Chapter 4: RTD Case Studies
This chapter is dedicated to present some examples of using R4All platform to perform RTD calculations.
4.1 Case Study 1: Safety Valve
After acquiring 100 safety valves and installing them in the plant, the engineers of an Oil & Gas company wanted to know what the probability of these valves work when requested. They accessed the event history of these valves and noted that, in the last 12 months of operation, only 2 of them did not perform their function when they were requested. What would the reliability of this component be with a 95% confidence?
As they were treating a discrete variable, the non-parametric binomial was the statistical method used to determine the reliability of this asset:
For a 95% confidence level, the reliability of these valves is 93.8381%.
4.2 Case Study 2: Truck’s LRU
A fleet of 20 trucks of the same model and operating under the same conditions was monitored for two years (a pre-series stage). These trucks were produced to perform a long-term reliability test before the launch of this new vehicle in series. From the lowest replaceable unit’s (LRU) failures of this fleet sample, a life data analysis was performed to estimate the reliability curve of each respective item.
However, after the end of this 2-year test period, several LRUs did not present any failure. Knowing that all vehicles reached 130,000 miles in the end of this period, what is the reliability of each one of these items with a 90% confidence?
The image below presents the R4All RTD module with the Non-Parametric Binomial method inputs and respective result:
4.3 Case Study 3: Underwater Valve
An Oil & Gas company has a valve located undersea. This valve’s function is to close the oil flow before performing the maintenance. In 20 years of operation, it is estimated that this valve will be triggered 8 times (cycle = seal and open). The test’s area has only one sample of this valve and they need to know if it is up to the reliability’s standards required for its operation.
The reliability specification is: R (20 years = 8 cycles) 95% with 95% confidence. There is no information about this valve’s life data, but due to being composed of 4 subsystems (electric, hydraulic, mechanical, and pneumatic), with several components each and each different component having around one or two failure modes, it is possible to assume a Weibull distribution with a beta close to 1 (or an exponential distribution), as there are several events that could lead to the loss of this asset’s function.
The test team big questions are: “In a life test of this only sample available, how much time (cycles) should be tested, simulating the operating conditions, without the loss of its function, to ensure it passes the reliability request?”
To determine the test time, the Parametric Binomial method was chosen. The image below presents the R4All RTD module with the Parametric Binomial method inputs and respective result:
So, the reliability requirement will be reached if the one item tested does not fail during the required test time (468 cycles).