Chapter 3: DA Case Studies

This chapter is dedicated to present some examples of using R4All platform to perform a Degradation Analysis.

3.1 Case Study 1: Life Cycle of a Lubricant

A pulp and paper company decided to improve the reliability of the pumps installed in its industrial plant. Knowing that the most common failure modes are all related to impurities in the lubricant, they decided to perform a degradation analysis of the lubricant properties. As the concentration of solid particles increases with the operation time, the lubricant film tends to be less effective; therefore, if the lubricant is not refilled, the failure is eminent.

The inspectors’ feedback reports point that the lubricant loses its properties when it reaches 0.05 milligrams per liter.

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As this analysis results returned small numbers, ​​it was necessary to go to settings (Settings DA) and change the mathematical precision to 8 decimal places. In the Analysis Results area, the mathematical model parameters for each item are presented.

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On the Plot tab, the degradation curve for each item is presented based on the mathematical model chosen.

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On the Plot tab, the degradation analysis results include the degradation models parameters, the estimation of the times to failure for each item and the degradation plot.

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The cost for changing the oil is $70, and the pump profit loss due to the time needed to change its lubricant is $1,000 (considering an average of 30 minutes stop to change the lubricant). Also, considering if the oil is not changed, the pumps will fail at a certain time of operation and it will need to be replaced by a new one. A new pump costs $53,000, considering profit loss of install time and the asset cost, as well.

So, the Optimum Replacement Interval can be calculated to determine the period to change the oil.

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The optimum change interval of this lubricant in the pumps is about 875.5 hours of operation.

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The image above presents the reliability of the lubricant for the change interval (875.5 hours)

R(87.4458) = 99.45 %, and in the worst scenario using the two-sided confidence bounds is 86.04%.

3.2 Case Study 2: Truck Brake Pads

As a new truck brake pad was recently developed, the company made a field test to determine the lifespan of this part. The applied conditions were going to be the same as the real ones, but the time available for the test was short.

The company collected, from an 18 vehicle-sample, the thickness of the pads at different intervals based on the running mileage: when they were brand new at 0 (zero) mileage, with 25,000 Km, with 50,000 Km and with 75,000 Km. Each measurement is made using the average of several measurements taken on different positions of the brake pad, as defined by the analyst.

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The engineers determined that the best mathematical model to fit the brake pads degradation is an exponential function. The maximum acceptable limit for the pad’s degradation is 19 millimeters.

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On the Plot tab, the degradation curve for each brake pad is presented based on the mathematical model chosen.

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On the LDA tab, the projected time to failure of each sample is automatically generated:

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On the LDA Plot tab, the reliability plots for the brake pads can be generated.

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The brake pad manufacturer determined that they could accept a 5% probability of failure in warranty, in other words, 95% reliability for the warranty time. So, based on that requirement, the warranty time for the brake pads was determined.

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The warranty time, for a 95% reliability in the median value, is 105,187.2249 Km; however, as the lower bound for a two-sided confidence of 90% is 96,926.9825 Km, the management decided to play safe and to give a warranty for 95,000 Km for the pads.