Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network

Water distribution networks are large complex systems affected by leaks, which often entail high costs and may severely jeopardise the overall water distribution performance. Successful leak location is paramount in order to minimize the impact of these leaks when occurring. Sensor placement is a key issue in the leak location process, since the overall performance and success of this process highly depends on the choice of the sensors gathering data from the network. Common problems when isolating leaks in large scale highly gridded real water distribution networks include leak mislabelling and the obtention of large number of possible leak locations. This is due to similarity of leak effect in the measurements, which may be caused by topological issues and led to incomplete coverage of the whole network. The sensor placement strategy may minimize these undesired effects by setting the sensor placement optimisation problem with the appropriate assumptions (e.g. geographically cluster alike leak behaviors) and by taking into account real aspects of the practical application, such as the acceptable leak location distance. In this paper, a sensor placement methodology considering these aspects and a general sensor distribution assessment method for leak diagnosis in water distribution systems is presented and exemplified with a small illustrative case study. Finally, the proposed method is applied to two real District Metered Areas (DMAs) located within the Barcelona water distribution network.Peer ReviewedPostprint (author's final draft

A method for sensor placement taking into account diagnosability criteria

International audienceThis paper presents a new approach to sensor placement based on diagnosability criteria. It is based on the study of structural matrices. Properties of structural matrices regarding detectability, discriminability and diagnosability are established in order to be used by sensor placement methods. The proposed approach manages any number of constraints modelled by linear or nonlinear equations and it does not require the design of analytical redundancy relations. Assuming that a constraint models a component and that the cost of the measurement of each variable is defined, a method determining sensor placements satisfying diagnosability specifications, where all the diagnosable, discriminable and detectable constraint sets are specified, is proposed. An application example dealing with a dynamical linear system is presented

Optimal sensor placement for sewer capacity risk management

2019 Spring.Includes bibliographical references.Complex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research

Efficient Detection on Stochastic Faults in PLC Based Automated Assembly Systems With Novel Sensor Deployment and Diagnoser Design

In this dissertation, we proposed solutions on novel sensor deployment and diagnoser design to efficiently detect stochastic faults in PLC based automated systems First, a fuzzy quantitative graph based sensor deployment was called upon to model cause-effect relationship between faults and sensors. Analytic hierarchy process (AHP) was used to aggregate the heterogeneous properties between sensors and faults into single edge values in fuzzy graph, thus quantitatively determining the fault detectability. An appropriate multiple objective model was set up to minimize fault unobservability and cost while achieving required detectability performance. Lexicographical mixed integer linear programming and greedy search were respectively used to optimize the model, thus assigning the sensors to faults. Second, a diagnoser based on real time fuzzy Petri net (RTFPN) was proposed to detect faults in discrete manufacturing systems. It used the real time PN to model the manufacturing plant while using fuzzy PN to isolate the faults. It has the capability of handling uncertainties and including industry knowledge to diagnose faults. The proposed approach was implemented using Visual Basic, and tested as well as validated on a dual robot arm. Finally, the proposed sensor deployment approach and diagnoser were comprehensively evaluated based on design of experiment techniques. Two-stage statistical analysis including analysis of variance (ANOVA) and least significance difference (LSD) were conducted to evaluate the diagnosis performance including positive detection rate, false alarm, accuracy and detect delay. It illustrated the proposed approaches have better performance on those evaluation metrics. The major contributions of this research include the following aspects: (1) a novel fuzzy quantitative graph based sensor deployment approach handling sensor heterogeneity, and optimizing multiple objectives based on lexicographical integer linear programming and greedy algorithm, respectively. A case study on a five tank system showed that system detectability was improved from the approach of signed directed graph's 0.62 to the proposed approach's 0.70. The other case study on a dual robot arm also show improvement on system's detectability improved from the approach of signed directed graph's 0.61 to the proposed approach's 0.65. (2) A novel real time fuzzy Petri net diagnoser was used to remedy nonsynchronization and integrate useful but incomplete knowledge for diagnosis purpose. The third case study on a dual robot arm shows that the diagnoser can achieve a high detection accuracy of 93% and maximum detection delay of eight steps. (3) The comprehensive evaluation approach can be referenced by other diagnosis systems' design, optimization and evaluation