Sensor Placement for Fault Diagnosis Performance Maximization under Budgetary Constraints

This paper presents a strategy based on fault diagnosability maximization to optimally locate sensors in complex systems. The goal is to characterize and determine a sensor configuration that guarantees a maximum degree of diagnosability and does not exceed a maximum sensor configuration cost. The strategy is based on the structural system model. Structural analysis is a powerful tool for dealing with complex nonlinear systems. The proposed approach is successfully applied to a Fuel Cell Stack System

Sensor placement for fault diagnosis based on structural models: application to a fuel cell stak system

The present work aims to increase the diagnosis systems capabilities by choosing the location of sensors in the process. Therefore, appropriate sensor location will lead to better diagnosis performance and implementation easiness. The work is based on structural models ands some simplifications are considered in order to only focus on the sensor placement analysis. Several approaches are studied to solve the sensor placement problem. All of them find the optimal sensor configuration. The sensor placement techniques are applied to a fuel cell stack system. The model used to describe the behaviour of this system consists of non-linear equations. Furthermore, there are 30 candidate sensors to improve the diagnosis specifications. The results obtained from this case study are used to strength the applicability of the proposed approaches.El present treball té per objectiu incrementar les prestacions dels diagnosticadors mitjançant la localització de sensors en el procés. D'aquesta manera, instal·lant els sensors apropiats s'obtenen millors diagnosticador i més facilitats d'implementació. El treball està basat en models estructurals i contempla una sèrie de simplificacions per tal de entrar-se només en la problemàtica de la localització de sensors. S'utilitzen diversos enfocs per tal de resoldre la localització de sensors, tot ells tenen com objectiu trobar la configuració òptima de sensors. Les tècniques de localització de sensors són aplicades a un sistema basat en una pila de combustible. El model d'aquest sistema està format per equacions no lineals. A més, hi ha la possibilitat d'instal·lar fins a 30 sensors per tal de millorar la diagnosis del sistema. Degut a aquestes característiques del sistema i del model, els resultats obtinguts mitjançant aquest cas d'estudi reafirmen l'aplicabilitat dels mètodes proposats.Postprint (published version

Sensor placement for leak detection and location in water distribution networks

The performance of a leak detection and location algorithm depends on the set of measurements that are available in the network. This works presents and optimization strategy that maximizes the leak diagnosability performance of the network. The goal is to characterize and determine a sensor configuration that guarantees a maximum degree of disnosability while the sensor configuration cost satifies a budgetary constraint. To efficiently handle the complexity of the distribuion networl an efficient branch and bound search strategy based on a strucutrual model is used. However, in order to reduce even more the size and the complexity of the problem the present work proposes to combine this methodology with clustering techniques. The strategy developed in this work is successfully applied to determine the optimal set of pressure sensors that should be installed to a District Metered Area in the Baarcelona Water Distribution Network.Peer ReviewedAward-winningPostprint (published version

Sensor placement for fault location identification in water networks: A minimum test cover approach

This paper focuses on the optimal sensor placement problem for the identification of pipe failure locations in large-scale urban water systems. The problem involves selecting the minimum number of sensors such that every pipe failure can be uniquely localized. This problem can be viewed as a minimum test cover (MTC) problem, which is NP-hard. We consider two approaches to obtain approximate solutions to this problem. In the first approach, we transform the MTC problem to a minimum set cover (MSC) problem and use the greedy algorithm that exploits the submodularity property of the MSC problem to compute the solution to the MTC problem. In the second approach, we develop a new \textit{augmented greedy} algorithm for solving the MTC problem. This approach does not require the transformation of the MTC to MSC. Our augmented greedy algorithm provides in a significant computational improvement while guaranteeing the same approximation ratio as the first approach. We propose several metrics to evaluate the performance of the sensor placement designs. Finally, we present detailed computational experiments for a number of real water distribution networks

Clustering techniques applied to sensor placement for leak detection and location in water distribution networks

This work presents an optimization strategy that maximizes the leak locatability performance of water distribution networks (WDN). The goal is to characterize and determine a sensor configuration that guarantees a maximum degree of locatability while the sensor configuration cost satisfies a budgetary constraint. The method is based on pressure sensitivity matrix analysis and an exhaustive search strategy. In order to reduce the size and the complexity of the problem the present work proposes to combine this methodology with clustering techniques. The strategy developed in this work is successfully applied to determine the optimal set of pressure sensors that should be installed in a district metered area (DMA) in the Barcelona WDN.Peer ReviewedPostprint (published version

Development Of A MATLAB-based Structural Analysis Toolbox For Sensor Placement In A Multi-domain Physical System

This paper discusses a Bond Graph (BG) Structural Analysis Toolbox developed in MATLAB® (MATSAT) that performs causal analysis on the BG and assists the user in the sensor selection process for a multi-domain physical system. MATSAT contains modules for performing the Sequential Causality Assignment Procedure (SCAP) and Causal Path Search (CaPS). The modules can be combined to check for structural properties such as structural observability (SO) for any sensor set. The working of MATSAT is shown for standard systems. Verification of SCAP, CaPS, and necessary and sufficient SO conditions is shown

Ensuring Uniform Energy Consumption in Non-Deterministic Wireless Sensor Network to Protract Networks Lifetime

Wireless sensor networks have enticed much of the spotlight from researchers all around the world, owing to its extensive applicability in agricultural, industrial and military fields. Energy conservation node deployment stratagems play a notable role for active implementation of Wireless Sensor Networks. Clustering is the approach in wireless sensor networks which improves energy efficiency in the network. The clustering algorithm needs to have an optimum size and number of clusters, as clustering, if not implemented properly, cannot effectively increase the life of the network. In this paper, an algorithm has been proposed to address connectivity issues with the aim of ensuring the uniform energy consumption of nodes in every part of the network. The results obtained after simulation showed that the proposed algorithm has an edge over existing algorithms in terms of throughput and networks lifetime

Sensor selection for fault diagnostics using performance metric

As technology advances, modern systems are becoming increasingly complex, consisting of large numbers of components, and therefore large numbers of potential component failures. These component failures can result in reduced system performance, or even system failure. The system performance can be monitored using sensors, which can help to detect faults and diagnose failures present in the system. However, sensors increase the weight and cost of the system, and therefore, the number of sensors may be limited, and only the sensors that provide the most useful system information should be selected.In this paper, a novel sensor performance metric is introduced. This performance metric is used in a sensor selection process, where the sensors are chosen based on their ability to detect faults and diagnose failures of components, as well as the effect the component failures have on system performance. The proposed performance metric is a suitable solution for the selection of sensors for fault diagnostics. In order to model the outputs that would be measured by the sensors, a Bayesian Belief Network (BBN) is developed. Sensors are selected using the performance metric, and sensor readings can be introduced in the BBN. The results of the BBN can then be used to rank the component failures in order of likelihood of causing the sensor readings. To illustrate the proposed approach, a simple flow system is used in this paper

Graph Structural Residuals: A Learning Approach to Diagnosis

Traditional model-based diagnosis relies on constructing explicit system models, a process that can be laborious and expertise-demanding. In this paper, we propose a novel framework that combines concepts of model-based diagnosis with deep graph structure learning. This data-driven approach leverages data to learn the system's underlying structure and provide dynamic observations, represented by two distinct graph adjacency matrices. Our work facilitates a seamless integration of graph structure learning with model-based diagnosis by making three main contributions: (i) redefining the constructs of system representation, observations, and faults (ii) introducing two distinct versions of a self-supervised graph structure learning model architecture and (iii) demonstrating the potential of our data-driven diagnostic method through experiments on a system of coupled oscillators