Comparing risk of failure models in water supply networks using ROC curves

[EN] The problem of predicting the failure of water mains has been considered from different perspectives and using several methodologies in engineering literature. Nowadays, it is important to be able to accurately calculate the failure probabilities of pipes over time, since water company profits and service quality for citizens depend on pipe survival; forecasting pipe failures could have important economic and social implications. Quantitative tools (such as managerial or statistical indicators and reliable databases) are required in order to assess the current and future state of networks. Companies managing these networks are trying to establish models for evaluating the risk Of failure in order to develop a proactive approach to the renewal process, instead of using traditional reactive pipe substitution schemes. The main objective of this paper is to compare models for evaluating the risk of failure in water supply networks. Using real data from a water Supply company, this study has identified which network characteristics affect the risk of failure and which models better fit data to predict service breakdown. The comparison using the receiver operating characteristics (ROC) graph leads us to the conclusion that the best model is a generalized linear model. Also, we propose a procedure that can be applied to a pipe failure database, allowing the most appropriate decision rule to be chosen. (C) 2009 Elsevier Ltd. All rights reserved.The authors are indebted to the anonymous referees whose suggestions improved the original manuscript. This study was partially supported by a grant from MEyC (Ministerio de Educacion y Ciencia, Spain, Project CGL2004-05507). The research of Ana Debon was partially supported by a grant from Generalitat Valenciana (Grant no. GVPRE/2008/103).Debón Aucejo, AM.; Carrión García, A.; Cabrera Marcet, E.; Solano, H. (2010). Comparing risk of failure models in water supply networks using ROC curves. Reliability Engineering & System Safety. 95(1):43-48. doi:10.1016/j.ress.2009.07.004S434895

A Front-Line and Cost-Effective Model for the Assessment of Service Life of Network Pipes

[EN] In any water utility, a reliable assessment of the service life of the network pipes is a key piece within the big puzzle of assets management. This paper presents a new statistical model (basic pipes life assessment, BPLA) to assess the service life of pipes, to locate the pipes on the failures bath curve and to forecast the expected failures in future years. Its main novelties are the processing of pipe information (is that information what is adapted to the classical maintenance engineering and not the other way back) and the definition of two different time variables that can be analyzed in parallel. The first novelty makes the model less demanding in terms of data and software tools than others currently available, and the second one allows to get all the results after one single stage of calculation. To show its usability, the BPLA has been applied to a pipe network that supplies water to 500,000 citizens for which two years of failure records are available. Procedures and results have been compared to the well-known Weibull proportional hazard model (WPHM), with final relative errors lower than 10% and 15% on each particular result.The authors would like to thank Global Omnium for the support provided, both directly and through the Catedra Aguas de Valencia of the UPV, for the development of the works presented in this paper.Ramírez-Aguilar, RX.; López Jiménez, PA.; Torres Toro, D.; Cobacho Jordán, R. (2020). A Front-Line and Cost-Effective Model for the Assessment of Service Life of Network Pipes. Water. 12(3):1-23. https://doi.org/10.3390/w12030667S123123Shamir, U., & Howard, C. D. D. (1979). An Analytic Approach to Scheduling Pipe Replacement. Journal - American Water Works Association, 71(5), 248-258. doi:10.1002/j.1551-8833.1979.tb04345.xKleiner, Y., Nafi, A., & Rajani, B. (2010). Planning renewal of water mains while considering deterioration, economies of scale and adjacent infrastructure. Water Supply, 10(6), 897-906. doi:10.2166/ws.2010.571Christodoulou, S., & Deligianni, A. (2009). A Neurofuzzy Decision Framework for the Management of Water Distribution Networks. Water Resources Management, 24(1), 139-156. doi:10.1007/s11269-009-9441-2Kutyłowska, M. (2015). Neural network approach for failure rate prediction. Engineering Failure Analysis, 47, 41-48. doi:10.1016/j.engfailanal.2014.10.007Motiee, H., & Ghasemnejad, S. (2018). Prediction of pipe failure rate in Tehran water distribution networks by applying regression models. Water Supply, 19(3), 695-702. doi:10.2166/ws.2018.137Di Nardo, A., Di Natale, M., Giudicianni, C., Greco, R., & Santonastaso, G. F. (2017). Complex network and fractal theory for the assessment of water distribution network resilience to pipe failures. Water Supply, 18(3), 767-777. doi:10.2166/ws.2017.124Kutyłowska, M. (2018). Forecasting failure rate of water pipes. Water Supply, 19(1), 264-273. doi:10.2166/ws.2018.078Le Gat, Y., & Eisenbeis, P. (2000). Using maintenance records to forecast failures in water networks. Urban Water, 2(3), 173-181. doi:10.1016/s1462-0758(00)00057-1Alvisi, S., & Franchini, M. (2010). Comparative analysis of two probabilistic pipe breakage models applied to a real water distribution system. Civil Engineering and Environmental Systems, 27(1), 1-22. doi:10.1080/10286600802224064Kimutai, E., Betrie, G., Brander, R., Sadiq, R., & Tesfamariam, S. (2015). Comparison of Statistical Models for Predicting Pipe Failures: Illustrative Example with the City of Calgary Water Main Failure. Journal of Pipeline Systems Engineering and Practice, 6(4), 04015005. doi:10.1061/(asce)ps.1949-1204.0000196Santos, P., Amado, C., Coelho, S. T., & Leitão, J. P. (2016). Stochastic data mining tools for pipe blockage failure prediction. Urban Water Journal, 14(4), 343-353. doi:10.1080/1573062x.2016.1148178Debón, A., Carrión, A., Cabrera, E., & Solano, H. (2010). Comparing risk of failure models in water supply networks using ROC curves. Reliability Engineering & System Safety, 95(1), 43-48. doi:10.1016/j.ress.2009.07.004Davis, P., Silva, D. D., Marlow, D., Moglia, M., Gould, S., & Burn, S. (2008). Failure prediction and optimal scheduling of replacements in asbestos cement water pipes. Journal of Water Supply: Research and Technology-Aqua, 57(4), 239-252. doi:10.2166/aqua.2008.035Punurai, W., & Davis, P. (2017). Prediction of Asbestos Cement Water Pipe Aging and Pipe Prioritization Using Monte Carlo Simulation. Engineering Journal, 21(2), 1-13. doi:10.4186/ej.2017.21.2.1Yoo, D., Kang, D., Jun, H., & Kim, J. (2014). Rehabilitation Priority Determination of Water Pipes Based on Hydraulic Importance. Water, 6(12), 3864-3887. doi:10.3390/w6123864D’Ercole, M., Righetti, M., Raspati, G., Bertola, P., & Maria Ugarelli, R. (2018). Rehabilitation Planning of Water Distribution Network through a Reliability—Based Risk Assessment. Water, 10(3), 277. doi:10.3390/w10030277Rajani, B., & Kleiner, Y. (2001). Comprehensive review of structural deterioration of water mains: physically based models. Urban Water, 3(3), 151-164. doi:10.1016/s1462-0758(01)00032-2Kropp, I., & Baur, R. (2005). Integrated failure forecasting model for the strategic rehabilitation planning process. Water Supply, 5(2), 1-8. doi:10.2166/ws.2005.0015García-Mora, B., Debón, A., Santamaría, C., & Carrión, A. (2015). Modelling the failure risk for water supply networks with interval-censored data. Reliability Engineering & System Safety, 144, 311-318. doi:10.1016/j.ress.2015.08.003Lei, Y. (2008). Evaluation of three methods for estimating the Weibull distribution parameters of Chinese pine (Pinus tabulaeformis ). Journal of Forest Science, 54(No. 12), 566-571. doi:10.17221/68/2008-jfsDatsiou, K. C., & Overend, M. (2018). Weibull parameter estimation and goodness-of-fit for glass strength data. Structural Safety, 73, 29-41. doi:10.1016/j.strusafe.2018.02.002Package survival https://cran.r-project.org/web/packages/survival/survival.pdfChristodoulou, S. E. (2010). Water Network Assessment and Reliability Analysis by Use of Survival Analysis. Water Resources Management, 25(4), 1229-1238. doi:10.1007/s11269-010-9679-

Modelling the failure risk for water supply networks with interval-censored data

[EN] In reliability, sometimes some failures are not observed at the exact moment of the occurrence. In that case it can be more convenient to approximate them by a time interval. In this study, we have used a generalized non-linear model developed for interval-censored data to treat the life time of a pipe from its time of installation until its failure. The aim of this analysis was to identify those network characteristics that may affect the risk of failure and we make an exhaustive validation of this analysis. The results indicated that certain characteristics of the network negatively affected the risk of failure of the pipe: an increase in the length and pressure of the pipes, a small diameter, some materials used in the manufacture of pipes and the traffic on the street where the pipes are located. Once the model has been correctly fitted to our data, we also provided simple tables that will allow companies to easily calculate the pipe's probability of failure in a future.The research by A. Debón was supported by the Ministerio de Ciencia e Innovación of Spain under grant no. MTM2013-45381-PGarcía Mora, MB.; Debón Aucejo, AM.; Santamaría Navarro, C.; Carrión García, A. (2015). Modelling the failure risk for water supply networks with interval-censored data. Reliability Engineering & System Safety. 144:311-318. https://doi.org/10.1016/j.ress.2015.08.003S31131814

Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data

Credit to the private sector has risen rapidly in European emerging markets but its risk evaluation has been largely neglected. Using retail-loan banking data from the Czech Republic we construct two credit risk models based on logistic regression and Classification and Regression Trees. Both methods are comparably efficient and detect similar financial and socio-economic variables as the key determinants of default behavior. We also construct a model without the most important financial variable (amount of resources) that performs very well. This way we confirm significance of socio-demographic variables and link our results with specific issues characteristic to new EU members.credit scoring, discrimination analysis, banking sector, pattern recognition, retail loans, CART, European Union

Predictive inference for system reliability after common-cause component failures

This paper presents nonparametric predictive inference for system reliability following common-cause failures of components. It is assumed that a single failure event may lead to simultaneous failure of multiple components. Data consist of frequencies of such events involving particular numbers of components. These data are used to predict the number of components that will fail at the next failure event. The effect of failure of one or more components on the system reliability is taken into account through the system׳s survival signature. The predictive performance of the approach, in which uncertainty is quantified using lower and upper probabilities, is analysed with the use of ROC curves. While this approach is presented for a basic scenario of a system consisting of only a single type of components and without consideration of failure behaviour over time, it provides many opportunities for more general modelling and inference, these are briefly discussed together with the related research challenges

Developing Decision Tree Models to Create a Predictive Blockage Likelihood Model for Real-World Wastewater Networks

To reduce the blockages occurring on wastewater networks, reducing costs, customer and environmental impact, greater levels of proactive maintenance are being conducted by water and sewerage companies. For effective prioritisation of this maintenance, an accurate model of blockage likelihood is required. This paper presents the development of a model, for provision of a blockage likelihood level and verification using unseen data, based on previous decision tree models constructed using the asset and historical incident data from the wastewater network of Dŵr Cymru Welsh Water. The model has been developed here using the geographical grouping of sewers and the application of ensemble techniques, with the results illustrating the potential benefits which can be derived from these techniques.The work has been conducted as part of a Knowledge Transfer Partnership (KTP) with funding provided by Innovate UK and Dŵr Cymru Welsh Water (DCWW), working in collaboration with the University of Exeter’s Centre for Water Systems (CWS)

Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

[EN] Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures. The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to examine their use in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures.The authors are indebted to the anonymous referees whose suggestions improved the original manuscript. This work was supported by a grant from PAID-06-08 (Programa de Apoyo a la Investigacion y Desarrollo) of the Universitat Politecnica de Valencia.Debón Aucejo, AM.; García-Díaz, JC. (2012). Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data. Reliability Engineering and System Safety. 100:102-114. https://doi.org/10.1016/j.ress.2011.12.022S10211410

An evolutionary fuzzy system to support the replacement policy in water supply networks: The ranking of pipes according to their failure risk

Article number 107731In this study, an evolutionary fuzzy system is proposed to predict unexpected pipe failures in water supply networks. The system seeks to underpin the decisions of management companies regarding the maintenance and replacement plans of pipes. On the one hand, fuzzy logic provides high degrees of interpretability over other black box models, which is requested in engineering application where decisions have social consequences. On the other hand, the genetic algorithm helps to optimize the parameters that govern the model, specifically, for two purposes: (i) the selection of variables; and (ii) the optimization of membership functions. Data from a real water supply network are used to evaluate the accuracy of the developed system. Several graphs that depict the ranking of pipes according to their risk of failure against the network length to be replaced support the choice of the most successful model. In fact, results demonstrate that the annual replacement of 6.75% of the network length makes it possible to prevent 41.14% of unexpected pipe failuresEm

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively