Design of robust, reliable sensor networks using constraint programming

Kotecha, Prakash; Bhushan, Mani; Gudi, Ravindra D.

doi:10.1016/j.compchemeng.2008.03.005

Cited by 13 publications

(17 citation statements)

References 29 publications

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“…Figure 1 shows the working principle of CP to solve a CSP and a detailed algorithm can be obtained from literature. 2,10,11 Unlike mathematical programming techniques, CP does not rely on integer relaxations or gradients but instead uses constraint propagation to reduce the domain of the variables. Thus, it avoids convergence to suboptimal (or local) solutions irrespective of the nature (convex/nonconvex) of the problem.…”

Section: Constraint Programmingmentioning

confidence: 99%

“…Additionally, the search strategy of CP can be easily modified so as to satisfy some soft constraints. 11 As will be shown in this article, CP can also be used to determine Kbest feasible solutions of a single objective optimization problem. In literature, CP and Integer Programming (IP) have been compared for the modified generalized assignment problem, 12 the template design problem, 13 the progressive party problem, 14 and the change problem.…”

Section: Constraint Programmingmentioning

confidence: 99%

“…This design problem involves the placement of sensors for a linear overall mass flow process, to ensure nonredundant sensor network design. Kotecha et al 11 proposed a cutsetbased framework that can been used to unify objectives like reliability and precision. In particular, we consider three different objectives namely (i) minimization of the network failure rate (k), (ii) minimization of network variance (r 2 ), and (iii) minimization of the network cost (C) subject to the criterion of an observable network.…”

Section: Design Of Sensor Network From a Reliability Perspectivementioning

confidence: 99%

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Efficient optimization strategies with constraint programming

2009

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In this article, we propose novel strategies for the efficient determination of multiple solutions for a single objective, as well as globally optimal pareto fronts for multiobjective, optimization problems using Constraint Programming (CP). In particular, we propose strategies to determine, (i) all the multiple (globally) optimal solutions of a single objective optimization problem, (ii) K-best feasible solutions of a single objective optimization problem, and (iii) globally optimal pareto fronts (including nonconvex pareto fronts) along with their multiple realizations for multiobjective optimization problems. It is shown here that the proposed strategy for determining K-best feasible solutions can be tuned as per the requirement of the user to determine either K-best distinct or nondistinct solutions. Similarly, the strategy for determining globally optimal pareto fronts can also be modified as per the requirement of the user to determine either only the distinct set of pareto points or determine the pareto points along with all their multiple realizations. All the proposed techniques involve appropriately modifying the search techniques and are shown to be computationally efficient in terms of not requiring successive re-solving of the problem to obtain the required solutions. This work therefore convincingly addresses the issue of efficiently determining globally optimal pareto fronts; in addition, it also guarantees the determination of all the possible realizations associated with each pareto point. The uncovering of such solutions can greatly aid the designer in making informed decisions. The proposed approaches are demonstrated via two case studies, which are nonlinear, combinatorial optimization problems, taken from the area of sensor network design.

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Section: Constraint Programmingmentioning

confidence: 99%

Section: Constraint Programmingmentioning

confidence: 99%

Section: Design Of Sensor Network From a Reliability Perspectivementioning

confidence: 99%

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Efficient optimization strategies with constraint programming

2009

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“…This increases the diagnosis ability (failure detection and localization [5]) of a system and makes sensor optimization critical for failure diagnosis. The problem of designing a sensor network is to find a set of sensors so that costs, observability, reliability, estimation accuracy and flexibility are satisfied [16].…”

Section: Sensor Optimizationmentioning

confidence: 99%

Decision trees and the eﬀects of feature extraction parameters for robust sensor network design

Gerdes

Galar

Scholz

2016

EiN

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IntroductionThis paper presents a condition monitoring system with sensor optimization capabilities to prevent unscheduled delays in the aircraft industry. Unscheduled delays cost airlines a great deal of money but can be prevented by condition monitoring [11]. The aim was to develop a simple condition monitoring system that can be understood by humans and modified by experts to incorporate knowledge that is not in the learning data set, using decision trees as the main tool. Decision Gerdes M, GAlAr d, scholz d. decision trees and the effects of feature extraction parameters for robust sensor network design. eksploatacja i Niezawodnosc - Maintenance and reliability 2017; 19 (1): 31-42, http://dx.doi.org/10.17531/ein.2017.1.5. trees satisfy the requirements and provide a ranking of data sources for condition monitoring.The first section of the paper gives the motivation for developing a condition monitoring system with sensor optimization capabilities and explains the basic concepts of the proposed method. The second section explains the method in detail. Section three discusses the experiments validating it. The results of the validation experiments are given in section four. The paper concludes with a discussion of the results. New and better monitoring approaches are required for condition monitoring, because systems are becoming more complex and more difficult to monitor [32]. Condition monitoring requires reliable sensors. To obtain enough sensing data, special attention should be given to optimizing sensor allocation to ensure system diagnosability, lower sensing cost and reduce time to diagnosis [37]. Sensors can be used to determine the system health of control systems; a failed sensor can lead to a loss of process control [18]. The information about a system is incomplete, if a sensor fails. Complex systems are often monitored by multiple sensors. An advantage of a multi sensor system is that a single failed sensor shows its effects in multiple sensors [18].This means the system condition is defined by all information from the sensors. However, the system's health status becomes uncertain when a sensor fails or sends wrong data. This could trigger incorrect maintenance, including maintenance on a part with no failure, as well as long maintenance times to find the correct fault or not noticing the fault at all.The Safety Integrity Level (SIL) defines the probability that the system safety function for a Safety Instrumented System (SIS) can be executed. There are four SILs; level four is the level with the highest probability that an SIS can be performed. Sensor failure detection (sensor validation) is a critical part of the safety function of a system. When a failure is detected SIS is put into a safe state to avoid risk and damage to humans and machines [14,13].Redundancy is used to reduce the risk of model uncertainty [5]. One way to create sensor redundancy is hardware redundancy; another is analytical redundancy [5]. Analytical redundancy assumes multiple sensors deliver the same information, and,...

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“…CP has found widespread application in solving combinatorial optimization [12]- [14] such as Assignment Problems [15], Network Problems [16], [17], Production Planning/Scheduling, Personnel Allocation. The benefits of CP include the ease of modeling as it does not restrict the user like mathematical programming, guarantee of the optimality of the solution even for non linear discrete problems and the ease of determination of value added solutions such as realizations and K-best solutions.…”

Section: Constraint Programmingmentioning

confidence: 99%

Constraint Programming Based Production Planning in a Petrochemical Industry

Kadambur¹,

Kotecha²

2014

IJMO

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Abstract-In this article, we provide a constraint programming based optimization model to determine the optimal production planning to maximize profit in a petrochemical industry. The proposed model despite its simplicity overcomes the limitations of the mixed integer linear programming model available in literature. The benefits of the proposed model are demonstrated on various instances of two case studies and it is shown that the proposed formulation enables an increase in the profit in the range of 0.58 to 3.68%.

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Design of robust, reliable sensor networks using constraint programming

Cited by 13 publications

References 29 publications

Efficient optimization strategies with constraint programming

Efficient optimization strategies with constraint programming

Decision trees and the eﬀects of feature extraction parameters for robust sensor network design

Constraint Programming Based Production Planning in a Petrochemical Industry

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