Stochastic data mining tools for pipe blockage failure prediction

Santos, Pedro; Amado, Conceição; Coelho, S. T.; Leitão, João P.

doi:10.1080/1573062x.2016.1148178

Cited by 19 publications

(14 citation statements)

References 22 publications

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“…Harvey and McBean (2014) apply random forests to predict the structural condition of sanitary sewer pipes. Santos, Amado, Coelho, and Leitão (2017) used the random forest algorithm to predict pipe blockage in sewers. However, random forests constitute only one possibility of ensemble methods for decision tree learning and, moreover, this (blue circle and green square).…”

Section: Decision Treesmentioning

confidence: 99%

Pipe failure modelling for water distribution networks using boosted decision trees

Winkler

Haltmeier

Kleidorfer

et al. 2018

Structure and Infrastructure Engineering

114

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Pipe failure modelling is an important tool for strategic rehabilitation planning of urban water distribution infrastructure. Rehabilitation predictions are mostly based on existing network data and historical failure records, both of varying quality. This paper presents a framework for the extraction and processing of such data to use it for training of decision tree-based machine learning methods. The performance of trained models for predicting pipe failures is evaluated for simple as well as more advanced, ensemblebased, decision tree methods. Bootstrap aggregation and boosting techniques are used to improve the accuracy of the models. The models are trained on 50% of the available data and their performance is evaluated using confusion matrices and receiver operating characteristic curves. While all models show very good performance, the boosted decision tree approach using random undersampling turns out to have the best performance and thus is applied to a real world case study. The applicability of decision tree methods for practical rehabilitation planning is demonstrated for the pipe network of a medium sized city.

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Section: Decision Treesmentioning

confidence: 99%

Pipe failure modelling for water distribution networks using boosted decision trees

Winkler

Haltmeier

Kleidorfer

et al. 2018

Structure and Infrastructure Engineering

114

View full text Add to dashboard Cite

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“…Ensemble classification integrates multiple models to improve the accuracy and reliability of estimations or decisions obtained using a single model [63].…”

Section: Data-driven Modelmentioning

confidence: 99%

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

et al. 2021

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Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.

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“…In the search for efficient models and tools to predict the physical condition of underground sewer infrastructure, studies such as Sousa, Matos, and Matias (2014), Jiang, Keller, Bond, and Yuan (2016), Santos et al (2017), Caradot et al (2018), andHernández, Caradot, Sonnenberg, Rouault, and were aimed to compare a collection of different models, and identifying the ones that produced the best results under several conditions. Additionally, Laakso, Kokkonen, Mellin, and Vahala (2018) and Elmasry, Hawari, and Zayed (2017) coupled different models as a part of a single framework with the idea of combining the predictive capabilities of such models in a single tool.…”

Section: Predictive Modelsmentioning

confidence: 99%

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

et al. 2021

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Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly rightskewed sewer system failure data to predict the failure risk, leveraging a bidimensional spacetime approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodnessof-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer system maintenance.

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Stochastic data mining tools for pipe blockage failure prediction

Cited by 19 publications

References 22 publications

Pipe failure modelling for water distribution networks using boosted decision trees

Pipe failure modelling for water distribution networks using boosted decision trees

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

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