAL ENGINEERING (2017)

Article Computer Science, Interdisciplinary Applications

Abnormal situation management: Challenges and opportunities in the big data era

Yidan Shu et al.

COMPUTERS & CHEMICAL ENGINEERING (2016)

Article Automation & Control Systems

An intelligent fault diagnosis system for process plant using a functional HAZOP and DBN integrated methodology

Jinqiu Hu et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2015)

Proceedings Paper Computer Science, Artificial Intelligence

A Review of Parameter Learning Methods in Bayesian Network

Zhiwei Ji et al.

ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, ICIC 2015, PT III (2015)

Article Automation & Control Systems

Fault detection and diagnosis for missing data systems with a three time-slice dynamic Bayesian network approach

Zhengdao Zhang et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2014)

Article Engineering, Chemical

A Multilogic Probabilistic Signed Directed Graph Fault Diagnosis Approach Based on Bayesian Inference

Di Peng et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2014)

Article Computer Science, Interdisciplinary Applications

Data-driven causal inference based on a modified transfer entropy

Yidan Shu et al.

COMPUTERS & CHEMICAL ENGINEERING (2013)

Article Automation & Control Systems

Direct Causality Detection via the Transfer Entropy Approach

Ping Duan et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2013)

Article Public, Environmental & Occupational Health

Coordinability and Consistency in Accident Causation and Prevention: Formal System Theoretic Concepts for Safety in Multilevel Systems

Raghvendra V. Cowlagi et al.

RISK ANALYSIS (2013)

Article Engineering, Environmental

Opportunistic predictive maintenance for complex multi-component systems based on DBN-HAZOP model

Jinqiu Hu et al.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2012)

Article Engineering, Chemical

Integrated Framework of Probabilistic Signed Digraph Based Fault Diagnosis Approach to a Gas Fractionation Unit

Ning Lue et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2011)

Article Engineering, Chemical

Dynamic risk assessment using failure assessment and Bayesian theory

Maryam Kalantarnia et al.

JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES (2009)

Article Automation & Control Systems

A practical method for identifying the propagation path of plant-wide disturbances

Margret Bauer et al.

JOURNAL OF PROCESS CONTROL (2008)

Article Automation & Control Systems

Finding the direction of disturbance propagation in a chemical process using transfer entropy

Margret Bauer et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2007)

Article Computer Science, Interdisciplinary Applications

Dynamic optimization of the Tennessee Eastman process using the OptControlCentre

T Jockenhövel et al.

COMPUTERS & CHEMICAL ENGINEERING (2003)

Review Computer Science, Interdisciplinary Applications

A review of process fault detection and diagnosis Part III: Process history based methods

V Venkatasubramanian et al.

COMPUTERS & CHEMICAL ENGINEERING (2003)

Review Computer Science, Interdisciplinary Applications

A review of process fault detection and diagnosis Part I: Quantitative model-based methods

V Venkatsubramanian et al.

COMPUTERS & CHEMICAL ENGINEERING (2003)

Article Computer Science, Interdisciplinary Applications

Dynamic probabilistic model-based expert system for fault diagnosis

D Leung et al.

COMPUTERS & CHEMICAL ENGINEERING (2000)

Article Physics, Multidisciplinary

Measuring information transfer

T Schreiber

PHYSICAL REVIEW LETTERS (2000)

Article Engineering, Industrial

Risk based maintenance optimization: foundational issues

S Apeland et al.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2000)