M
Mani Bhushan
Researcher at Indian Institute of Technology Bombay
Publications - 103
Citations - 1330
Mani Bhushan is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Kalman filter & Wireless sensor network. The author has an hindex of 17, co-authored 89 publications receiving 1115 citations. Previous affiliations of Mani Bhushan include University of Alberta & Purdue University.
Papers
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Locating sensors in complex chemical plants based on fault diagnostic observability criteria
TL;DR: A digraph-based approach is proposed for the problem of sensor location for identification of faults and various graph algorithms that use the developed digraph in deciding the location of sensors based on the concepts of observability and resolution are discussed.
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Comprehensive design of a sensor network for chemical plants based on various diagnosability and reliability criteria. 1. Framework
TL;DR: In this article, a reliability maximization based optimization framework for sensor location from a fault diagnosis perspective is presented, aimed toward maximizing the reliability of the fault monitoring system while satisfying the constraints imposed on the system.
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Design of Sensor Network Based on the Signed Directed Graph of the Process for Efficient Fault Diagnosis
TL;DR: Algorithms for sensor network design based on the signed directed graph (SDG) representation of the process are detailed and applied to two chemical engineering case studies.
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Design of sensor location based on various fault diagnostic observability and reliability criteria
TL;DR: In this paper, a reliability formulation for selecting optimal sensors is presented, which takes into account quantitative information such as fault occurrence probabilities, sensor failure probabilities, and sensor costs, and heuristics to solve the posed problem are also discussed.
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PHASUITE: AN AUTOMATED HAZOP ANALYSIS TOOL FOR CHEMICAL PROCESSES Part I: Knowledge Engineering Framework
TL;DR: A software system (PHASuite) for automated HAZOP analysis has been developed that can greatly increase the efficiency of the HAZop analysis, support best analysis practices, and provide foundation for reuse of the safety knowledge generated from the analysis.