An analysis of process fault diagnosis methods from safety perspectives
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Faisal Khan | Syed Imtiaz | Salim Ahmed | Rajeevan Arunthavanathan | S. Imtiaz | Salim Ahmed | F. Khan | Rajeevan Arunthavanathan
[1] Ming Chen,et al. A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network , 2020 .
[2] Valerio Cozzani,et al. Towards dynamic risk analysis: A review of the risk assessment approach and its limitations in the chemical process industry , 2016 .
[3] Chung C. Chang,et al. On-line fault diagnosis using the signed directed graph , 1990 .
[4] Raghunathan Rengaswamy,et al. A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis , 2007 .
[5] Paul M. Frank,et al. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..
[6] Richard D. Braatz,et al. Knowledge-based Methods , 2001 .
[7] Steven X. Ding,et al. Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .
[8] Gautam Biswas,et al. Bayesian Fault Detection and Diagnosis in Dynamic Systems , 2000, AAAI/IAAI.
[9] Brian J. Tyler,et al. HAZOP study training from the 1970s to today , 2012 .
[10] Raghunathan Rengaswamy,et al. A framework for on-line trend extraction and fault diagnosis , 2010, Eng. Appl. Artif. Intell..
[11] Linxuan Zhang,et al. Attention Based Echo State Network: A Novel Approach for Fault Prognosis , 2019, ICMLC '19.
[12] Jamil Ahmad,et al. A Fuzzy AHP-TOPSIS Framework for the Risk Assessment of Green Supply Chain Implementation in the Textile Industry , 2015 .
[13] Nicola Paltrinieri,et al. Learning about risk: Machine learning for risk assessment , 2019, Safety Science.
[14] Rajagopalan Srinivasan,et al. Quantifying the effectiveness of an alarm management system through human factors studies , 2014, Comput. Chem. Eng..
[15] Ivo Punčochář,et al. A Survey of Active Fault Diagnosis Methods , 2018 .
[16] Erik Hollnagel,et al. Cognitive reliability and error analysis method : CREAM , 1998 .
[17] Zhengdao Zhang,et al. Fault detection and diagnosis for missing data systems with a three time-slice dynamic Bayesian network approach , 2014 .
[18] Stamatis Voliotis,et al. Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects , 2019, Sensors.
[19] Ping Zhang,et al. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .
[20] Fausto Pedro García Márquez,et al. False Alarms Management by Data Science , 2019, Data Science and Digital Business.
[21] Torsten Jeinsch,et al. A characterization of parity space and its application to robust fault detection , 1999, IEEE Trans. Autom. Control..
[22] Xianfeng Tang,et al. Layer-constrained variational autoencoding kernel density estimation model for anomaly detection , 2020, Knowl. Based Syst..
[23] B. R. Mehta,et al. Alarm management systems , 2015 .
[24] Gary J. Powers,et al. On-line hazard aversion and fault diagnosis in chemical processes: the digraph+fault-tree method , 1988 .
[25] Faisal Khan,et al. A conceptual offshore oil and gas process accident model , 2010 .
[26] P. K. Marhavilas,et al. Risk Estimation in the Greek Constructions' Worksites by using a Quantitative Assessment Technique and Statistical Information of Occupational Accidents , 2009 .
[27] Antoine Rauzy,et al. New insight into the average probability of failure on demand and the probability of dangerous failure per hour of safety instrumented systems , 2010 .
[28] Janos Gertler,et al. Fault detection and diagnosis in engineering systems , 1998 .
[29] Yuanqing Xia,et al. A deep Boltzmann machine and multi-grained scanning forest ensemble collaborative method and its application to industrial fault diagnosis , 2018, Comput. Ind..
[30] Igor Svrkota,et al. Risk assessment model of mining equipment failure based on fuzzy logic , 2014, Expert Syst. Appl..
[31] Rolf Isermann,et al. Fault diagnosis of machines via parameter estimation and knowledge processing - Tutorial paper , 1991, Autom..
[32] Youmin Zhang,et al. Experimental Test of a Two-Stage Kalman Filter for Actuator Fault Detection and Diagnosis of an Unmanned Quadrotor Helicopter , 2013, J. Intell. Robotic Syst..
[33] Sohrab Zendehboudi,et al. Dynamic risk assessment of reservoir production using data-driven probabilistic approach , 2020 .
[34] Hangseok Choi,et al. Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels , 2015 .
[35] Kay Chen Tan,et al. Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[36] Liang Guo,et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.
[37] Nancy G. Leveson,et al. Applying systems thinking to analyze and learn from events , 2010 .
[38] Steven X. Ding,et al. A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.
[39] Raman K. Mehra,et al. Failure detection and identification and fault tolerant control using the IMM-KF with applications to the Eagle-Eye UAV , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).
[40] Sohag Kabir,et al. Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review , 2019, Safety Science.
[41] E. Hollnagel. FRAM: The Functional Resonance Analysis Method: Modelling Complex Socio-technical Systems , 2012 .
[42] PooGyeon Park,et al. Unsupervised Anomaly detection of LM Guide Using Variational Autoencoder , 2019, 2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE).
[43] Moncef Gabbouj,et al. Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.
[44] Marvin Rausand,et al. Reliability analysis of safety-instrumented systems operated in high-demand mode , 2014 .
[45] Li Lin,et al. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks , 2018, Neurocomputing.
[46] Mohammad Zaid Kamil,et al. Dynamic domino effect risk assessment using Petri-nets , 2019 .
[47] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.
[48] G. Heredia,et al. Sensor fault detection in small autonomous helicopters using observer/Kalman filter identification , 2009, 2009 IEEE International Conference on Mechatronics.
[49] Elena M. Cimpoesu,et al. FAULT DETECTION AND IDENTIFICATION USING PARAMETER ESTIMATION TECHNIQUES , 2014 .
[50] Jay H. Lee,et al. Fault detection and classification using artificial neural networks , 2018 .
[51] Rainer Oehler,et al. Online Model-Based Fault Detection and Diagnosis for a Smart Aircraft Actuator , 1997 .
[52] Chunhui Zhao,et al. Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability , 2020, IEEE Transactions on Industrial Electronics.
[53] Grzegorz Kaczor,et al. Verification of safety integrity level with the application of Monte Carlo simulation and reliability block diagrams , 2016 .
[54] Venkat Venkatasubramanian,et al. Process Fault Detection and Diagnosis: Past, Present and Future , 2001 .
[55] Morten Lind,et al. Making sense of the abstraction hierarchy in the power plant domain , 2003, Cognition, Technology & Work.
[56] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[57] Hee-Jun Kang,et al. A survey on Deep Learning based bearing fault diagnosis , 2019, Neurocomputing.
[58] Mohd Salman Leong,et al. Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review , 2019, IEEE Access.
[59] Iman Izadi,et al. Pattern matching of alarm flood sequences by a modified Smith–Waterman algorithm , 2013 .
[60] Takehisa Yairi,et al. A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.
[61] Ying Sun,et al. An unsupervised monitoring procedure for detecting anomalies in photovoltaic systems using a one-class Support Vector Machine , 2019, Solar Energy.
[62] Faisal Khan,et al. Real-time fault diagnosis using knowledge-based expert system , 2008 .
[63] Md. Tanjin Amin,et al. A bibliometric review of process safety and risk analysis , 2019, Process Safety and Environmental Protection.
[64] Marvin Rausand,et al. Reliability of safety instrumented systems: Where to direct future research? , 2010 .
[65] Sanjay Jayaram. A new fast converging Kalman filter for sensor fault detection and isolation , 2010 .
[66] Nina F. Thornhill,et al. Advances in alarm data analysis with a practical application to online alarm flood classification , 2019 .
[67] Venkat Venkatasubramanian,et al. A hybrid framework for large scale process fault diagnosis , 1997 .
[68] Walmir M. Caminhas,et al. Adaptive fault detection and diagnosis using an evolving fuzzy classifier , 2013, Inf. Sci..
[69] Xin Ma,et al. Application of Signed Directed Graph Based Fault Diagnosis of Atmospheric Distillation Unit , 2010, 2010 2nd International Workshop on Intelligent Systems and Applications.
[70] Kim J. Vicente,et al. Making the abstraction hierarchy concrete , 1994, Int. J. Hum. Comput. Stud..
[71] Patrick Haffner,et al. Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.
[72] Sachin S. Kamble,et al. Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives , 2018, Process Safety and Environmental Protection.
[73] Mohammad Esmalifalak,et al. Fault Detection in Wind Turbine: A Systematic Literature Review , 2013 .
[74] Feihu Qi,et al. A Comparison of Model Selection Methods for Multi-class Support Vector Machines , 2005, ICCSA.
[75] Shen Yin,et al. Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System , 2015, IEEE Transactions on Industrial Electronics.
[76] Zifeng Wang,et al. Data-driven risk assessment on urban pipeline network based on a cluster model , 2020, Reliab. Eng. Syst. Saf..
[77] Bernd Scholz-Reiter,et al. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection , 2016 .
[78] Theodora Kourti,et al. Statistical Process Control of Multivariate Processes , 1994 .
[79] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[80] Yacov Y. Haimes,et al. Risk modeling, assessment, and management , 1998 .
[81] Donald L. Simon,et al. Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics , 2005 .
[82] Efstratios N. Pistikopoulos,et al. A data-driven alarm and event management framework , 2019, Journal of Loss Prevention in the Process Industries.
[83] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[84] Xiang Li,et al. Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..
[85] Venkat Venkatasubramanian,et al. A neural network methodology for process fault diagnosis , 1989 .
[86] Yu Zheng,et al. An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation , 2020, Comput. Ind..
[87] Rob Alexander,et al. Supporting systems of systems hazard analysis using multi-agent simulation , 2013 .
[88] Faisal Khan,et al. Dynamic process fault detection and diagnosis based on a combined approach of hidden Markov and Bayesian network model , 2019, Chemical Engineering Science.
[89] S. R. Trammell,et al. Using a modified Hazop/FMEA methodology for assessing system risk , 2001, Proceedings 2nd International Workshop on Engineering Management for Applied Technology. EMAT 2001.
[90] Mohammad Modarres,et al. A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics , 2019, Sensors.
[91] Rolf Isermann,et al. Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .
[92] Ludovic Tanguy,et al. Natural language processing for aviation safety reports: From classification to interactive analysis , 2016, Comput. Ind..
[93] Youmin Zhang,et al. Bibliographical review on reconfigurable fault-tolerant control systems , 2003, Annu. Rev. Control..
[94] Hans J. Pasman,et al. Process hazard analysis, hazard identification and scenario definition: Are the conventional tools sufficient, or should and can we do much better? , 2017 .
[95] Theodora Kourti,et al. Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .
[96] Yuping Zhang,et al. Fault detection and diagnosis using Bayesian-network inference , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.
[97] Jing Huang,et al. Unsupervised learning for fault detection and diagnosis of air handling units , 2020, Energy and Buildings.
[98] Shuyuan Zhang,et al. Bidirectional Recurrent Neural Network-Based Chemical Process Fault Diagnosis , 2019, Industrial & Engineering Chemistry Research.
[99] Manabu Kano,et al. Monitoring independent components for fault detection , 2003 .
[100] Børge Rokseth,et al. Applications of machine learning methods for engineering risk assessment – A review , 2020, Safety Science.
[101] H. Karimi,et al. Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process , 2014 .
[102] Tarun Gupta,et al. A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance , 2018, 2018 5th International Conference on Industrial Engineering and Applications (ICIEA).
[103] Raghunathan Rengaswamy,et al. A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..
[104] Olivier Adrot,et al. Review of Machine Learning Approaches In Fault Diagnosis applied to IoT Systems , 2019, 2019 International Conference on Control, Automation and Diagnosis (ICCAD).
[105] Bill Hollifield,et al. The alarm management handbook : a comprehensive guide , 2010 .
[106] Gong Ping,et al. An End-to-End model based on CNN-LSTM for Industrial Fault Diagnosis and Prognosis , 2018, 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC).
[107] Kamarizan Kidam,et al. Analysis of equipment failures as contributors to chemical process accidents , 2013 .
[108] Erik Hollnagel,et al. Understanding accidents-from root causes to performance variability , 2002, Proceedings of the IEEE 7th Conference on Human Factors and Power Plants.
[109] Jun Peng,et al. A Remaining Useful Life Prediction Method with Automatic Feature Extraction for Aircraft Engines , 2019, 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).
[110] Marvin Rausand,et al. Reliability assessment of safety instrumented systems subject to different demand modes , 2011 .
[111] John D. Lee,et al. Improving process safety: What roles for Digitalization and Industry 4.0? , 2019 .
[112] P. K. Marhavilas,et al. Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000–2009 , 2011 .
[113] Ausif Mahmood,et al. Review of Deep Learning Algorithms and Architectures , 2019, IEEE Access.
[114] Tongwen Chen,et al. Constructing workflow models of alarm responses via trace labeling and dependency analysis , 2019, 2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).
[115] Hector Budman,et al. Fault detection, identification and diagnosis using CUSUM based PCA , 2011 .
[116] Lawrence Beckman. Easily Assess Complex Safety Loops , 2001 .
[117] Hu-Chen Liu,et al. A New Model for Failure Mode and Effects Analysis Based on k-Means Clustering Within Hesitant Linguistic Environment , 2022, IEEE Transactions on Engineering Management.
[118] Zhiqiang Ge,et al. Review on data-driven modeling and monitoring for plant-wide industrial processes , 2017 .
[119] Paul Amyotte,et al. Bayesian Stochastic Petri Nets (BSPN) - A new modelling tool for dynamic safety and reliability analysis , 2020, Reliab. Eng. Syst. Saf..
[120] Liana M. Kiff,et al. Common Procedural Execution Failure Modes during Abnormal Situations , 2011 .
[121] Paul M. Frank,et al. Analytical and Qualitative Model-based Fault Diagnosis - A Survey and Some New Results , 1996, Eur. J. Control.
[122] Steven X. Ding,et al. Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems , 2014 .
[123] Ahmad B. Rad,et al. Fuzzy-genetic algorithm for automatic fault detection in HVAC systems , 2007, Appl. Soft Comput..
[124] Yaguo Lei,et al. Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .
[125] Alexandros Mouzakitis,et al. Classification of Fault Diagnosis Methods for Control Systems , 2013 .
[126] J. S. Alford,et al. Alarm management for regulated industries , 2005 .
[127] Peter Bullemer,et al. Common operations failure modes in the process industries , 2010 .
[128] Erik Hollnagel,et al. The functional resonance accident model , 2004 .
[129] David John,et al. Development of risk‐based process safety indicators , 2009 .
[130] R. Li,et al. Dynamic fault detection and diagnosis using neural networks , 1990, Proceedings. 5th IEEE International Symposium on Intelligent Control 1990.
[131] Hong Xu,et al. Combining dynamic fault trees and event trees for probabilistic risk assessment , 2004, Annual Symposium Reliability and Maintainability, 2004 - RAMS.
[132] Faisal Khan,et al. Abnormal situation management for smart chemical process operation , 2016 .
[133] Joseph R. Belland,et al. Using fault trees to analyze safety-instrumented systems , 2016, 2016 Annual Reliability and Maintainability Symposium (RAMS).
[134] Mohd Azlan Hussain,et al. A review of data-driven fault detection and diagnosis methods: applications in chemical process systems , 2019 .
[135] Vasilis Fthenakis,et al. Hazard and operability (HAZOP) analysis. A literature review. , 2010, Journal of hazardous materials.
[136] Douglas H Rothenberg,et al. Alarm Management for Process Control: A Best-Practice Guide for Design, Implementation, and Use of Industrial Alarm Systems , 2009 .
[137] Mashrur Chowdhury,et al. Fault-Tree Model for Risk Assessment of Bridge Failure: Case Study for Segmental Box Girder Bridges , 2013 .
[138] Abdulhamit Subasi,et al. Traffic accident detection using random forest classifier , 2018, 2018 15th Learning and Technology Conference (L&T).
[139] Nima Khakzad,et al. Dynamic safety assessment of natural gas stations using Bayesian network. , 2017, Journal of hazardous materials.
[140] Kok Kiong Tan,et al. Fault Diagnosis and Fault-Tolerant Control in Linear Drives Using the Kalman Filter , 2012, IEEE Transactions on Industrial Electronics.
[141] Yonghui Song,et al. Risk assessment methodology for Shenyang Chemical Industrial Park based on fuzzy comprehensive evaluation , 2015, Environmental Earth Sciences.
[142] Raghunathan Rengaswamy,et al. A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..
[143] Syed Imtiaz,et al. Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique , 2020, Comput. Chem. Eng..
[144] Jinsong Zhao,et al. Fault detection and diagnosis based on transfer learning for multimode chemical processes , 2020, Comput. Chem. Eng..
[145] Ruokang Li,et al. Fault detection and diagnosis in a closed-loop nonlinear distillation process: application of extended Kalman filters , 1991 .
[146] Josiah C. Hoskins,et al. Artificial neural network models for knowledge representation in chemical engineering , 1990 .
[147] A. C. Cilliers,et al. Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant , 2018, Annals of Nuclear Energy.
[148] Abhinav Gupta,et al. Probabilistic risk assessment based model validation method using Bayesian network , 2018, Reliab. Eng. Syst. Saf..
[149] Salah Bouhouche,et al. Fault detection and diagnosis using principal component analysis. Application to low pressure lost foam casting process , 2011, Int. J. Model. Identif. Control..
[150] Xianhui Yang,et al. A simple reliability block diagram method for safety integrity verification , 2007, Reliab. Eng. Syst. Saf..
[151] Enrico Zio,et al. Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture , 2019, IEEE Transactions on Industrial Electronics.
[152] Antoine Grall,et al. Combination of safety integrity levels (SILs): A study of IEC61508 merging rules , 2008 .
[153] Hongfu Zuo,et al. Hazard identification and prediction system for aircraft electrical system based on SRA and SVM , 2020 .
[154] Jun Wu,et al. Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks , 2019, IEEE Transactions on Industrial Informatics.
[155] Stephen Butt,et al. Safety and risk analysis of managed pressure drilling operation using Bayesian network , 2015 .
[156] Le Li,et al. Remaining useful life prediction via a variational autoencoder and a time‐window‐based sequence neural network , 2020, Qual. Reliab. Eng. Int..
[157] Brian Litt,et al. Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets , 2010, 2010 Ninth International Conference on Machine Learning and Applications.
[158] Gilles Dusserre,et al. Review of 62 risk analysis methodologies of industrial plants , 2002 .
[159] Pan Yue,et al. A hybrid fuzzy evaluation method for curtain grouting efficiency assessment based on an AHP method extended by D numbers , 2016, Expert Syst. Appl..
[160] Jens Rasmussen,et al. Risk management in a dynamic society: a modelling problem , 1997 .
[161] Zhiwei Gao,et al. From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis , 2013, IEEE Transactions on Industrial Informatics.
[162] Heikki N. Koivo,et al. Application of artificial neural networks in process fault diagnosis , 1991, Autom..
[163] Brijesh Verma,et al. Impact of Automatic Feature Extraction in Deep Learning Architecture , 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).
[164] B. Shibata,et al. Fault diagnosis of chemical processes utilizing signed directed graphs-improvement by using temporal information , 1989 .
[165] Faisal Khan,et al. Methods and models in process safety and risk management: Past, present and future , 2015 .
[166] Seungchul Lee,et al. Fault detection and identification method using observer-based residuals , 2018, Reliab. Eng. Syst. Saf..
[167] Qin Zhang,et al. On intelligent risk analysis and critical decision of underwater robotic vehicle , 2017 .
[168] Aniruddha Datta,et al. Industrial alarm systems: Challenges and opportunities , 2017 .
[169] Abouzar Yousefi,et al. Systemic accident analysis models: A comparison study between AcciMap, FRAM, and STAMP , 2018, Process Safety Progress.
[170] Faisal Khan,et al. Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network , 2013 .
[171] Rolf Isermann,et al. Fault-Diagnosis Applications , 2011 .
[172] Serkan Balli,et al. Operating System Selection Using Fuzzy AHP and TOPSIS Methods , 2009 .
[173] Nancy G. Leveson,et al. A new accident model for engineering safer systems , 2004 .
[174] Oliver Niggemann,et al. Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis , 2017, ML4CPS.
[175] Faisal Khan,et al. Risk Assessment and Safety Evaluation Using Probabilistic Fault Tree Analysis , 2001 .
[176] Hui Li,et al. Assessing Risk in Chinese Shale Gas Investments Abroad: Modelling and Policy Recommendations , 2016 .
[177] F. Gu,et al. Fault detection and diagnosis using Principal Component Analysis of vibration data from a reciprocating compressor , 2012, Proceedings of 2012 UKACC International Conference on Control.
[178] Guohua Wu,et al. Diagnosis of operational failures and on-demand failures in nuclear power plants: An approach based on dynamic Bayesian networks , 2020 .
[179] C. T. Seppala,et al. A review of performance monitoring and assessment techniques for univariate and multivariate control systems , 1999 .
[180] Liang Tang,et al. Research on Prognosis for Engines by LSTM Deep Learning Method , 2019, 2019 Prognostics and System Health Management Conference (PHM-Qingdao).
[181] Genserik Reniers,et al. Process safety indicators, a review of literature , 2016 .
[182] Zengtao Hou,et al. An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network , 2019, Sensors.
[183] Lisa M. Bendixen,et al. Chemical plant risk assessment using HAZOP and fault tree methods , 1984 .
[184] Faisal Khan,et al. SHIPP methodology: Predictive accident modeling approach. Part II. Validation with case study , 2011 .
[185] Sirish L. Shah,et al. Fault detection and diagnosis in process data using one-class support vector machines , 2009 .
[186] Youmin Zhang,et al. A fault detection and diagnosis approach based on hidden Markov chain model , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).
[187] Valerio Cozzani,et al. Assessment of domino effect: State of the art and research Needs , 2015, Reliab. Eng. Syst. Saf..
[188] Hong Wang,et al. An adaptive observer-based fault detection and diagnosis for nonlinear systems with sensor and actuator faults , 2015, 2015 International Conference on Advanced Mechatronic Systems (ICAMechS).
[189] Liang Guo,et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.
[190] Dan Stefanoiu,et al. Fault Detection and Diagnosis Using Parameter Estimation with Recursive Least Squares , 2013, 2013 19th International Conference on Control Systems and Computer Science.
[191] Iman Izadi,et al. Pattern mining in alarm flood sequences using a modified PrefixSpan algorithm. , 2019, ISA transactions.
[192] Piergiuseppe Di Marco,et al. Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network , 2019, Sensors.
[193] Sirish L. Shah,et al. An Overview of Industrial Alarm Systems: Main Causes for Alarm Overloading, Research Status, and Open Problems , 2016, IEEE Transactions on Automation Science and Engineering.
[194] Weihua Gui,et al. A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network. , 2019, ISA transactions.
[195] Bo-Suk Yang,et al. Support vector machine in machine condition monitoring and fault diagnosis , 2007 .
[196] Zhiyu Zhu,et al. A novel deep learning method based on attention mechanism for bearing remaining useful life prediction , 2020, Appl. Soft Comput..
[197] Yoshinobu Sato,et al. Estimation of average hazardous-event-frequency for allocation of safety-integrity levels , 1999 .
[198] Henk A. P. Blom,et al. Safety Risk Assessment by Monte Carlo Simulation of Complex Safety Critical Operations , 2006, SSS.
[199] Enrico Zio,et al. Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.
[200] Chih-Min Fan,et al. A Bayesian framework to integrate knowledge-based and data-driven inference tools for reliable yield diagnoses , 2008, 2008 Winter Simulation Conference.
[201] Janos Gertler,et al. Diagnosing parametric faults: from parameter estimation to parity relations , 1995, Proceedings of 1995 American Control Conference - ACC'95.
[202] Qingtian Wu,et al. Unsupervised Learning Based On Artificial Neural Network: A Review , 2018, 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS).
[203] Raghunathan Rengaswamy,et al. A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..
[204] Jinqiu Hu,et al. A two-level intelligent alarm management framework for process safety , 2016 .
[205] Jie Yu,et al. A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis , 2012 .
[206] Steven X. Ding,et al. A survey on model-based fault diagnosis for linear discrete time-varying systems , 2018, Neurocomputing.
[207] Fadwa T. Eljack,et al. Process safety and abnormal situation management , 2016 .
[208] Gary J. Powers,et al. Computer-aided Synthesis of Fault-trees , 1977, IEEE Transactions on Reliability.
[209] Jie Yu,et al. A support vector clustering‐based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data , 2013 .
[210] Faisal Khan,et al. Techniques and methodologies for risk analysis in chemical process industries , 1998 .
[211] Liang Gao,et al. A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.
[212] Lei Ren,et al. Prediction of Bearing Remaining Useful Life With Deep Convolution Neural Network , 2018, IEEE Access.
[213] Laibin Zhang,et al. Alarm Management Technology and Its Progress in Process Industries , 2017 .
[214] Zhanpeng Zhang,et al. A deep belief network based fault diagnosis model for complex chemical processes , 2017, Comput. Chem. Eng..
[215] Faisal Khan,et al. Accident modelling and analysis in process industries , 2014 .
[216] Jizhong Tao,et al. A Novel Bearing Health Indicator Construction Method Based on Ensemble Stacked Autoencoder , 2019, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM).
[217] MengChu Zhou,et al. Zonotoptic Fault Estimation for Discrete-Time LPV Systems With Bounded Parametric Uncertainty , 2020, IEEE Transactions on Intelligent Transportation Systems.
[218] Guoming Chen,et al. Dynamic quantitative risk assessment of accidents induced by leakage on offshore platforms using DEMATEL-BN , 2019, International Journal of Naval Architecture and Ocean Engineering.
[219] Sirish L. Shah,et al. Detection of Frequent Alarm Patterns in Industrial Alarm Floods Using Itemset Mining Methods , 2018, IEEE Transactions on Industrial Electronics.
[220] Randy C. Paffenroth,et al. Anomaly Detection with Robust Deep Autoencoders , 2017, KDD.
[221] Jun Shang,et al. Early Classification of Alarm Floods via Exponentially Attenuated Component Analysis , 2020, IEEE Transactions on Industrial Electronics.
[222] Yang Liu,et al. Least-Squares Fault Detection and Diagnosis for Networked Sensing Systems Using A Direct State Estimation Approach , 2013, IEEE Transactions on Industrial Informatics.
[223] Kjell G. Robbersmyr,et al. Early detection and classification of bearing faults using support vector machine algorithm , 2017, 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD).
[224] R. Macchiaroli,et al. Appraisal of a New Safety Assessment Method using the Petri Nets for the Machines Safety , 2018 .
[225] Shahid Abbas Abbasi,et al. Major accidents in process industries and an analysis of causes and consequences , 1999 .
[226] Zhu Mao,et al. Deep Recurrent Convolutional Neural Network for Remaining Useful Life Prediction , 2019, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM).
[227] Carine Jauberthie,et al. Fault detection and identification via bounded-error parameter estimation using distribution theory , 2019, 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT).
[228] Ronald J. Willey. Layer of Protection Analysis , 2014 .
[229] Jie Chen,et al. Observer-based fault detection and isolation: robustness and applications , 1997 .
[230] Tore Hägglund,et al. Causal analysis for alarm flood reduction , 2016 .
[231] Jin Wang,et al. Application of a generic bow-tie based risk analysis framework on risk management of sea ports and offshore terminals. , 2011, Journal of hazardous materials.
[232] N.F.F. Ebecken,et al. Fault-tree analysis: a knowledge-engineering approach , 1995 .
[233] Hui Zhang,et al. Novel naïve Bayes classification models for predicting the chemical Ames mutagenicity. , 2017, Toxicology in vitro : an international journal published in association with BIBRA.
[234] J.V. Bukowski,et al. Incorporating process demand into models for assessment of safety system performance , 2006, RAMS '06. Annual Reliability and Maintainability Symposium, 2006..
[235] K. Salahshoor,et al. Process Fault Detection and Diagnosis by Synchronous and Asynchronous Decentralized Kalman Filtering using State-Vector Fusion Technique , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.
[236] Ümit V. Çatalyürek,et al. Distributed dynamic event tree generation for reliability and risk assessment , 2006, 2006 IEEE Challenges of Large Applications in Distributed Environments.
[237] Hazem Nounou,et al. Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems , 2020 .
[238] Faisal Khan,et al. SHIPP methodology: Predictive accident modeling approach. Part I: Methodology and model description , 2011 .
[239] Minyar Sassi Hidri,et al. Discovery of Frequent Patterns of Episodes Within a Time Window for Alarm Management Systems , 2020, IEEE Access.
[240] Raghunathan Rengaswamy,et al. Fault Diagnosis by Qualitative Trend Analysis of the Principal Components: Prospects and Some New Results , 2003 .
[241] Jun g Sik Kong,et al. Quantitative risk evaluation based on event tree analysis technique: Application to the design of shield TBM , 2009 .
[242] Abdessamad Kobi,et al. Fault Detection and Diagnosis in a Bayesian Network classifier incorporating probabilistic boundary1 , 2015 .
[243] Jing Li,et al. Risk assessment and validation of flood disaster based on fuzzy mathematics , 2009 .
[244] Marcantonio Catelani,et al. Sensitivity analysis with MC simulation for the failure rate evaluation and reliability assessment , 2015 .
[245] Si-Zhao Joe Qin,et al. Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..
[246] Zhiwu Huang,et al. Remaining Useful Life Estimation Using CNN-XGB With Extended Time Window , 2019, IEEE Access.
[247] H. Odendaal,et al. Actuator fault detection and isolation: An optimised parity space approach , 2014 .
[248] Lei Wang,et al. Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network , 2020, J. Comput. Inf. Sci. Eng..
[249] Xin Xu,et al. Dilated Convolution Neural Network for Remaining Useful Life Prediction , 2020, J. Comput. Inf. Sci. Eng..
[250] P Hajdú,et al. [Analytical methods I]. , 1975, Arzneimittel-Forschung.
[251] Faisal Khan,et al. A Bibliometric Review and Analysis of Data-Driven Fault Detection and Diagnosis Methods for Process Systems , 2018, Industrial & Engineering Chemistry Research.
[252] Leila Parsa,et al. Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors , 2011, IEEE Transactions on Industrial Electronics.
[253] Magnus Löfstrand,et al. Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application , 2014, Comput. Ind..
[254] Keem Siah Yap,et al. Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection , 2013, 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA).
[255] G. Fu,et al. The development history of accident causation models in the past 100 years: 24Model, a more modern accident causation model , 2020 .
[256] Mudassir M. Rashid,et al. Hidden Markov Model Based Adaptive Independent Component Analysis Approach for Complex Chemical Process Monitoring and Fault Detection , 2012 .
[257] Marvin Rausand,et al. Reliability effects of test strategies on safety-instrumented systems in different demand modes , 2013, Reliab. Eng. Syst. Saf..
[258] Henk A. P. Blom,et al. Systemic accident risk assessment in air traffic by Monte Carlo simulation , 2009 .
[259] J. E. Cockshott. Probability Bow-Ties: A Transparent Risk Management Tool , 2005 .
[260] Hao Ye,et al. A new parity space approach for fault detection based on stationary wavelet transform , 2004, IEEE Transactions on Automatic Control.
[261] Xin Wu,et al. Leakage detection for hydraulic IGV system in gas turbine compressor with recursive ridge regression estimation , 2017 .
[262] Wan Chul Yoon,et al. The effects of presenting functionally abstracted information in fault diagnosis tasks , 2001, Reliab. Eng. Syst. Saf..