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

## Intro

The package aggregates to some extent the separate knowledge concerning engineering, reliability, diagnostics and predictive maintenance of pipeline systems. For the present time the package contains utilities for processing corrosion data commonly gathered by inline inspection, as well as empirical models for calculations of local thermal-hydraulic regimes of district heating network.

## Installation

For the stable version install from CRAN:

install.packages("pipenostics")

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:

library(pipenostics)

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

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

which transfers heat-carrier (water) at

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

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

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

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

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

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:

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")
pipenostics::mepof: process case [4/4] - 100 % . All done, thanks!                 
print(pof)
[1] 0.000000 0.252935 0.368741 0.771299

So, the POF of the pipe is near

print(max(pof))
[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

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)
pipenostics::mepof: process case [4/4] - 100 % . All done, thanks!             
print(pof)
[1] 0.000000 0.526646 0.647422 0.928825

For entire pipe we get something near:

print(max(pof))
[1] 0.928825

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

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)
pipenostics::mepof: process case [4/4] - 100 % . All done, thanks!
print(pof)
[1] 0.000000 0.040849 0.072734 0.272358

For entire pipe we get something near:

print(max(pof))
[1] 0.272358

### Regime tracing

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

Suppose we have the next sensor readings for forward tracing:

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

discharges <- seq(0, 30, 10)  # [ton/hour]
print(discharges)
[1]  0 10 20 30

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

regime_fw <- m325traceline(t_fw, p_fw, g_fw, discharges, forward = TRUE)
print(regime_fw)
$temperature [1] 129.1799 128.4269 127.9628 127.3367$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.