Cumulative residual Kullback-Leibler divergence based sensor placement using reliability criteria

Prakash, Om; Bhushan, Mani

doi:10.1016/j.compchemeng.2022.107908

Cited by 4 publications

(17 citation statements)

References 30 publications

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“…In active redundancy, given redundant sensors to measure a variable, the measurement will be available if at least one of the sensors placed on a variable is functional, and it becomes unavailable if all the sensors placed on a variable fail. 15,28 In standby redundancy, only one sensor operates at a time, and other sensors are kept in standby mode. A switching mechanism detects failure of currently used sensors and switches on one of the standby sensors.…”

Section: Background Materialmentioning

confidence: 99%

“…A switching mechanism detects failure of currently used sensors and switches on one of the standby sensors. 15,27 The probability of the measurement of the variable being available at any given time will depend on the redundancy mode being used. As opposed to hardware redundancy, software or analytical redundancy exists when a variable can be estimated using its relationships with other variables as captured by a process model.…”

Section: Background Materialmentioning

confidence: 99%

“…CRKL has been used in reliability literature to compare the reliability of one variable relative to the other 25 and has also been used for sensor placement design for linear systems. 15 2.2.4. System Reliability of Estimation.…”

Section: Background Materialmentioning

confidence: 99%

“…14 In contrast to most of the sensor placement design works, Maquin et al 10 explicitly considered reliability as a function of time and suggested the use of Mean Time to Failure as a criterion for sensor placement design. Recently, Prakash and Bhushan 15 also considered the time-varying nature of system reliability for a linear flow process. They showed that if the system reliability is considered as a constant value corresponding to a specific time instant, then the optimal sensor placement is dependent on the time instant being considered.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned earlier, Prakash and Bhushan 15 presented a method to compute the time-varying system reliability of estimation using graph theoretical approaches for linear steadystate processes. These graph theoretical methods cannot be directly extended to find the time-varying system reliability of estimation for nonlinear processes.…”

Section: Introductionmentioning

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: Background Materialmentioning

confidence: 99%

Section: Background Materialmentioning

confidence: 99%

Section: Background Materialmentioning

confidence: 99%