{"id":840,"date":"2011-04-15T06:03:17","date_gmt":"2011-04-15T11:03:17","guid":{"rendered":"http:\/\/www.livingreliability.com\/en\/?p=840"},"modified":"2011-05-06T12:31:44","modified_gmt":"2011-05-06T17:31:44","slug":"exakt-for-complex-items","status":"publish","type":"post","link":"https:\/\/www.livingreliability.com\/en\/posts\/exakt-for-complex-items\/","title":{"rendered":"EXAKT for complex items"},"content":{"rendered":"

Abstract-Using failure and condition data for a Mill pinion bearing, EXAKT software was evaluated on how it performs prognostic modeling for complex items with multiple condition monitoring variables. It was found out that Reliability Centered Maintenance (RCM) based failure data is a pre-requisite for EXAKT, and that it requires the failure and condition data in specially formatted and structured Events and Inspection tables. Using this data EXAKT performed prognostic modeling for the bearing using Marginal analysis, modeling each significant failure mode with respect to its respective data behavior patterns, whilst incorporating all significant condition data variables. It was concluded\u00a0 that good failure prediction models depend on data adequacy, and that the main challenge to using EXAKT is in getting maintenance personnel involved in work orders to adopt the language of RCM and\u00a0 alter the way work orders are closed to include failure modes and event types. <\/em><\/p>\n

1. INTRODUCTION<\/h2>\n

Numerous papers have been written on the application of mathematical maintenance optimization models. In reference [1] whilst reviewing the application of maintenance optimization models, Dekker analyzed the role of these models in maintenance and noted then that the impact on decision making within maintenance organizations was limited. In the abstract of reference [2], Philip A. Scarf added, \u201cThis paper may be taken as an appeal to maintenance modelers to work with maintenance engineers and managers on real problems. Such collaboration is essential if maintenance modeling is to be accepted within the engineering community….\u201d.<\/p>\n

As if heeding this call, in 1995 a project was commenced at the University of Toronto to develop a Proportional Hazard Model (PHM) based software that could be used to identify optimal replacement decisions. This software was named EXAKT. Jardine et al [3] state that the overall purpose of the software is to combine historical and condition-based information to establish lowest cost replacement decisions.<\/p>\n

Tsang et al<\/em> [5], stated that in his seminal paper, Cox (1972) reported an application of PHM in the medical field using a multivariate regression analysis procedure to analyse medical survival data. and that Since 1985, PHM applications have been extended to the analysis of failure data of physical assets.<\/p>\n

In 2010, Wong et al<\/em> [4] reviewed PHM application case studies in maintenance and noted that PHM has been used in a variety of applications to determine optimal policy, for example to model aircraft engine and marine gas turbine failure and condition monitoring data, to optimize times of repair to mine haul truck wheel motors, and to model vibration monitoring data for pumps at a coal wash plant. In reference [5] Tsang gives further examples of application in the maintenance field.<\/p>\n

This being the case it makes sense to\u00a0 carry out this project, not only in support of this effort which others are making in applying prognostic modeling to maintenance, but also to add value to the colossal masses of asset health data which is being amassed.<\/p>\n

We use various condition monitoring (CM) techniques to monitor the health of roller element bearings on critical equipment such as Ball Mills. These include visual inspection, temperature, tribology and vibration monitoring. The technologies focus mainly on the acquisition of the multi-variate condition data, and its use for diagnostic purposes. However the related and yet more important question is how to add value to this mass of data. How can we use this asset health information for prognosis, to predict the remaining asset lifetime, thus optimizing our maintenance policies?<\/p>\n

Bearing this question in mind, EXAKT was assessed on its capability to perform prognostic modeling using a Ball Mill pinion bearing as a case study. We are seeking a tool which can add value to asset\u00a0 condition monitoring data, a decision making tool which can enhance the application of our Condition Based Maintenance (CBM) programmes by\u00a0 forecasting the bearings\u2019 remaining operational life, future condition, or risk to complete operation. This will not only optimize our maintenance policies, but also eliminate lost opportunity costs due to unscheduled downtime.<\/p>\n

To this end Asset health data for the pinion bearing consisting of multiple condition monitoring variables namely vibration readings, oil analysis results, temperature readings, and work order data was used to assess how EXAKT performs the prognostic modeling. A hands-on approach on the application of the EXAKT PHM process to the reliability data was followed. The main thrust was towards understanding and carrying out the data modeling and manipulation to produce the appropriate models.<\/p>\n

Section 2 summarizes the theory on which the EXAKT is based and the Proportional hazards modeling (PHM), process. The next section is a narration of the work carried out during the case study. Sections 4 and 5 are the results and analysis respectively, while section 6 is the conclusion.<\/p>\n

2. EXAKT THEORY<\/h2>\n

According to Tsang et al<\/em> [5], Proportional hazards modeling (PHM) refers to an approach to modeling an asset\u2019s hazard that takes into account information provided by condition monitoring data. It is multivariate regression analysis procedure, which aims to formally blend together data about the age of equipment along with the signals arriving from condition monitoring to estimate statistically the risk of the equipment failing at the time of inspection. In reference [6] Jardine et al<\/em> state that this method tries to account for the influence of both the physical condition indicators and the working age on the statistical probability of failure and summarize it as a function in reference [7] as follows:<\/p>\n

\"h(t)=\frac{\beta}{\eta}\left[ \frac{t}{\eta} \right]^{\beta-1}e^{\gamma_{1}Z_{1}(t)+ \dots +\gamma_{2}Z_{2}(t)}\"\u00a0\u00a0 [1]<\/p>\n

In Equation 1 h(t) is the (instantaneous) conditional probability of failure at time t, also known as the hazard function, given the values of Z1<\/sub>(t), Z2<\/sub>(t), \u2026 Zm<\/sub>(t). Each Zi(t) in represents a monitored condition data item at the time of inspection. These measurements may include the parts per million of iron or the vibration amplitude at the second harmonic of shaft rotation, or any variable that reflects the accumulated stress or damage with respect to some mode of failure. These condition data are sometimes called covariates<\/em>. The model consists of two parts, the first part is a baseline hazard function (Weibull) that takes into account the age of the equipment at time of inspection, . The second part, takes into account the key variables and their associated weights.<\/p>\n

The PHM is used to find a mathematical relationship between the risk of a component failing and the condition information together with working age.<\/p>\n

TRANSITION PROBABILITY MODEL<\/h3>\n

In reference [4], Wong assets that a transition probability model describes the changes of covariates over time, from inspection to inspection. EXAKT assumes the Markov Chain Model can be used to express this behaviour to create the Transition Probability Model (TPM). The first step to creating the TPM is to define states or covariate bands for each covariate included in the model. EXAKT automatically sets default Interval Start Points so that each covariate band contains a certain percentage of data points. Time intervals, the period during which transition probabilities are assumed to remain unchanged, are also be defined. Transition rates are calculated by EXAKT, and are the rates of changes of transition probabilities for a short period of time. It is assumed that transitions to neighbouring states (or covariate bands) in a short period are more likely than to other states. Transition probabilities are calculated from the transition rates, to produce a transition probability matrix (which shows the probability of going from one state to the next. The Transition Probability Model is needed to make predictions based on CM data. <\/strong>This, together with the PHM forms a complete statistical model that is used along with cost information to calculate the corresponding optimum component replacement strategy.<\/p>\n

DATA REQUIREMENTS FOR PHM<\/h3>\n

Failure or replacement data makes it possible to construct the PHM for specific failure modes of an asset. When the logged event is an overhaul or preventive replacement of a component, it is classified as a\u201csuspension\u201d.It also includes the date and time of occurrence of the failure. Inspection data includes the covariates (variables), and may contain more than one element, depending on the number of indicators being tracked in the condition-monitoring program that applies to the asset. Maintenance action data include data which may range from oil change, tightening of loosened parts, preventive replacement of parts and overhaul to breakdown maintenance. Start and finish dates for maintenance action are also required.<\/p>\n

Installation data includes the date and time when the asset was first put into service.<\/p>\n

In reference <\/em>[5], Tseng et al <\/em>also points out that the accuracy of the PHM model that characterizes the risk of failure of an asset depends on the quality of the data used for modelling. The quality problems of these data that often arise in practice include non existence of working age of the equipment, missing or no records on the type of maintenance action performed, transcription mistakes. and saturated (truncated) values obtained from sensors.<\/p>\n

USING EXAKT SOFTWARE<\/h3>\n

In reference [8] Wiseman summarizes the steps in applying EXAKT software to optimize CBM decisions as follows:<\/p>\n

Step 1: Preparing the data <\/strong><\/h4>\n

The maintenance data to be used in the decision analysis often contain missing and\/or erroneous data. They must be cleaned before using them as inputs of the analysis.<\/p>\n

Step 2: Building the PHM model<\/h4>\n

Parameters of the PHM model that fits the field data are estimated by the maximum likelihood estimation method. Covariates (zi) with statistically insignificant covariate parameters are removed to keep the model as simple as possible. The PHM model\u2019s accuracy improves as more failure events with their associated condition data are available.<\/p>\n

Step 3: Testing goodness of the PHM model<\/h4>\n

Residual analysis using graphical techniques and statistical tests is performed to determine how well the PHM fits the data. The larger the sample size and the more the histories ending in failure are, the more accurate the estimated model will be.<\/p>\n

<\/strong>Step 4: Defining states and determining transition probabilities<\/p>\n

Changes in the measurements of the covariates featured in the PHM model are modelled as the results of a semi-Markov process. It is then necessary to describe the state transition behaviour of the covariates through construction of the transition-probability matrix between inspection intervals. This matrix is built from the transition statistics of the condition data from one inspection to the next.<\/p>\n

Step 5: Making the optimal decision<\/h4>\n

At each inspection, as the latest condition data are available, the results of the PHM model, the transition-probability model, as well as the relative costs of failure and planned repairs are considered collectively to find a decision that will optimize the long-run maintenance cost for the component or system in question. The optimal decision policy is to determine whether the equipment should be replaced immediately, should continue operating and be inspected at the next inspection time, or should continue operating but be replaced at a specified time before the next planned inspection time.
\n<\/p>\n

3. THE CASE STUDY<\/h3>\n

Asset health data for a Ball Mill pinion bearing was used to assess how EXAKT performs prognostic modeling. The pinion bearing can be classified as a complex item. A complex item has at least two failure modes. The data included historical maintenance data comprised of multiple condition monitoring variables namely vibration readings, oil analysis results, temperature readings, and work order data raised during that period. EXAKT uses Marginal analysis for complex items.<\/p>\n

3.1. EVALUATION<\/h4>\n

A hands on approach on the application of the EXAKT PHM marginal analysis procedure on the reliability data was followed. Whilst the EXAKT PHM process was evaluated to completion, the main thrust was towards understanding and carrying out the data modeling to produce a decision model.<\/p>\n

3.1.1. DATA PREPARATION<\/h4>\n

Work order data was from our Maintenance Information Management system (EMPAC) and condition data from our Condition Monitoring Contractor databases was used. Fig.2 below shows original work order and condition monitoring data cleaned, converted to table format and ready for entry to LRCM software.<\/p>\n

\n
\n
\"\"<\/a><\/dt>\n
Fig.1: Original CMMs and CBM data<\/dd>\n<\/dl>\n<\/div>\n

Because our work order closing procedures do not include failure mode and event type, first I had to analyze the work order information to indicate the failure modes and event types. With the assistance of OMDEC, an off the shelf software package called LRCM (Living RCM) from BI-cycle was used for this and was intended to eventually transform the diverse CBM data sources to specially structured and analyzable Events table format required by EXAKT. Fig.3 below shows the establishment of failure mode \u2013work order relationships using Bicycle software. The relationships would be used as a point of reference in the modeling process to map out the Events table.<\/p>\n

In the Events table each failure mode life cycle is represented by two records namely an ending event record, and a beginning event record including information on failures, suspensions as shown in Table 1 below.<\/p>\n

However, due to project time constraints the Events table was eventually completed manually. For this purpose and integration to CMMs, the importance of using Bicycle software cannot be overemphasized.<\/p>\n

\n
\n
\"\"<\/a><\/dt>\n
Fig.3: Mapping failure modes \u2013 work order to RCM relationships for Pinion Ball Mill bearing<\/dd>\n<\/dl>\n<\/div>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Table 1: The Events table in format required in EXAKT Events_MA<\/th>\n<\/tr>\n
Ident<\/strong><\/td>\nDate<\/strong><\/td>\nWorking Age<\/strong><\/td>\nEvent<\/strong> <\/strong><\/td>\nComment<\/strong><\/td>\n<\/tr>\n
F20MB31A<\/td>\n24\/03\/1999<\/td>\n0<\/td>\nB<\/td>\nInstall new bearings<\/td>\n<\/tr>\n
F20MB31A<\/td>\n26\/09\/2001<\/td>\n13008<\/td>\nEF1<\/td>\nRepair<\/td>\n<\/tr>\n
F20MB31A<\/td>\n27\/09\/2001<\/td>\n0<\/td>\nB<\/td>\n<\/td>\n<\/tr>\n
F20MB31A<\/td>\n3\/12\/2001<\/td>\n5976<\/td>\nES<\/td>\nRoutine Maintenance<\/td>\n<\/tr>\n
F20MB31A<\/td>\n9\/04\/2003<\/td>\n0<\/td>\nB<\/td>\n<\/td>\n<\/tr>\n
F20MB31A<\/td>\n31\/12\/2003<\/td>\n42648<\/td>\nEF3<\/td>\nRepair<\/td>\n<\/tr>\n
F20MB31A<\/td>\n1\/01\/2004<\/td>\n0<\/td>\nB<\/td>\n<\/td>\n<\/tr>\n
F20MB31A<\/td>\n5\/08\/2005<\/td>\n54984<\/td>\nEF4<\/td>\nRepair<\/td>\n<\/tr>\n
F20MB31A<\/td>\n6\/08\/2005<\/td>\n0<\/td>\nB<\/td>\n<\/td>\n<\/tr>\n
F20MB31A<\/td>\n23\/05\/2006<\/td>\n61920<\/td>\nES2<\/td>\nRepair<\/td>\n<\/tr>\n
F20MB31A<\/td>\n24\/05\/2006<\/td>\n0<\/td>\nB<\/td>\n<\/td>\n<\/tr>\n
F20MB31A<\/td>\n23\/03\/2010<\/td>\n95040<\/td>\nEF<\/td>\nRepair<\/td>\n<\/tr>\n
F20MB31A_1<\/td>\n4\/12\/2001<\/td>\n0<\/td>\nB<\/td>\n<\/td>\n<\/tr>\n
F20MB31A_1<\/td>\n8\/04\/2003<\/td>\n34920<\/td>\nES1<\/td>\nRepair<\/td>\n<\/tr>\n
F20MB31A_2<\/td>\n4\/12\/2001<\/td>\n0<\/td>\nB<\/td>\n<\/td>\n<\/tr>\n
F20MB31A_2<\/td>\n8\/04\/2003<\/td>\n34920<\/td>\nEF2<\/td>\nRepair<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Once work order and condition monitoring data had been transformed to standard Events and Inspections table format, the data was ready to be applied on EXAKT to find predictive relationships between inspection (condition monitoring) data and failure.<\/p>\n

3.1.2 PHM MODELLING<\/h4>\n

A hands-on approach on the application of the EXAKT PHM marginal analysis procedure on the reliability data was followed. Whilst the modeling was evaluated to completion, the main thrust was towards understanding and carrying out the data modeling to produce a decision model.<\/p>\n

The Events and Inspections table prepared from the Ball Mill pinion bearing raw data were used as input to EXAKT to start modeling using Marginal analysis. The data is\u00a0\u00a0 associated with a single equipment number for the pinion bearing, F20MB31A, with several failure modes as picked from the work orders. Marginal analysis models each significant failure mode with respect to its respective data behavior pattern.\u00a0 \u00a0Data prep tool software was used to create specially designed Marginal Analysis databases on which Marginal analysis software procedure was then applied in steps.\u00a0 A proportional hazards model based on these data was created and statistically tested using the goodness of\u00a0 fit test. Significant condition variables were selected; covariate parameters estimated using the maximum likelihood method and transition probabilities established using a Markov Chain model to produce a full statistical PHM model. Cost parameters were entered to give a decision model.<\/p>\n

4. RESULTS AND DISCUSSION<\/h4>\n

Table 2 below shows a summary of the The K-S test (goodness of fit test). The K-S test checks whether the residuals calculated from the Weibull PHM follows a negative exponential distribution. EXAKT uses Cox-generalized residuals. If the reported p-value is large (.5 per cent), the hypothesis is that the model fits the data well and can be accepted. This model was found to be of good fit.<\/p>\n\n\n\n\n
Table 2: Goodness of fit test results<\/th>\n<\/tr>\n
\"\"<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Table 3\u00a0 below, out of the 48 condition variables only one variable,Vis40 showed any correlation with failure.(The rest were found to be of little or no value in predicting failure).Though weak, the failures of the bearing can infact be predicted using oil analysis.<\/p>\n\n\n\n\n
Table 3: Summary of estimated parameters<\/th>\n<\/tr>\n
\"\"<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The Replacement decision graph generated by EXAKT is shown in Fig. 5.The horizontal axis represents Working Age t, while the vertical axis represents the composite covariate Z (obtained from the covariate-based portion of the PHM) at t.Three separate decision areas can be seen on the graph: replace immediately (red),expect to replace (yellow) and don\u2019t replace (green). The recommendation is to intervene immediately as shown in Fig 5 below.<\/p>\n

\n
\n
\"\"<\/a><\/dt>\n
Fig. 5.EXAKT replacement decision<\/dd>\n<\/dl>\n<\/div>\n

The yellow area is small because the inspection interval is small compared to the range on the horizontal axis.The summary in Table 4 shows a benefit of 6.2% choosing the optimal policy over replacement at failure policy.<\/p>\n\n\n\n\n
Table 4.Summary of Cost Analysis<\/th>\n<\/tr>\n
\"\"<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

While the model was accepted as of \u2018good fit\u2019, the predictive capability in this particular model is difficult to see since the standard deviation is high. This was attributed to the quality of the data used, and also the fact that the events table was manually prepared, and that the author had to read between the lines to identify failure modes and event types in work orders. All these factors increased chances of errors and integrity of the data..<\/p>\n

To this end, Marginal analysis was also applied on pre-prepared Events and Inspections data sets for complex items used for demonstration purposes by OMDEC .The data is from a case study on a gearbox. The EXAKT replacement decision\u00a0 as shown in Fig.6 was not to intervene. Not shown in the graph, replacement was expected in 749.503 hrs.<\/p>\n

\n
\n
\"\"<\/a><\/dt>\n
Fig. 6.EXAKT replacement decision from pre-prepared data<\/dd>\n<\/dl>\n<\/div>\n

This result showed ample capability of EXAKT to predict failures, and also demonstrates that mathematical models by themselves do not guarantee that correct decisions will be made if the raw data do not have the required quality.<\/p>\n

ANALISIS<\/p>\n

The Marginal analysis procedure for prognostic modeling for complex items combines failure and condition data with costs to give an optimal decision for component replacement. The steps in the modeling process are straightforward and make sense. As long as properly formatted data is available, EXAKT can model each failure mode whilst incorporating all significant condition data variables .If need be, the models can be integrated to form one model for prognosis. In my opinion this is a great achievement for CBM.<\/p>\n

However biggest challenge is in getting maintenance personnel involved in work orders to adopt the clear language of RCM. Because RCM knowledge of failures is a pre-requisite for EXAKT, there is need to alter the way work orders are closed to include failure mode and Event type. For example, currently there is no RCM or FMEA knowledge base in our CMMs because maintenance personnel are unaccustomed to applying RCM concepts in their daily maintenance information related procedures. Without establishing events and failure modes in work orders it would impossible to apply EXAKT. Once these are established, LRCM links the work orders to the RCM knowledge base, and each link becomes a point in the sample to be analyzed.\u00a0 The author had to examine each work order to establish the relevant failure mode, which was then linked to the corresponding work order. This was both the most difficult and yet the most rewarding reliability improvement activity. Successful implementation of such a RCM process will pay large dividends because it will enable reliability analysis and resulting continuous improvement.<\/p>\n

Manual data preparation is too involving, and in my view most maintenance people would find it time consuming. Use of LRCM software to help in that regard couldn\u2019t have come at a better time. There is a high level of programming (script writing) and statistical understanding involved in setting up as well as in interpretation of reports. In addition, several other software packages separate from EXAKT are being used in preparing data, e.g. Talend software, LRCM software, and Data-prep Tool. This makes EXAKT not user friendly. I also encountered problems opening the database tables in Windows 7, the reason being that OMDEC have not yet configured it for Windows 7.I had to resort to using Windows Virtual PC to be able to continue with my project. The easier it becomes to use EXAKT, the more acceptable the product will become.<\/p>\n

6. CONCLUSION<\/h3>\n

Good failure prediction models depend on data adequacy and consistency. Mathematical models by themselves do not guarantee that correct decisions will be made if the raw data do not have the required quality. As long as properly formatted data is available, EXAKT can perform prognostic modeling for complex items with multiple condition data variables. This is achieved using Marginal Analysis by modeling each significant failure mode with respect to its respective data behavior patterns, whilst incorporating all significant condition data variables. The models can then be integrated to form one model for prognosis.<\/p>\n

However the challenge is in getting maintenance personnel involved in work orders to adopt the clear language of RCM. Because RCM knowledge of failures is a pre-requisite for EXAKT, there is need to alter the way work orders are closed to include failure mode and Event types. EXAKT can be useful in assisting with prognostic CBM decision making for our critical assets. There would be need to train maintenance personnel on RCM concepts and alter our work order closing procedures to include failure mode and Event types. Only then would we be able to provide reliability data of the quality and form required for analysis, modeling and processing by EXAKT. At the same time, with improvement in user friendliness, EXAKT can find wider application in maintenance and revolutionize decision making for CBM.<\/p>\n

ACKNOWLEDGEMENTS<\/h3>\n

This work was done to meet the requirements of unit MRE5008 MRE Project in the Monash University postgraduate programs in maintenance and reliability engineering. I would like to give special thanks to\u00a0 Murray Wiseman and Daming Lin both of OMDEC for not only providing software for this project, but also for the guidance on data mapping and technical assistance they offered me on\u00a0 EXAKT during the course of the project. My thanks also go to Joann Dorrepaal of Bicycle for providing the Bi-cycle software and guidance on data transformation.<\/p>\n

REFERENCES<\/h3>\n
    \n
  1. R.Dekker, Applications of maintenance optimization models: review and analysis, Erasmus University, Rotterdam, the\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Netherlands,1996<\/li>\n
  2. Philip A.Scarf,On the application of mathematical models in maintenance, Center of OR and Applied\u00a0\u00a0 Statistisc,University of Slaford,Manchester,M54WT,United Kingdom,1999.<\/li>\n
  3. Jardine, A.K.S., Joseph, T. and Banjevic, D Optimizing condition-based maintenance decision for equipment subject to vibration monitoring, Journal of Quality in Maintenance Engineering, Vol. 5 No. 3, .1999<\/li>\n
  4. E. Lorna Wong, Timothy Jefferis, Neil Montgomery, Proportional hazards modeling of engine failures in military vehicles, Journal Volume: 16 Issue: 2 2010<\/li>\n
  5. Albert H.C. Tsang, W.K. Yeung, Andrew K.S. Jardine, Bartholomew P.K. Leung, Data management for CBM optimization, : Journal of Quality in Maintenance Engineering Volume: 12 Issue: 1 2006<\/li>\n
  6. A.K.S Jardine, D.Banjevic, T.Joseph Optimizing condition-based maintenance decisions for vibration monitored bearings, Condition-Based Maintenance Laboratory, Department of Mechanical and Industrial Engineering, University of Toronto, PricewaterhouseCoopers, Toronto, Canada<\/li>\n
  7. A.K.S. Jardine, D. Banjevic, M. Wiseman, S. Buck, T. Joseph, Optimizing a Mine Haul Truck Wheel Motors\u2019 Condition Monitoring Program: Use of Proportional Hazards Modeling, Journal of Quality in Maintenance Engineering Volume: 7 Issue: 4 2001<\/li>\n
  8. Wiseman, M. \u201cOptimal condition based maintenance\u201d, in Campbell, J.D. and Jardine,A.S.K. (Eds), Maintenance Excellence: Optimizing Equipment Life-Cycle Decisions, Marcel Dekker, New York, NY, . 2001.<\/li>\n<\/ol>\n\n","protected":false},"excerpt":{"rendered":"

    Abstract-Using failure and condition data for a Mill pinion bearing, EXAKT software was evaluated on how it performs prognostic modeling for complex items with multiple condition monitoring variables. It was found out that Reliability Centered Maintenance (RCM) based failure data is a pre-requisite for EXAKT, and that it requires the failure and condition data in […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[86],"tags":[36],"class_list":["post-840","post","type-post","status-publish","format-standard","hentry","category-case-studies","tag-exakt"],"_links":{"self":[{"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/posts\/840","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/comments?post=840"}],"version-history":[{"count":0,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/posts\/840\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/media?parent=840"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/categories?post=840"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/tags?post=840"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}