Design of Non-Redundant Sensor Networks for Reliable Estimation in Nonlinear Systems

Patel, Garima; Bhushan, Mani

doi:10.1016/b978-0-323-85159-6.50221-9

Cited by 1 publication

(12 citation statements)

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“…To clearly highlight the benefit of the proposed approach, we compare the performance of the optimal sensors obtained by the proposed approach with those obtained by the approach presented in Patel and Bhushan for nonlinear steady-state systems. In their work, system reliability was defined as the minimum reliability of estimation among all variables and not as joint probability of estimation as considered in the current work. Further, they considered only point values of system reliability corresponding to sensor failures at a specific time for nonredundant sensor placement.…”

Section: Case Studiesmentioning

confidence: 99%

“…In their work, system reliability was defined as the minimum reliability of estimation among all variables and not as joint probability of estimation as considered in the current work. Further, they considered only point values of system reliability corresponding to sensor failures at a specific time for nonredundant sensor placement.…”

Section: Case Studiesmentioning

confidence: 99%

“…This would correspond to C * = 5 if each sensor is assumed to be of unit cost. Specifically, we compare the following three sensor placements. Optimal sensors obtained by Patel and Bhushan for t = 1 year: The optimal sensors obtained are listed in Table . The variation with time of the resulting system reliability with this set of sensors is shown in Figure (green curve in the figure). Optimal sensors obtained by Patel and Bhushan for t = 3 years: The optimal sensors obtained are again listed in Table , and these turn out to be different from those obtained for t = 1 year.…”

Section: Case Studiesmentioning

confidence: 99%

“…Specifically, we compare the following three sensor placements. Optimal sensors obtained by Patel and Bhushan for t = 1 year: The optimal sensors obtained are listed in Table . The variation with time of the resulting system reliability with this set of sensors is shown in Figure (green curve in the figure). Optimal sensors obtained by Patel and Bhushan for t = 3 years: The optimal sensors obtained are again listed in Table , and these turn out to be different from those obtained for t = 1 year. The variation with time of the resulting system reliability with this set of sensors is also shown in Figure (blue curve in the figure). Optimal sensors obtained by the proposed approach: The optimal sensors obtained by solving the optimization problem given in eq are listed in Table .…”

Section: Case Studiesmentioning

confidence: 99%

“…Nonlinear steady-state CSTR case study: system reliabilities obtained using proposed approach and existing method for a nonredundant configuration.…”

Section: Case Studiesmentioning

confidence: 99%

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Reliability-Based Sensor Placement Design in Nonlinear Processes Using Cumulative Residual Kullback–Leibler Divergence

Patel,

Bhushan

2024

Ind. Eng. Chem. Res.

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In the current work, we propose optimal sensor placement for reliable estimation of both steady-state as well as dynamical nonlinear systems. While there is significant work on reliability-based sensor placement for linear processes, there is hardly any work for nonlinear systems. For nonlinear systems, we use appropriate structural matrices to check system observability for a given set of measurements and hence, our work is applicable at the design stage where exact operating conditions or values of various parameters may not be known. Characterization of system observability, along with knowledge of sensor failure probabilities as functions of time, allows us to define system reliability of estimation as a function of time. Subsequently, we propose to use an information theoretic, cumulative residual Kullback–Leibler (CRKL) divergence-based sensor placement design approach for reliable estimation of variables for both steady-state and dynamical nonlinear processes where the objective is to minimize the difference between a user-specified target system reliability of estimation function and the one obtained by a given set of sensors. Thus, this approach enables an end user to obtain a tailored optimal design by specifying their preference as the target system reliability in the objective function. Explicit incorporation of the time-varying nature of system reliability in computing the CRKL objective ensures a sensor placement design that leads to desired reliability throughout the life of the plant. The proposed sensor placement design problem is a nonlinear integer programming problem, and a greedy heuristic is used to solve the problem. The utility of the proposed approach is demonstrated on benchmark continuous stirred tank reactor and Tennessee Eastman case studies taken from the literature.

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Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

“…Nonlinear steady-state CSTR case study: system reliabilities obtained using proposed approach and existing method for a nonredundant configuration.…”

Section: Case Studiesmentioning

confidence: 99%

See 3 more Smart Citations