J-6                                                        2012 IEEE International Conference on Condition Monitoring and ...
predictions. The combination of sufficient data and appropriate              III.                        ESTABLISHING FAIL...
Besides failure data, additional data regarding the un-failed                                 reached ages higher than 20 ...
population no exact installation year was specified in the                               pin-pointed condition monitoring ...
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Statistical Approach to Establish Failure Behaviour on Incomplete Asset Lifetime Data

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Abstract—Asset failures, that needs to be managed, has an uncertain characteristic and analysis of uncertainty is essential to Asset Management (AM). Forecasting the technical performance of assets forms an integral part of strategic and operational activities within AM. To establish the failure behaviour of assets requires a significant degree of reliable asset information, which, in many practical cases, is not sufficiently rich or available to provide a basis for straightforward decision-making. In this paper a practical and systematic statistical methodology is used for dealing with incomplete asset lifetime data. The method described in this paper is based on a statistical parametric method and is applied with the aim of obtaining an indicator of the future failure expectancy with a certain confidence interval. On the whole, the paper concludes that, even though input data was either missing or incomplete, it is in certain cases possible to develop sensible probability models. These models take into account uncertainty and ultimately can be applied to facilitate the asset manager in AM decision-making. In addition to applying statistical methods, this contribution highlights the vital role of engineering and expert knowledge in interpreting the statistical results.

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Statistical Approach to Establish Failure Behaviour on Incomplete Asset Lifetime Data

  1. 1. J-6 2012 IEEE International Conference on Condition Monitoring and Diagnosis 23-27 September 2012, Bali, Indonesia Statistical Approach to Establish Failure Behaviour on Incomplete Asset Lifetime Data Ravish P.Y. Mehairjan, Arjan M. van Voorden Dhiradj Djairam, Qikai Zhuang, Johan J. Smit Asset Management High Voltage Technology & Asset Management Stedin B.V. Delft University of Technology Rotterdam, the Netherlands Delft, the Netherlands r.p.y.mehairjan@tudelft.nl/ ravish.mehairjan@stedin.netAbstract—Asset failures, that needs to be managed, has an due to many mergers and acquisitions of smaller regionaluncertain characteristic and analysis of uncertainty is essential to utilities into larger consolidated utilities, intellectual propertiesAsset Management (AM). Forecasting the technical performance often was lost. Correspondingly, AM is a fairly new conceptof assets forms an integral part of strategic and operational for the electric power sector. Hence, in the earlier days, manyactivities within AM. To establish the failure behaviour of assets utilities did not see reasons for collecting detailed informationrequires a significant degree of reliable asset information, which, to track equipment lifetimes [3]. Nevertheless, with the AMin many practical cases, is not sufficiently rich or available to framework heavily relying on asset level data to support soundprovide a basis for straightforward decision-making. In this AM decisions [4], a strong demand for methods and tools waspaper a practical and systematic statistical methodology is used brought forth. These methods and tools should be able tofor dealing with incomplete asset lifetime data. The methoddescribed in this paper is based on a statistical parametric analyse equipment lifetime data even in the often occurringmethod and is applied with the aim of obtaining an indicator of case of incomplete or inconsistent data. In section II , athe future failure expectancy with a certain confidence interval. systematic statistical approach is described, followed by theOn the whole, the paper concludes that, even though input data application of this approach to a practical case of incompletewas either missing or incomplete, it is in certain cases possible to asset data for distribution cable assets. In section III, two casedevelop sensible probability models. These models take into studies are presented. The former discusses a sensitivityaccount uncertainty and ultimately can be applied to facilitate analysis used for the presence of a suspect asset group in athe asset manager in AM decision-making. In addition to certain failure dataset. The latter delves into a case of dealingapplying statistical methods, this contribution highlights the vital with incomplete data, in which expert judgements played arole of engineering and expert knowledge in interpreting the crucial role.statistical results. II. STATISTICAL LIFE DATA ANALYSIS Keywords-asset management; failure rate; statistical life dataanalysis. A. Parametric Distribution Fitting Method Reference [5] described the application of statistical I. INTRODUCTION analysis in AM decision processes. Likewise, the parametric Supported by the publicly available specification method [6], [7], which uses mathematical assumptions to fit,BSI:PAS55, and the forthcoming ISO55000/1/2 Standards, hypothesized probability of failure distributions to the data, isthe discipline of AM is progressively emerging as a framework used. This method comprises a number of steps and thisfor competent asset intensive organizations [1]. In this context, straightforward procedure is depicted in figure 1.forecasting asset failure behaviour, based on reliabilityengineering, is connected to aging asset strategies and isguiding operations and maintenance decision-making. The keyto making good AM decisions is acquiring appropriate assetknowledge. Consequently, in the past years, electric utilitiesand industries have progressively created databases to recordasset or business data, such as failure, maintenance, operationand cost [2]. However, throughout electric utilities, andprobably in other industries as well, it is commonlyencountered that asset managers raise the matter of lack ofsufficient information for making good decisions. There aremany reasons for this shortcoming. The majority of assets in Figure 1: Evaluation flowchart for life data analysis, with emphasis on thepower systems are aged and at that specific time in history parametric method, which is applied in this study.there were no information recording systems available to B. Data Typesrecord asset information. In cases where such informationsystems were gradually becoming available, it occurred that Statistical failure distribution models rely extensively ondue to system modifications, information was lost. Moreover, the data, life data or time-to-failure of a component, to make This research has been performed in close collaboration with Stedin (aDutch Distribution Network Operator, DNO). 978-1-4673-1018-5/12/$31.00 ©2012 IEEE 517
  2. 2. predictions. The combination of sufficient data and appropriate III. ESTABLISHING FAILURE FORECASTS WITH INCOMPLETEstatistical model choice, will usually result in acceptable ASSET LIFE TIME DATApredictions. In life data analysis a distinction can be madebetween failure data (failed unit) and suspended data (un- A. Medium Voltage (MV) Distribution Network [8]failed unit). Furthermore, the collected life data for statistical At Stedin, the third largest Dutch Distribution Networkanalysis purpose should have the following properties [7]: Operator (DNO), MV cable joints contribute to a vast majority of distribution grid outage times (45%). With the goal to - Randomness predict the technical performance of this asset group, an - Independency investigation for the application of statistical life data analysis - Homogeneity was carried out for a particular region of 10 kV distribution - Sufficient amount of data network of the utility [8].In the analysis of life data, it is deemed advisable to use allavailable data. In practise, however, it is challenging, B. Available Dataexpensive and sometimes impossible to collect all required life Paper-based outage data recording started, partly, arounddata. Consequently, the available life data, at utilities, is 1976 in the Netherlands, followed by a database collection toolincomplete or include uncertainties (censored data), as to when in 1991 named “KEMA Nestor”. This failure reportingexactly a component failed or was installed. database has developed throughout the years and has been improved continuously. At the time that this case study wasC. Failure Distribution Fitting/ Parameter Estimation performed, the available MV failure data for the period 2004 Failure probability distributions are mathematical Time Windowequations allowing a large amount of information, Introduction of Kema Failure Data Available Nestor databasecharacteristics and behaviours to be described by a smallnumber of parameters. In general, a certain failure distributionfor an asset population is chosen based on one or more of the ~ 1976 ~ 1991 2004 2009 Failure Data Unavailablefollowing considerations: Paper matter data collection New network components New voltage levels - The dominant failure mechanism satisfies most or all New way of data collection Many utility merges assumptions which underlie a certain statistical New data definition distribution until 2009 had been consistent and could be used in a viable - The choice is limited to the failure distribution that way. The development of failure data recording is shown in best fits the life time data figure 2. - A simple distribution, which is well suitable for Figure 2: Timeline showing the availability of failure data in distribution analytical computations. network for this study. This time window reflects the period where failure dataOften used statistical functions, which describe the failure is available. In between, failure data is often missing or incomplete.distribution, are the probability density function (pdf), the The analysis takes into account 556 cable joint failures, withincumulative distribution function (cdf), the reliability function the last 6 years.(R) and the failure rate functions (λ). These functions containall information about the failure process of the assets under Number of reported 10 kV cable joint failuresconsideration. Frequently used failure distributions for Synthetic Joint Mass-Insulated Joint Oil-Insulated Joint 250(continuous) life data analysis are normal, Weibull, # of joint failures 200exponential and Gumbel distribution. After a certain failuredistribution is selected to fit the data, the next step is to 150estimate the parameters of this distribution. Three, often 100applied, methods are; Probability Plotting (PP), Least Squares 50Estimation (LSE) and Maximum Likelihood Estimation 0(MLE). <1 [1-5] [5-10] [10-20] [20-40] > 40 Age Bins (years)D. Maximum Likelihood Estimation (MLE) In this contribution, the MLE method is applied, as this Figure 3: 10 kV joint failure records for the period 2004-2009 for three categories of cable joints. As result of unknown exact age at the moment ofmethod has the ability to take into account large data sets and failure of a component, age intervals are used to estimate the age of the failedlarge quantities of suspended data points, which is common components.for electric network components. By maximising the value ofthe likelihood function (L), which is a statistical expression of Most of the time, the exact age of the cable joints at the moment of failure is unknown to the utility. To circumvent thisthe probability of the parameter, the most likely parameter for problem, estimated age intervals for the reported failures arethe given data set is estimated. used, as shown in figure 3, for three categories of cable joints namely; synthetic insulated, mass-insulated and oil-insulated cable joints. 518
  3. 3. Besides failure data, additional data regarding the un-failed reached ages higher than 20 years. Two scenarios were assets are also considered. The total recorded population of all analysed, in which failure data points were removed as follows: three types of cable joints is roughly 31700 pieces. Firstly, for large portions of the joint population the exact age (year of - All failures from age bin [>40] year and 10 failures installation) is not specified or unknown. Such records are from age bin [20-40] year often missing for assets that were installed more than 20 to 30 - All failures from age bin [>40] year and 20 failures years ago. Secondly, for some parts of the cable joint from age bin [20-40] year. population the corresponding joint type is unknown. The first The calculated failure rates, according to the best fit failure shortcoming is dealt with by dividing the number of joints distribution (Weibull), are shown in figure 5. without age, proportionally, and adding these joints to the joints installed in particular years (conceptually shown in figure 4a). A formula related to this procedure is:  ( ) The second shortcoming is dealt with by using information, based on expert knowledge, regarding the historic application of certain joint types. These experts still have knowledge regarding the history of when a certain type of joint was taken into operation (conceptually illustrated in figure 4b). (a) (b) [25 - 50]Number of components Number of components Figure 5: This figure shows the failure rate plots for three subsets of life data for synthetic insulated cable joints. The blue failure rate plot represents the original data record, while the black and green failure plot represent scenario 1 Population Age Population Age and 2, respectively. Figure 4: Simplified impression for the estimation methods which are applied to incorporate the missing data (missing asset installation year). From figure 5, it can be found that the failure rates are As a result, it was possible to make rough estimations of the considerably lower for the synthetic joints when the suspect missing records and incorporate these in the statistical analysis. “Nekaldiet” failure records are excluded from the statistical The systematic approach, which is depicted in figure 1, is used analysis. Therefore, we may reasonably conclude that the for modelling the life data of the three different 10 kV cable suspect “Nekaldiet” failure records negatively impact the joints populations. overall failure behaviour of the synthetic insulated joint population. More specifically, the asset manager can justify, C. Statistical Analysis: Example 1[8] based on these results, that replacing aged “Nekaldiet” cable For the case of synthetic insulated cable joints, experts at the joints, or applying condition monitoring to cable feeders with DNO indicated that the cable joint failures, which are reported these types of joints, can be a feasible strategy to mitigate in the age intervals [20-40] and [>40] years (see figure 3) are future failures. probably failures of 10 kV resin joints that were installed in the D. Statistical Analysis: Example 2[8] 1970’s. These resin joints, often referred as “Nekaldiet” joints, have resulted significantly to outages in the past years, With the developed failure rate models and the number of however, are not applied anymore and replaced as much as components still in operation, the asset manager can obtain an possible. Consequently, a sensitivity analysis was performed, indication of the future failure expectancy. To assess whether using the calculated failure rates, to assess the failure behaviour the predicted number of failures reasonably describe the of synthetic cable joints without the suspect “Nekaldiet” failure behaviour, it was decided to perform a validation test. failures. For this purpose, it was required to exclude certain By comparing the actual recorded number of failures for the failure as well as appropriate in-service data records. After period 2004-2009 with the predicted number of failures for the consulting experts at the utility, it was agreed to exclude all same period, it is assessed whether the developed failure rate failures which were recorded in the age bin [>40] years and a models reasonably describe the failure behaviour of the number of failures from the age bin [20-40] years. Likewise, considered population (validation test). For two joint the in-service data was adjusted. These considerations were populations (synthetic and oil) the validation test suggests to based on the viewpoint that “Nekaldiet” joints were installed a be in accordance with the actual occurred failures. However, few decades ago and ,therefore it was very likely that this for the mass-insulation cable joint population this was not the group of synthetic joints had operated sufficiently to have case. It is worth noting, that for almost 60% of the mass joint 519
  4. 4. population no exact installation year was specified in the pin-pointed condition monitoring is necessary in the comingdatabase. These incomplete datasets were taken into account years, as part of the AM strategic and operational policies.as described in section B (figure 4a). In order to assesswhether this first estimation, regarding the 60%, might be an IV. CONCLUSIONSimproper estimation, a number of new estimations were Inherently, asset failure is a source of uncertainty in AM [9],examined. In a second attempt, the 60% of data was not while asset managers seek to manage this uncertain behaviour,divided proportionally, but according to a certain age interval, the quest for tools and methods to analyse these are required.as shown in figure 4b. The main reason behind this second This paper describes a rigorous statistical life data analysisattempt was based on experts’ opinions, who indicated that methodology, which can be used for assessing and predictingmass-insulated joints were mostly used a few decades ago. the technical performance of assets. From the first example, weThus, it was likely that the missing 60% data should be of a found that with the failure probability models, technicalpopulation which is older than roughly 20 years. Therefore, reliability assessments can be carried out for suspect group ofthis 60% was proportionally divided in various age intervals, assets within a population. On top of this, forecasting the technical performance of assets is one of the mainsatisfying this assumption. Different scenarios were used responsibilities of the asset manager. With the developednamely; age intervals of [20-30], [20-40], [25-50], etc. The failure rate models for each population and the number ofexpected future failure outcomes for the interval [25-50] years components in operation, the asset manager can anticipate thewere most in accordance with the actual occurred failures in development of future cable joint failures. On that account, thethe period 2004-2009. In figure 6, two scenarios (black and management of the utility has applied the results from thisblue plot) are illustrated together with the actual recorded investigation to justify the need for increased capitalnumber of failures (red plot). expenditures (CAPEX) towards MV distribution cable assets. From the second presented example, it is found that by choosing appropriate statistical models and in-depth engineering and expert reasoning it is possible to create valuable information on the failure behaviour of asset populations, even in case of uncertain or missing data. Altogether, we can conclude that, even though data was either missing or incomplete, it is still possible to develop sensible probability methods in order to provide the asset manager with useful information to understanding the (uncertain) failure behaviour of assets and support AM decision making. ACKNOWLEDGMENT The authors would like to thank Stedin B.V. for their support, knowledge and access to data. REFERENCESFigure 6: This figure shows the failure prediction for the mass insulated joints [1] The Institute of Asset Management (IAM), “Asset Management- antogether with the corresponding 90% confidence intervals. The blue plot anatomy”, Issue 1, dec 2011.represents the first case (60% of missing data is estimated proportionally), [2] EPRI, “Guidelines for Intelligent Asset Replacement: Undergroundwhile the black plot indicates the second case (60% of missing data is estimated Distribution Cables”, EPRI, Palo Alto, CA:2005.1010740.using specific intervals, based on expert judgement). [3] R.P.Y. Mehairjan, D. Djairam, Q. Zhuang, J.J. Smit, A.M. van Voorden,Under these circumstances, it can be concluded that based on “Statistical Life data Analsyis for Electricity Distribution cable Assets – An Asset Management Appraoch”, IET International Asset Managementthe analysis, it seems probable that the population of mass- Conference London, dec 2011.insulated joints without recorded installation year (60% of the [4] CIGRE WG D1.17, “Generic Guidelines for Life Timo Conditionpopulation) might be older than 25 year. However, it should Assessment of HV Assets and Related Knowledge Rules”, CIGRE,be noted, that these assumptions are based on the available 2010.data at the moment of the study. Another way of reasoning [5] R.A. Jongen, J.J. Smit, A.L.J. Janssen,”Application of Statisticalmight reveal that there have been more failures of mass- Analysis in the Asset Management Decision Process”, International Conference on Condition Monitoring and Diagnosis, 2008.insulated joints in the past, of which the records are missing, [6] R.A. Jongen, “Statistical Lifetime Management of Energy Networkand therefore the failure rates obtained here could be Components”, Ph.D Dissertation, Delft University of Technology, theconservative values. Whether the mass-insulated joint Netherlands, 2012.population is of an older age category or the number of [7] Reliasoft Corporation, “Life Data Analysis (Weibull Analysis)failures in the past are higher, in either case, the asset manager Reference Book”, Reliasoft.now has more knowledge on the failure behaviour of the [8] R.P.Y. Mehairjan, “Application of Statistical Life Data Analysis for Cable Joints in MV Distribution Networks – An Asset Managementmass-insulation joint population. With this information, the Approach”, MSc Thesis Report, Delft University of Technology, theasset manager can determine if the expected number of future Netherlands, 2010.failures are acceptable or whether structured replacement or [9] C.D. Feinstein, P.A. Morris, ”The Role of Uncertainty in Asset Management”, IEEE Transactions, 978-1-4244-6547-7/10, 2010 520

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