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Sensor Placement for Diagnosis of Large-Scale, Complex Systems: Advancement of Structural Methods

Rahman, Brian M
http://orcid.org/0000-0003-0208-5813
http://rave.ohiolink.edu/etdc/view?acc_num=osu1562859497638274

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Mechanical Engineering.
Technology and societal advancements drive industry adaptations. What was considered advanced ten years ago is now considered traditional, and what is advanced today will become the new standard. Systems in the modern world are increasing in complexity and requiring a broader range of components to provide the functions necessary to stay competitive and operate at peak potential. Technological advancements and the demand for immediate satisfaction only fuel the progression. What must occur in the background for the modern world to operate efficiently and with as few problems and delays as possible? How do large, complex systems operate without issues? If an issue were to occur, how can that issue be detected, isolated, and resolved with as little troubleshooting and downtime as possible? This dissertation provides a complete and systematic methodology to efficiently detect and isolate faults within large, complex systems through the advancement of a technique called structural analysis. Structural analysis investigates the connections, or structure, between unknowns, knowns, and faults through the constraints (equations) of a system. The method hinges on an analytical model being converted into a structural model, represented by bipartite graphs or incidence matrices, which allow for detection and isolation properties to be investigated by decomposition. Open issues with structural analysis and model-based diagnosis including how to address diagnosis in systems that are under-constrained, how to optimize sensor placement in under-constrained and/or large-scale systems, and how to systematically address diagnosis in large-scale complex systems are addressed. The application that motivates this research is that of compressed air systems in industrial processes or plants. Compressed air systems are fully adjustable, do not interfere with electrical monitoring equipment, are able to operate in extreme temperatures, and can be stored in pressurized tanks or vessels of virtually any size. In an industrial plant, the extensive use of compressed air for operation drives the need for minimal downtime due to faults. It is estimated that a ¼” diameter leak in an industrial compressed air system incurs an annual energy cost of $4,000 [1]. Equipment downtime associated with finding and repairing the leak can dwarf the energy cost, especially with the modern and societal demand for immediate satisfaction. The systematic methodology developed is applicable to any system modeled through equations, implemented in the first phase on a physical pneumatic test bench for data acquisition and proof of concept, and scaled in the second phase through automation and optimization techniques to a real large-scale industrial complex system.
Giorgio Rizzoni (Advisor)
Cheena Srinivasan (Committee Member)
Tunc Aldemir (Committee Member)
Ran Dai (Committee Member)
Qadeer Ahmed (Committee Member)
460 p.
Electrical Engineering; Mechanical Engineering
Fault Detection; Fault Isolation; Model-based Diagnosis; Structural Analysis; Large-Scale Systems; Sensor Placement; Fluid Systems

Recommended Citations

Citations

  • Rahman, B. M. (2019). Sensor Placement for Diagnosis of Large-Scale, Complex Systems: Advancement of Structural Methods [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1562859497638274

    APA Style (7th edition)

  • Rahman, Brian. Sensor Placement for Diagnosis of Large-Scale, Complex Systems: Advancement of Structural Methods. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1562859497638274.

    MLA Style (8th edition)

  • Rahman, Brian. "Sensor Placement for Diagnosis of Large-Scale, Complex Systems: Advancement of Structural Methods." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1562859497638274

    Chicago Manual of Style (17th edition)

osu1562859497638274
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This open access ETD is published by The Ohio State University and OhioLINK.