Journal ArticleDOI
A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis
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TLDR
A combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis that combines the completeness property of SDG with the high diagnostic resolution property of QTA.Abstract:
In this article a combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis has been proposed. The SDG is the first level in this framework and provides a possible candidate set of faults based on the incipient response of the process. The search for the actual fault is performed based on a QTA (level 2), which uses the temporal evolution of the sensors for further resolution. Thus, this framework combines the completeness property of SDG with the high diagnostic resolution property of QTA. Methods to address the problem of incorrect diagnosis arising due to incorrect measurement of initial response have also been presented. The proposed approach is tested on the Tennessee Eastman (TE) case study. Correct fault diagnosis is performed in all possible single fault scenarios. It is shown that this framework provides fast, reliable and accurate incipient fault diagnosis.read more
Citations
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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches
TL;DR: This is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed, respectively, from the knowledge-based and hybrid/active viewpoints.
Journal ArticleDOI
Deep convolutional neural network model based chemical process fault diagnosis
Hao Wu,Jinsong Zhao +1 more
TL;DR: A fault diagnosis method based on a DCNN model consisting of convolutional layers, pooling layers, dropout, fully connected layers is proposed for chemical process fault diagnosis and the benchmark Tennessee Eastman (TE) process is utilized to verify the outstanding performance.
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Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges
TL;DR: The features of different model-based and data-driven FD-HM approaches are investigated separately as well as the existing works that attempted to integrate both of them are investigated.
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An Overview of Industrial Alarm Systems: Main Causes for Alarm Overloading, Research Status, and Open Problems
TL;DR: Four main causes are identified as the culprits for alarm overloading, namely, chattering alarms due to noise and disturbance, alarm variables incorrectly configured, alarm design isolated from related variables, and abnormality propagation owing to physical connections.
Journal ArticleDOI
Integration of process design and control: A review
TL;DR: In this paper, the authors present a thematic review of the methods for integration of process design and control, and the evolution paths of these methods are described and the advantages and disadvantages of each method are explained.
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How iris recognition works
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A plant-wide industrial process control problem
James J. Downs,E.F. Vogel +1 more
TL;DR: In this article, a model of an industrial chemical process for the purpose of developing, studying and evaluating process control technology is presented, which is well suited for a wide variety of studies including both plantwide control and multivariable control problems.
Journal ArticleDOI
A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods
TL;DR: This three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives and broadly classify fault diagnosis methods into three general categories and review them in three parts.
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A review of process fault detection and diagnosis: Part III: Process history based methods
TL;DR: This final part discusses fault diagnosis methods that are based on historic process knowledge that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.