Convention used:

Meaning:

X

instruction to close the current sub-window (or pane)

Detailed Explanation

Steps to follow

1

Install the EXAKT program from the Flash player user interface on the CD (or from the downloadable zip file).

EXAKT, Install Exakt, follow prompts

2

Install the data files from the CD's Flash player user interface. (Alternatively download, unzip, and place them in a folder on your hard drive. Modify the path given in step 4 and step 2 (of Section 2)accordingly.)

EXAKT, Install data files

3

Launch “EXAKT for Modelling”. This is the program for validating and analyzing condition monitoring and event data and for building the optimized CBM (condition based maintenance) model

Start, “Exakt for Modelling” (resize and arrange the EXAKTm window and this window so that you can see both).

4

Load the working model database(Transmission_WMOD.mdb).

File, Open, navigate to c:\Program Files\Exakt\tutorial1, (or where ever you installed the data files)

Transmission_WMOD.mdb, Open

5

From the EXAKT – Modelling program attach the sample measurements and events (Transmission_MES.mdb) database to the Exakt working model database. After executing the steps to the right you may examine the attachment script by again hitting Modeling, Data Set-up. You will note that it creates an ODBC (open database connectivity) link to an external database called “Transmission_MES.mdb.” and has attached a number of tables. It has applied its own internal names to two of the tables using the A=B syntax but other tables are attached directly since their names are already consistent with EXAKT’s internal names for those tables.

Click anywhere on the left window pane to activate it. Modelling (on the Menu bar), Data setup, type in the attachment script (actually it is already keyed in for you), Execute, Save

6

Notice that the attached tables have now become visible and accessible in the right tree structure of the right pane. In the next steps you will examine each one of those tables to become familiar with their content and structure, starting with Inspections. Open the Inspections table. Note the column names and content. Ident, Date, and WorkingAge are key words used by EXAKT. “Ident” is the unique name of each unit of a specific type of Item to be analyzed. An item is a significant system, subsystem, or component upon which it is convenient and desirable to conduct a reliability analysis. An item may consist of several components and may undergo several failure modes. But in this introductory section of the tutorial we will keep it simple and assume that the item is a simple item. The “Date” may be in date or date/time format. If condition monitoring inspections are more frequent than once every 24 hours, the date/time format must be used. The WorkingAge is a measure such as hours of operation, fuel consumed, thousands of feet of steel rolled, or any other measurement that reflects the accumulated usage or stress on the item. Calendar time can only be used if the units operate regularly in time – a rare situation. Databases of production records, hour meters, or counters must be used to acquire useful WorkingAge data. The remaining columns contain the condition monitoring data which we refer to as condition data.

Inspections, X

7

Now examine the Events data table. Contrasted with the Inspections table, its information represents the other side of the coin. Both Event and Inspection data are required for CBM optimization. The EXAKT modelling process is one of correlation of Events (of all kinds) and Inspections (that is, condition data). Condition data often comes from specialized databases provided by CBM product or service vendors. Common examples are oil analysis and vibration analysis. These databases are invariably well organized and consistently populated. The Events data, on the other hand, often comes from the organization’s CMMS (computerized maintenance management system) and from production databases. (The records in the CMMS, typically, have been less rigorously kept than the others. Hence EXAKT contains tools and techniques to validate and get the CMMS data into shape.) The basic required Events are: 1) Beginning (an item has been placed into service) designated by B. 2) Ending by Failure, (EF)and 3) Ending by Suspension (ES). By “suspension” we mean that the item has been taken out of service for any reason other than failure. For example, it may have been preventively replaced. Once again the Ident, Date of the Event, WorkingAge are required fields. The Event itself is recorded in the fourth column. “OC” in this example represents an “oil change” event. Any event which affects the condition data (in this case it would initialize the wear metals and contaminant elements to zero) must be included in the model.

Events, X

8

Examine the CovariatesOnEvent table. We must provide the “initialization values” for each event. Note that in this case we are initializing wear metals and contaminants to zero and additives to their new-condition levels. We may also establish calendar periods for which these initialized values to be used. (For example, the brand or grade of lubricating oil may be changed periodically.)

CovariatesOnEvent, X

9

Examine the EventsDescription table. The column P (for precedence) tells EXAKT program in which order to consider separate events that occur at the same date/time. For example, if an oil sample is drawn from an oil drain, we would wish that the sequence of the Inspection precede that of the oil change. The inspection event is implicitly given the precedence “0”.

EventsDescription, X

10

Examine the Models table. It contains no records yet. That is because you have not yet begun building a model. This table is populated automatically by EXAKT as you proceed. The only time you might access this table manually would be to delete certain sub-model(s) that you do not wish to retain. A sub-model is one of any number of models that are tested in the modelling process. The sub-model that is considered the best, is then exported to become the intelligent agent that will provide decision optimization on a particular item’s condition data.

Models, X

11

Now that we have examined the internal and external database tables we are ready to proceed with the development of a rudimentary CBM optimization model. We turn our attention to the right hand window pane containing buttons arranged in a flow chart of activities. We enter the general project data.

Data Preparation, Enter General Data, Project Title: Haul Trucks, CBM Model: Trans Oil Anal, Description: 350 T Transmission Oil Analysis, Time Unit: Hrs., OK

12

Next we instruct EXAKT to assemble the Events and Inspections into a single table C_Inspections to be used for subsequent calculations. Depending on which version of EXAKT you are using there are a number of alternative buttons we may hit. But for this exercise please choose the option similar to “Covariates – Complete”. After hitting this button two more tables will appear in the left pane, C_Events and C_Inspections.

With Covariates (Complete)

13

Examine the C_Inspections table. Note that the records of both tables (Events and Inspections) have been combined and arranged in chronological order in the single table C_Inspections. Inspection (condition monitoring) record events are designated by an *. The other event records have monitored data (covariate) values set to their initialized levels according to the CovariatesOnEvent table discussed previously.

C_Inspections, X

14

Now let’s begin the “modelling” phase of the analysis. Hit the “Modelling” button in the “Transmissions Oil Analysis(*):2 window, not the “Modelling” menu item. After executing steps A on the right, the Trans Oil Anal (ilcm) report window appears. Examine the report. The “Summary of Events and Censored Values” presents the overall summary of the data being analyzed. A “Sample Size” of 13 means that there are 13 histories or lifetimes having a beginning and some kind of ending event. Of the 13 histories 6 ended in failure, 3 (Censored (Def)) ended prior to a failure, and 4 (Censored (Temp)) units are currently in operation at the time of building this model. They are referred to in EXAKT as “temporary suspensions” and are identified automatically by the software. The next tabulation “Summary of Estimated Parameters” provides the results of our first sub-model “ilcm”. The column “Sign.” indicates whether the “Parameter” is significant – that is, whether it has been found to be statistically related to failure. The Shape (i.e. WorkingAge), Iron, and Lead are designated as significant (at this point in the analysis) while Calcium and Magnesium are not. Note that Magnesium has the highest p-Value; the p-value represents the relative probability that Magnesium has no significant impact on risk of failure. The next step is to try a different model by eliminating the lowest impact variable - magnesium. Close the window and execute steps B and C to create 2 more sub-models. Notice that we are successively removing the covariate with the highest reported p-Value. After hitting “OK” you will receive an alert warning message from EXAKT.m telling you that the procedure is over. This is normal for samples of small size (low number of histories ending in failure). You may safely ignore this message by hitting OK in the message box. Each of the reports produced from the different models may be printed (Ctl-P). The columns in the reports are explained in the Exakt Manual accessible from the Windows Start menu.

A. Modelling (button on flow diagram), Weibull PHM, Select Covariates, Submodel Name: ilcm, Iron, à, à, à, à, OK, X

B. Select Covariates, sub-model Name: ilc, Magnesium, ß, OK (in warning message), OK, X

C. Select Covariates, sub-model Name: il, Calcium, ß, OK (in warning message), OK, X

15

At this point we have a sub-model with covariates and shape parameter that are all significant. We may conclude that this, therefore, is potentially an acceptable model for failure risk prediction. To be rigorous, we should test one last possible combination – a sub-model with iron alone. (We choose Iron as it is the variable with the lowest p-value and thus is likely to have the strongest relationship to failure.)The report tells us that this is also a potentially good predictive model (i.e. iron alone is still significant). In the next step we decide which of the two sub-models should be retained and later deployed.

Select Covariates, sub-model Name: i, Lead, ß, OK, X

16

After executing the steps on the right the “PHM Parameter Estimation - Comparison” report is displayed. The “N” in the second column is telling you that the sub-model “i” is not close to the base sub-model “il”. This means that this simpler sub-model is not as good as il and that we would be losing confidence by using it rather than the more complete model “il”.

Comparative Report, Compare: il, i, à, OK, X

17

In this step we examine the results of statistical testing performed by EXAKT on the retained sub-model, il. Reactivate this model with the steps on the right. Use the menu item “Modeling”

Activate left (Transmission_WMOD.mdb) pane. Modeling (menu item), Select Current Model, Sub model: il, OK.

18

Now hit the Modeling button (not the Modeling menu item). The third table of the “PHM Goodness of Fit Test” tells us that the proportional hazards model we constructed for risk as a function of working age and the two significant covariates “fits” the data well enough for it to be used with a confidence of 95%.The test used for this is known as the Kolmogorov Smirnov test and is well accepted as a statistical tool. The test shows that the model is not rejected at the 5% significance level - i.e. it is accepted at a 95% confidence level.

Modeling, Weibull PHM, Summary Report, X

19

After executing the steps of (A) on the right we see that EXAKT has created a set of bands (listed under Interval Start Points) or “transition” states for Lead with which to build a “transition probability model”. The transition probability model calculates the probability of jumping to another state at the next inspection interval. (An example of what we mean by jumping to another state will be given below in step 20). Execute step (B) and notice the transition bands provided for Iron are quite different. This is because historical iron measurements are scattered throughout an entirely different range of values. This can be ascertained using EXAKT's cross-graph function (see user guide) Execute step (C) to close the window.

(A) Transition Probability Model, Covariate Bands, Covariate: Lead
(B) select Covariate Iron
(C) OK

20

Execute step A. Notice that the two buttons “Display Matrix” and “Display Survival” become active. Let’s examine the Display Survival function report. Set WorkingAge to, say, 8000 hours, and Observation Interval to, say 200 hours. (assuming, for example, that our asset is currently at age 8000 and we are interested in knowing its risk of failure in the next 200 hours.) The “Markov Chain Model Survival Probability matrix” report is displayed. The probabilities of Iron values jumping to another state and the probability of failure in the upcoming interval are displayed in a tabular format. (This table represents only a part of the entire set of transition probabilities taken into account by the model, since we have chosen to ignore the other significant covariate, Lead in this report. To include more than one covariate in the visual report would require the representation of multi-dimensional matrices which. Instead this report allows us to see how a single variable changes irrespective of the others.) Looking at the table we see for example that the cell "0- 4.004" and "4.004-9.009" has the entry 0.301615. This means that there is a 30.1615% probability that iron will be that state state at the next monitoring interval. Hence this report provides the probabilities of being in any state at some future time. (Of course, this report is provided for analysis purposes only while building the model. The transition probabilities are fully integrated into the final decision model that will be deployed in section 2.)

(A) Transition Rates, Display Survival, Working Age: 8000, Observation Interval: 200, Report, Close the report, close the “Display Survival Probabilities” dialog.

21

Now for the final step in developing a decision optimization model. We blend into the model the economics governing the failure and repair of this item. That is we apply the average cost of a preventive repair C and the average cost (including consequential costs) of a failure C+K. (It is rarely necessary to have great precision in these amounts for relative costs. The cost sensitivity function of EXAKT allows us to confirm this for the decision model in question. It’s usage is described in the EXAKT help file guide.) After hitting the Full Report Icon (which you'll find to the left of the Print Icon on the Tool Bar), the “Condition Based Replacement Policy – Cost Analysis report appears. Examine the “Summary of Cost Analysis” table below the Cost Function graph. It is telling you that by adhering to the interpretive decisions of the model, an optimal long run ratio of preventive to failure replacements will be 98.8:1.2 which will result in a cost savings of 75.1% relative to a replacement-only-at-failure policy. (The cost comparison reporting function similarly compares the optimal EXAKT policy with existing practice. It’s usage is described in the EXAKT help file guide.)

Decision Model, Decision Model Parameters, Replacement (C): 1200, Failure (C+K): 6000, Cost Unit: $, Inspection Interval: 250, OK, Full Report Icon  (to the left of the Print Icon), X

22

We have been, up to now, building a model based on the historical data from the entire fleet. We may now test the model on any individual unit either for the current situation (i.e. the latest data available in the database, called "LH" for last history) or we may look at any other history retroactively. The steps on the right display the reports of the latest monitored values of each unit. Four graphs are shown - one for each of the four units 17-66, 17-67, 17-77 and 17-79. By examining the four graphs we see that none are in alarm at the current moment when this snapshot of the data has been made. If the weighted sum of the significant covariates (i.e. the y-axis plotted variable) falls in the Green region, no action is necessary; in the yellow, the item should be renewed before the next monitoring interval; in the read, the item should be repaired or replaced immediately. It should be noted that these boundries vary with working age which reflects the analysis findings that working age, as well as Iron and Lead, are significnt failure risk factors. At some point in the past the values for 17-67 hit the red zone. This may indicate a spurious laboratory result that was corrected in a follow-up verification. (For modeling, known incorrect data should be removed from consideration.) Note that the x-axis scale differs from graph to graph depending on the current age of the unit.

Decisions, 17-66, shift+17-79, Report, Full Report Icon , PgDn, PgDn, PgDn, X, Last Histories dialog, X

23

The analysis and model building phase is complete. We are now ready to export the optimal decision model we created into our maintenance system environmnent (where it has access to continuously renewing data) so that it can do its job. Activate the pane on the left by clicking it. By hitting save as instructed on the right, you are sending the model to a database located on the network. But before you do so, we will, for expedience, copy the script onto the clipboard as instructed. Then hit save. You will notice that several new table links to an external database have been added to the tree in the left pane. Now that the ODBC links have been set up, we proceed to the actual export step next.

Activate left pane, ModelDbase, Connect to Database Script, key in the script for exporting the model (actually it has been keyed in for you in this sample), Save

24

After executing the steps on the right you may examine the tables DecModels, UnitToModel, DecCovariatesOnEvent, DecEventsDescription (by double clicking on the file names in the tree view of the left pane) to see just what information has been exported to the external database. Please proceed to Section II of this tutorial in order to deploy the decision model that you have just created. You may close the EXAKT Modelling (EXAKTm) program

ModelDbase, Store the Decision model, close EXAKTm

1

In this section we run the “agent” manually. (It can also be set up to run automatically). After you execute the steps on the right the user interface of the EXAKTd decision agent appears.

Start, Programs, Exakt, Exakt for Decisions

2

Execute the steps on the right, to create a working database for decisions (Transmissions_WDEC.mdb).

File, New, Navigate to c:\Program Files\Exakt\data, (or where ever the data files are), Transmission_WDEC.mdb, Create

3

Now we will link (via ODBC) to the database where we previously exported our model (Step 24 of Section I.). After executing the steps on the right you will see the name of the Model you created, “Trans Oil Anal” in the top left pane.

Setup, Connect to model database script, copy and paste this script:

DATABASE="Transmission_DMDR.mdb";

ATTACH DecModels,

     UnitToModel,

     DecCovariatesOnEvent,

     DecEventsDescription,

     Decisions

hit Save

4

After executing this step, you will see each of the units whose optimal decisions for oil analysis will be governed by this model. (new units may be added easily in the EXAKTd program.)

Expand “Trans Oil Anal”

5

By selecting any unit in the top left pane, we see a list of properties but no values. We will next run the agent manually on the latest available set of condition monitoring oil analysis data.

17-66

6

Now you will re-select the Model “Trans Oil Anal” and execute the decision agent by following the steps on the right.

Trans Oil Anal, Reports, Create reports, Calculate time to replace

7

The results of the entire fleet have been analyzed and decisions have been returned for each unit. You may examine the reports of each fleet member by following the steps on the right.

Full Report icon , expand report window, scroll, X

8

With “Trans Oil Anal” selected you can conveniently examine the optimal decisions for the entire fleet on one list in the right window. You are actually examining the contents of the Decisions table of the Transmissions_DMDR.mdb database. This database can be accessed easily by any program, such as your CMMS. This implies that the decision model’s operation and its results may be integrated within existing maintenance system software. In other words, the EXAKTd program need not be used at all. However, it does have a very convenient user interface and several useful functions, some of which are described in the following steps.

Reports, Create new report list, New Report List Name: Indoor trucks, OK Reports, Create new report list, New Report List Name: Outdoor trucks, OK Trans Oil Anal, Select 17-66 + 17-67, ctrl-c, Indoor Trucks, ctrl-v, Trans Oil Anal, Select 17-77 + 17-79, ctrl-c, Outdoor Trucks, ctrl-v

9

Now we will use the new report lists to help manage our trucks by department.

Select Indoor Trucks, Reports, Create Reports, Calculate time to replace Select Outdoor Trucks, Reports, Create Reports, Calculate time to replace

10

This completes this section of the Tutorial. This has been a minimal exercise to demonstrate a small portion of the EXAKT functionality. Please refer to the On-line guide (available on your Start | Programs | EXAKT menu) for a much more detailed treatment of the subject of CBM optimization.