scholarly journals Prediction of Failure Frequency of Water-Pipe Network in the Selected City

2017 ◽  
Author(s):  
Małgorzata Kutyłowska
Keyword(s):  
Failure Rate ◽  
Failure Frequency ◽  
Input Signals ◽  
Water Mains ◽  
Output Neurons ◽  
Predicted Values ◽  
Hidden Layer ◽  
Water Meters ◽  
Quasi Newton ◽  
Operational Data

The paper presents the modelling results of failure rate of watermains, distribution pipes and house connections in one Polishcity. The prediction of failure frequency was performed usingartificial neural networks. Multilayer perceptron was chosen asthe most suitable for modelling purposes. Neural network architecturecontained 11 input signals (sale, production, consumptionand losses of water, number of water-meters, length andnumber of failures of water mains, distribution pipes and houseconnections). Three neurons (failure rates of three conduitstypes) were put to the output layer. One hidden layer, with hiddenneurons in the range 1-22, was used. Operating data fromyears 2005-2011 were used for training the network. Optimalmodel was verified using operational data from 2012. ModelMLP 11-10-3 was chosen as the best one for failure rate prediction.In this model hidden and output neurons were activatedby exponential function and the learning was done using quasi-Newton approach. During the learning process the correlation(R) and determination (R2) coefficients for water mains, distributionpipes and house connections equaled to 0.9921, 0.9842;0.8685, 0.7543 and 0.9945, 0.9891, respectively. The convergencesbetween real and predicted values seem to be, from engineeringpoint of view, satisfactory.

scholarly journals Neural Network Approach for Availability Indicator Prediction

2017 ◽  
Author(s):  
Małgorzata Kutyłowska
Keyword(s):  
Training Methods ◽  
Neural Network Approach ◽  
Failure Frequency ◽  
Promising Tool ◽  
Water Mains ◽  
Output Neurons ◽  
Experimental Values ◽  
Hidden Neurons ◽  
Quasi Newton ◽  
And Training

The principal aim of this research was to find out if artificial neuralnetworks could be employed to predict the availability factorfor water mains, distribution pipes and house connections.Modelling by means of artificial neural networks (ANNs) wascarried out using the Statistica 10.0 software package. Operatingdata from the years 1999–2005 were used to train the ANNswhile data from the next seven years of operation were usedto verify the model. The optimal model (characterized by thelowest mean-square error) contained 11 hidden neurons activatedby the exponential function. The linear function was usedto activate the 3 output neurons. 185 training epochs sufficed totrain the ANN, using the quasi-Newton method. The correlationbetween the availability indicator experimental values and themodelling results would remain high, amounting during modelverification to R2 = 0.740, R2 = 0.823, R2 = 0.992 for respectivelywater mains, distribution pipes and house connections. As theavailability indicator prediction example shows, the artificialneural networks are a promising tool enabling quick and easyanalysis of failure frequency. It is possible to train the ANN furtherand change the number of training epochs and hidden neuronsas well as the activation functions and training methods.

scholarly journals MARSplines method as a tool for failure frequency modelling

2018 ◽  
Vol 44 ◽  
pp. 00086
Author(s):  
Małgorzata Kutyłowska
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Failure Rate ◽  
Basis Function ◽  
Adaptive Algorithm ◽  
Point Of View ◽  
Water Utility ◽  
Failure Frequency ◽  
Rate Prediction ◽  
Operational Data

The paper presents the results of failure rate prediction using adaptive algorithm MARSplines. This method could be defined as segmental and multiple linear regression. The range of segments defines the range of applicability of that methodology. On the basis of operational data received from Water Utility two separate models were created for distribution pipes and house connections. The calculations were carried out in the programme Statistica 13.1. Maximal number of basis function was equalled to 30; so-called pruning was used. Interaction level equalled to 1, the penalty for adding basis function amounted to 2, and the threshold – 0.0005. GCV error equalled to 0.0018 and 0.0253 as well as 0.0738 and 0.1058 for distribution pipes and house connections in learning and prognosis process, respectively. The prediction results in validation step were not satisfactory in relation to distribution pipes, because constant value of failure rate was observed. Concerning house connections, the forecasting was slightly better, but still the overestimation seems to be unacceptable from engineering point of view.

scholarly journals Application of MARSplines Method for Failure Rate Prediction

2018 ◽  
Author(s):  
Małgorzata Kutyłowska
Keyword(s):  
Failure Rate ◽  
Basis Function ◽  
Water Pipes ◽  
Independent Variables ◽  
Rate Prediction ◽  
Final Note ◽  
Dependent Variables ◽  
Water Mains ◽  
Predicted Values ◽  
Model Failure

In this paper MARSplines method was presented to model failure rate of water pipes in years 2015-2016 in the selected Polish city. The output parameters were chosen as three dependent variables - three values of failure rate of water mains, distribution pipes and house connections. Diameter, season, material and kind of the conduit were selected as independent variables. At the beginning of modelling 21 basis (splines) function were assumed. On a final note two functions were selected (after reduction of negligible functions). The model consists of three factors: β0, β1 and β2. The penalty for adding basis function was assumed at the level of 2. The correlation was equalled to 0.44. Relatively huge discrepancies between real and predicted values of failure rate of water mains and house connections were observed. In the future investigations concerning this problem the three separated models for each kind of conduit should be created. The calculations using MARSplines method were carried out in the program Statistica 13.1.

scholarly journals Application of K-nearest neighbours method for water pipes failure frequency assessment

2018 ◽  
Vol 59 ◽  
pp. 00021
Author(s):  
Małgorzata Kutyłowska
Keyword(s):  
Failure Rate ◽  
Euclidean Distance ◽  
Optimal Model ◽  
Water Utility ◽  
Water Pipes ◽  
Failure Frequency ◽  
Regression Methods ◽  
Machine Learning Methods ◽  
Nearest Neighbours ◽  
Operational Data

The paper describes the results of failure rate modeling using K-nearest neighbours method (KNN). This algorithm is one among other regression methods, called machine learning methods. The aim of the presented paper was to check the possibilities of application of such kind of modelling and the comparison between current results and investigations of failure rate prediction in another Polish city. Operational data from 12 years of exploitation, received from water utility, were used to predict dependent variable (failure rate). Data (249 and 294 for distribution pipes and house connections, respectively) from the time span 2001–2012 were used for creating the KNN models. On the basis of other data (one case for each year) the validation of optimal model, based on Euclidean distance metric with the number of nearest neighbours K = 2, was carried out. The realization of the modelling was performed in the software program Statistica 12.0.

An Update to the UKOPA Pipeline Damage Distributions

2012 ◽  
Author(s):  
Graham Goodfellow ◽  
Susannah Turner ◽  
Jane Haswell ◽  
Richard Espiner
Keyword(s):  
Failure Rate ◽  
Structural Reliability ◽  
Operating Experience ◽  
Quantitative Risk Assessment ◽  
External Interference ◽  
Failure Frequency ◽  
Product Loss ◽  
Distribution Parameters ◽  
Transmission Pipeline ◽  
Incident Rates

The United Kingdom Onshore Pipeline Operators Association (UKOPA) was formed by UK pipeline operators to provide a common forum for representing operators interests in the safe management of pipelines. This includes providing historical failure statistics for use in pipeline quantitative risk assessment and UKOPA maintain a database to record this data. The UKOPA database holds data on product loss failures of UK major accident hazard pipelines from 1962 onwards and currently has a total length of 22,370 km of pipelines reporting. Overall exposure from 1952 to 2010 is of over 785,000 km years of operating experience with a total of 184 product loss incidents during this period. The low number of failures means that the historical failure rate for pipelines of some specific diameters, wall thicknesses and material grades is zero or statistically insignificant. It is unreasonable to assume that the failure rate for these pipelines is actually zero. However, unlike the European Gas Incident data Group (EGIG) database, which also includes the UK gas transmission pipeline data, the UKOPA database contains extensive data on measured part wall damage that did not cause product loss. The data on damage to pipelines caused by external interference can be assessed to derive statistical distribution parameters describing the expected gouge length, gouge depth and dent depth resulting from an incident. Overall 3rd party interference incident rates for different class locations can also be determined. These distributions and incident rates can be used in structural reliability based techniques to predict the failure frequency due to 3rd party damage for a given set of pipeline parameters. The UKOPA recommended methodology for the assessment of pipeline failure frequency due to 3rd party damage is implemented in the FFREQ software. The distributions of 3rd party damage currently used in FFREQ date from the mid-1990s. This paper describes the work involved in updating the analysis of the damage database and presents the updated distribution parameters. A comparison of predictions using the old and new distributions is also presented.

scholarly journals Analyzing the reliability of rolling stock units based on operational data

2021 ◽  
Vol 2021 (2) ◽  
pp. 251-260
Author(s):  
Aleksey E. TSAPLIN ◽  
◽  
Zh. O. Kuvondikov ◽  
Keyword(s):  
Failure Rate ◽  
Statistical Data ◽  
Practical Importance ◽  
Reliability Theory ◽  
Rolling Stock ◽  
Reliability Indicators ◽  
Equipment Reliability ◽  
Reliability Characteristics ◽  
Operational Data

Objective: To determine the most failure-prone rolling stock components and assemblies by processing statistical data obtained during operation using the classical reliability theory; to develop recommendations for maintaining the operational state of individual rolling stock components. Methods: Methods for calculating the quantitative reliability characteristics are used based on the rolling stock operational statistical data. Results: The 5-year operational data have been used to provide tabulated statistics on the failure rate of various rolling stock equipment. Reliability indicators have been calculated for various types of rolling stock equipment and the corresponding graphs have been plotted. Based on the calculations, the recommendations for the rolling stock maintenance have been developed. Practical importance: The calculations and the recommendations described determine the types of rolling stock equipment requiring more attention during maintenance

Bayesian Analysis of Piping Leak Frequency Using OECD/NEA Data

2010 ◽  
Vol 132 (7) ◽  
Author(s):  
Min Wang ◽  
Mahesh D. Pandey ◽  
Jovica R. Riznic
Keyword(s):  
Failure Rate ◽  
Nuclear Power ◽  
The United States ◽  
Bayesian Procedure ◽  
Two Stage ◽  
Energy Agency ◽  
Failure Frequency ◽  
Nuclear Plants ◽  
Consistent Manner ◽  
Construction Operation

The estimation of piping failure frequency is an important task to support the probabilistic risk analysis and risk-informed in-service inspection of nuclear power plant systems. This paper describes a hierarchical or two-stage Poisson-gamma Bayesian procedure and applies this to estimate the failure frequency using the Organization for Economic Co-operation and Development/Nuclear Energy Agency pipe leakage data for the United States nuclear plants. In the first stage, a generic distribution of failure rate is developed based on the failure observations from a group of similar plants. This distribution represents the interplant (plant-to-plant) variability arising from differences in construction, operation, and maintenance conditions. In the second stage, the generic prior obtained from the first stage is updated by using the data specific to a particular plant, and thus a posterior distribution of plan specific failure rate is derived. The two-stage Bayesian procedure is able to incorporate different levels of variability in a more consistent manner.

scholarly journals Artificial Neural Network (ANN) Analysis of Co-pyrolysis of Waste Coconut Husk and Laminated Plastic Packaging

2021 ◽  
Vol 21 (2) ◽  
pp. 241
Author(s):  
Joselito Abierta Olalo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Techniques ◽  
Fuel Oil ◽  
Oil Yield ◽  
Coconut Husk ◽  
Input Layer ◽  
Artificial Neural ◽  
Predicted Values ◽  
Hidden Layer

Co-pyrolysis of plastic with biomass was used in the possible mitigation of environmental health problems associated with plastic waste. The pyrolysis method possessed the highest solution in the reduction of waste problems. Fuel oil can be produced through the pyrolysis of plastic and biomass waste. Many researchers used pyrolysis technology to produce a suitable amount of pyrolytic oil through different optimization techniques. This study will predict the percentage mass oil yield using an artificial neural network. It uses an input layer, hidden layer and an output layer. Three input factors for the input layer were (i) temperature, (ii) particle size, and (iii) percentage coconut husk. The structure has one hidden layer with two neurons. The artificial neural network was designed to predict the percentage oil yield after 15 pyrolysis runs set by the Box-Behnken design of the experiment. Percentage oil yields after pyrolysis were calculated. Results showed that temperature and percentage of coconut husk significantly influenced the percentage oil yield. Predicted values from simulation in the artificial neural network showed a good agreement through a correlation coefficient of 99.5%. The actual percentage oil yield overlaps the predicted values, which ANN demonstrates as a viable solution.

scholarly journals Analysis and assessment of water distribution subsystem failure

2016 ◽  
Vol 40 (1) ◽  
pp. 47-62
Author(s):  
Katarzyna Pietrucha-Urbanik ◽  
Katarzyna Pociask
Keyword(s):  
Water Supply ◽  
Failure Rate ◽  
Water Distribution ◽  
Material Structure ◽  
Water Supply Systems ◽  
Water Network ◽  
Network Failure ◽  
Failure Frequency ◽  
Key Indicators ◽  
Supply Systems

Abstract The aim of the study is to analyse and assess the water supply network failure frequency in last ten years, with particular emphasis on the last year of the analysis. The analysis is based on actual data obtained from the water company. The study contains the analysis of the network failure with division into used material, the type of network and place of failure occurrence. Also the failure rate was calculated. Continuous changes in the age and material structure of the water network cause the need for conducting failure frequency research because data on failure rate are the key indicators for operational policy of water supply systems.

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