pipenostics

R-package for diagnostics, reliability and predictive maintenance of pipeline systems.

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Intro

The package aggregates some of the separate knowledge regarding engineering, reliability, diagnostics, and predictive maintenance of pipeline systems. At the moment, the package includes utilities for processing corrosion data commonly collected by inline inspection, as well as empirical models for calculating local thermal-hydraulic conditions in district heating networks. Additionally, the package provides a variety of useful tools and datasets that can assist with solving a range of related issues.

Installation

For the stable version install from CRAN:

R install.packages("pipenostics")

For the latest version leverage r-universe:

R install.packages("pipenostics", repos = "https://omega1x.r-universe.dev")

Usage examples

Corrosion diagnostics

By using of b31crvl() simply imitate the output of CRVL.BAS which is the honored software for determining the allowable length and maximum allowable working pressure presented in ASME B31G-1991:

```R library(pipenostics)

b31crvl(maop = 910, d = 30, wth = .438, smys = 52000, def = .72, depth = .1, l = 7.5) ```

txt -- Calculated data -- Intermediate factor (A) = 1.847 Design pressure = 1093 PSI; Safe pressure = 1093 PSI Pipe may be operated safely at MAOP, 910 PSI With corrosion length 7.500 inch, maximum allowed corrosion depth is 0.2490 inch; A = 1.847 With corrosion depth 0.100 inch, maximum allowed corrosion length is Inf inch; A = 5.000

Probability of failure

Let's consider a pipe in district heating network with

R diameter <- 762 # [mm] wall_thickness <- 10 # [mm] UTS <- 434.3697 # [MPa]

which transfers heat-carrier (water) at

R operating_pressure <- 0.588399 # [MPa] temperature <- 95 # [°C]

During inline inspection four corroded areas (defects) are detected with:

R depth <- c(2.45, 7.86, 7.93, 8.15) # [mm]

whereas the length of all defects is not greater 200 mm:

R length <- rep(200, 4) # [mm] print(length)

R [1] 200 200 200 200

Corrosion rates in radial and in longitudinal directions are not well-known and may vary in range .01 - .30 mm/year:

R rar = function(n) stats::runif(n, .01, .30) / 365 ral = function(n) stats::runif(n, .01, .30) / 365

Then probabilities of failure (POFs) related to each corroded area are near:

R pof <- mepof(depth, length, rep(diameter, 4), rep(wall_thickness, 4), rep(UTS, 4), rep(operating_pressure, 4), rep(temperature, 4), rar, ral, method = "dnv")

txt pipenostics::mepof: process case [4/4] - 100 % . All done, thanks!

R print(pof)

R [1] 0.000000 0.252935 0.368741 0.771299

So, the POF of the pipe is near

R print(max(pof))

R [1] 0.771299

The value of POF changes in time. So, in a year after inline inspection of the pipe we can get something near

R pof <- mepof(depth, length, rep(diameter, 4), rep(wall_thickness, 4), rep(UTS, 4), rep(operating_pressure, 4), rep(temperature, 4), rar, ral, method = "dnv", days = 365)

txt pipenostics::mepof: process case [4/4] - 100 % . All done, thanks!

R print(pof)

R [1] 0.000000 0.526646 0.647422 0.928825

For entire pipe we get something near:

R print(max(pof))

R [1] 0.928825

Two years ago before inline inspection the pipe state was rather good:

R pof <- mepof(depth, length, rep(diameter, 4), rep(wall_thickness, 4), rep(UTS, 4), rep(operating_pressure, 4), rep(temperature, 4), rar, ral, method = "dnv", days = -2 * 365)

txt pipenostics::mepof: process case [4/4] - 100 % . All done, thanks!

R print(pof)

R [1] 0.000000 0.040849 0.072734 0.272358

For entire pipe we get something near:

R print(max(pof))

R [1] 0.272358

Regime tracing

Let's consider the next 4-segment tracing path:

Suppose we have the next sensor readings for forward tracing:

R t_fw <- 130 # [°C] p_fw <- 0.588399 # [MPa] g_fw <- 250 # [ton/hour]

Let's discharges to network for each pipeline segment are somehow determined as

R discharges <- seq(0, 30, 10) # [ton/hour] print(discharges)

R [1] 0 10 20 30

Then the calculated regime (red squares) for forward tracing is

R regime_fw <- m325traceline(t_fw, p_fw, g_fw, discharges, forward = TRUE) print(regime_fw)

```R $temperature [1] 129.1799 128.4269 127.9628 127.3367

$pressure [1] 0.5878607 0.5874226 0.5872143 0.5870330

$flow_rate [1] 250 240 220 190 ```

ℹ Read article Concepts and useful notes for a deeper dive into the topic.