• International Journal of Technology (IJTech)
  • Vol 14, No 3 (2023)

Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines

Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines

Title: Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines
Andy Noorsaman, Dea Amrializzia, Habiburrahman Zulfikri, Reviana Revitasari, Arsene Isambert

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Cite this article as:
Noorsaman, A., Amrializzia, D., Zulfikri, H., Revitasari, R., Isambert, A., 2023. Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines. International Journal of Technology. Volume 14(3), pp. 680-689

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Andy Noorsaman Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Dea Amrializzia Department of Process Engineering, PT. Rekayasa Engineering, Jl. Kalibata Timur II No.36, South Jakarta, Jakarat 12740, Indonesia
Habiburrahman Zulfikri Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Reviana Revitasari Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Arsene Isambert Laboratoire de Genie de Procedes et Materiaux, Ecole Centrale Paris, F 92295 Chatenay Malabry Cedex, France
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Abstract
Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines

A transmission pipeline is the safest and most effective way of transporting large volumes of natural gas over long distances. However, if not maintained efficiently, failures of gas transmission pipelines can occur and cause catastrophic events. Therefore, an accurate prediction of pipe failures and operational reliability is required to determine the optimal pipe replacement timing such that the incidence of pipe failures can be prevented. Nowadays, computer-assisted technology helps businesses make better decisions, and machine learning is among the excellent techniques that can be utilized in predicting failures. In this study, two machine learning algorithms, i.e., random forest and binary logistic regression, are developed, and their prediction abilities are compared. The model is developed based on a decade of unstructured and complex historical failure data of the onshore gas transmission pipelines released by the United States Department of Transportation. The modeling process begins with data pre-processing followed by model training, model testing, performance measuring, and failure predicting. Both algorithms have demonstrated excellent results. The random forest model achieved an AUC of 0.89 and a predictive accuracy of 0.913, while the binary logistic regression model outperformed with an AUC of 0.94 and a prediction accuracy of 0.949. The trained model is further employed to predict future failures on a 11900-mile natural gas pipeline spanning from Louisiana to the northeast section of the United States. We show the location of the pipes that will be broken in the interval of five years and estimate that 29%/63%/83% of the pipes will break by 2025/2030/2035.

Binary logistic regression; Failure prediction; Machine learning; Random forest; Transmission pipeline

Introduction

Natural gas as a petroleum substitute offers many economic, technological, and environmental benefits and increases efficiency because it is quickly developed (Lee et al., 2012) as cited in Bawono and Kusrini, 2017). It is a versatile energy source because it can be stored and transported in trucks or tankers as liquified natural gas, medium-conditioned liquified gas, or compressed natural gas (Ríos-Mercado and Borraz-Sánchez, 2015) as cited in (Farizal, Dachyar, and Prasetya, 2021).  However, Mikolajková-Alifov et al. (2019) study conveys that transporting large amounts of natural gas via pipelines, one of which is through onshore gas transmission pipelines, is more cost-effective (Farizal, Dachyar, and Prasetya, 2021). A submarine pipeline in a submerged floating tunnel (SFT) is proposed as an alternative solution to pipeline-related ecological issues (Budiman, Raka, and Wahyuni, 2017). However, SFT is not covered by the scope of this study, which is concerned with a natural gas pipeline that runs from Louisiana to the northeast United States.

Although more efficient than trucks or tankers, onshore gas transmission pipelines face serious challenges. Its failures are disastrous, causing financial losses, environmental damage, and even death. Gas pipeline failures are caused by several factors, including pipe/weld material failure, excavation damage, corrosion, equipment failure, soil movement, and incorrect operation (Dai et al., 2017).  Between January 2010 and November 2017, approximately 17.55 billion cubic feet of methane gas was lost through the transmission pipeline in the United States. This amount of gas is enough to heat around 233 thousand houses for a year. Unfortunately, during this period, pipeline failures caused nearly 100 fatalities, and around 500 injuries, and incurred a cost of approximately 1.1 billion US dollars  (Thompson, 2017).

In the context of industrial internet of things (IIoT)– increased interconnectedness and opportunities to collect data, process and analyze information – predictive maintenance can be a good strategy to face the problem. Predictive maintenance utilizes a wealth of process data and advanced analytical methods to predict failures well before urgent action has to be taken.

The current era of the fourth industrial revolution has enabled computer-assisted technology to help businesses, including the oil and gas industries, make better decisions (Hanga and Kovalchuk, 2019). In particular, machine learning techniques, which allow for automation of the process of analytical model building, offer great potential in predicting failures accurately. Machine learning tools are built to learn from data by establishing data structures and mapping complex relationships between input parameters and targets such that they can adapt to future input data (Shalev-Shawrtz and Ben-David, 2014). For instance, recently, the machine-learning created model for predicting pipe failures in water supply networks in Seville, Spain, shows detailed estimation and suggest specific and realistic suggestion to prevent approximately 30% of failures by replacing only 3% of the network's pipes annually (Robles-Velasco et al., 2020). Eastvedt, Naterer, and Duan (2022) have presented a method of monitoring a subsea oil pipeline for fault detection using a regression-supervised machine learning (ML) algorithm. ML algorithms were developed by using flow velocity data derived from ANSYS Fluent simulations, pressure, and temperature. It shows that the ML algorithm could 97% accurately predict the outputs (Eastvedt, Naterer, and Duan, 2022). A study has also been carried out by analyzing the performance of the Bayesian network in predicting pipe failure using a large and highly variable dataset from the water distribution system in the United Kingdom. Method one involved a supervised learning method to build a Bayesian network by understanding common failure types (joint, pinhole, circumferential, and longitudinal), while method two involved an automated learning method. The Bayesian network built using the automated method was able to achieve an overall accuracy of 84.4% compare to the 81.2% for the Bayesian network supervised learning method (Tang, Parsons, and Jude, 2019). Therefore, machine learning, if trained properly, can predict failures quickly and accurately. In this study, two ML algorithms, i.e., random forest and binary logistic regression, are developed, and their performances are compared in predicting a decade of historical data on gas transmission pipeline failures in the US. 

Experimental Methods

2.1. Raw Dataset

        The raw dataset was collected from the open-source data released by the Pipeline and Hazardous Materials Safety Administration (PHMSA) under the US Department of Transportation. The data were collected from 2010 to 2020, and their attributes can be classified into three groups. The first group includes the pipes’ physical characteristics, such as the nominal diameter of the section, material, length, cover depth, and wall thickness. The second group comprises the attributes of an operational condition, such as working pressure and class locations. The third group of attributes comprises work logs related to the pipes, including pipe ID, pipe facility name, operator of the pipe, region, latitude, longitude, years since installation, date of every historical break, and incident report number. To facilitate the understanding of the available datasets, we created a map (Figure 1) that displays the status of onshore gas transmission pipes in the United States from 2010 to 2020. The map indicates that 1065 out of 1270 pipes (84%) are currently in a "fail" status, while the remaining pipes, marked in green, are in a "not fail" status.

Figure 1 Map of status of pipes for onshore natural gas transmission in the United States from 2010 to 2020

2.2. Methods

Pre-processing, learning, testing, performance measuring, and mapping of the datasets were performed using R, a language and software environment for statistical computing, version 4.0.2. R studio version 1.2.5042 was used as an integrated development environment for R. The ‘caret’ R package was used to perform the models (Kuhn, 2008), and Leaflet, an open-source JavaScript library, is employed to create an interactive map of the pipes network with the attached state of failure.

2.2.1.   Data Pre-Processing

Raw datasets are usually not used directly in machine learning because of several reasons, including missing values and noises. Therefore, data pre-processing is necessary to perform easy operations in later steps. Firstly, the attributes deemed irrelevant to the prediction system, such as gas flow, and the attributes with more than 50% missing values, are eliminated. Secondly, to improve the predictors’ predictive performance and to simplify the model for easy interpretation, several most influential attributes in the prediction problems are selected based on the importance value (Guyon and Elisseeff, 2003). Here, calculations of filter-based variable importance were carried out to select attributes for the model. Thirdly, we divided the dataset into 90% of the dataset for the training set and the remaining 10% for the testing set. We note that the percentage of failed and not-failed pipes in both training and testing data sets is the same. 

It is well known that the problem of learning from imbalanced data (He and Gracia, 2009) emerged from underrepresented data, and severe class distribution skews can cause the standard classification models to perform improperly (Wang et al., 2013). In the present study, the dataset is imbalanced as 84% of the data are correlated to the “fail” status, and hence the model will tend to predict a “fail” status. One method that can be applied to solve the imbalance problem, which is employed in the present study, is the Randomly Over Sampling Example (ROSE) technique. One method that can be applied to solve the imbalance problem, which is employed in the present study, is the Randomly Over Sampling Example (ROSE) technique (Lunardon, Menardi, and Torelli, 2014). After incorporating the imbalanced treatment is incorporated in the pre-processing step, the user data is ready to be used for the machine learning algorithms.

2.2.2.   Failure Prediction Modeling: Training and Testing

Machine learning (ML) is a technique that automatically learns patterns from data without assumptions regarding the structure of the data. We adopted Binary Logistic Regression (BLR) and Random Forest (RF) as ML algorithms in our prediction system. BLR is a supervised machine learning algorithm that analyzes a dataset containing one or multiple variables to predict a binary outcome. The classification algorithm of BLR is trained on a labeled dataset and uses true labels during the training phase. BLR allows us to “study how a set of predictor variables is related to a dichotomous response variable” (Harrell, 2005). If we note  as the set of n explanatory variables,  as the set of n+1 parameters, and Y as the dependent variable, the logit model or logistic model can be constructed as follow:

If we denote P(Y=1) = p, the logit function becomes

The model measures the estimated probability of the predicted output, which varies between 0 and 1 and is on a sigmoid function of the following form

The random forest algorithm is a classification model that consists of a structured collection of trees. Unlike a decision tree, which only consists of one tree in the classification and prediction process, a random forest creates a voting mechanism for the class, which significantly affects the accuracy of the model (Chen, Liaw, and Breiman, 2004). Decision trees are generated using the attribute selection indicators such as information acquisition, acquisition ratio, and the Gini index for each attribute. The Gini index is used to measure the probability of a particular variable being misclassified when randomly selected.

Random forest is an ensemble learning technique that combines multiple decision trees to improve the accuracy of the model. The random forest equation can be written as:

where

f(x) is the predicted output. n is the number of decision trees in the forest.

fi(x) is the predicted output of the ith decision tree.

Each decision tree is trained on a different subset of the training data and a random subset of the features. The final prediction is made by taking the average of the predictions of all the decision trees.

Random forest depends on a random vector value with the same distribution in all trees. Each decision tree has the maximum depth. The random forest is a classifier consisting of a tree classifier  is a random vector distributed independently, and each tree with the most votes and the most popular class is selected as a result. RF is arguably simpler and more powerful than other non-linear classification algorithms (Breiman, 2001).

2.2.3.   Measuring Performance

In building the prediction system, we need a numerical indicator to tell whether the system performs well or not. The performance is measured by the confusion matrix, the receiver operating characteristic (ROC) curve, and the estimation of the accuracy. The confusion matrix contains the real values against those predicted for the validation set (Table II). There are four possible results for each sample: true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN). Each box would include the value of observations of each type (Han, Kamber and Pei, 2000).

Table 1 Confusion matrix for evaluating the performance of a classification model

 

 

Predicted

 

 

 

Negative

Positive

Actual

Negative

TN

FP

Positive

FN

TP

The Receiver Operating Characteristics curve depicts the TP rate (or recall) against the FN rate (or 1?specificity) for the different values of a risk threshold. The accuracy is calculated as the fraction of the correct predictions to the total predictions. Furthermore, it is common to calculate the Area Under the Curve (AUC) as a metric, representing a classifier’s ability to avoid false classifications. The AUC is between 0 and 1 (Fawcett, 2006). When an AUC equals 0.5, the classifier will make random classifications. An AUC higher than 0.9 is excellent (Hosmer and Lemeshow, 2000; Bawono and Kusrini, 2017).

After optimizing and testing the performance of the model, we applied it to predict the failure on a 11900-mile natural gas pipeline that spans from Louisiana to the northeast section of the United States. The pipeline is operated by the Tennessee Gas Pipeline Company. The reconstructed dataset consists of approximately 50 documented properties, including diameter, material, and length.

Results and Discussion

     We considered thirteen attributes, the importance value of which are calculated and shown in Table II. The correlation value between pipe failures (dependent variable) and other selected attributes (independent variable) from the training datasets is listed in the column of Table II. Attributes, the importance value of which is below 0.5, are considered to have no relevance to pipe failures (Wang et al., 2016). Therefore, the attributes "Explode Indication," "Ignite Indication," and "Case" are excluded in the modeling stage, and further computation will only consider the remaining ten attributes.

Table 2 Attributes of the dataset with the type of data, description, and the important values between each attribute and the “Failure” attribute.

Name of Attributes

Type

Description

Value

Explode Indication

Categorical

Indication of possible pipe explosion

0.206

Ignite Indication

Categorical

Indication of possible pipe ignition

0.212

Cause

Categorical

Cause of pipe failure

0.215

MAOP

Numerical

Maximum Allowable Operating Pressure in psig

0.630

Thickness

Numerical

The pipe’s wall thickness is in inch

0.634

Depth

Numerical

The depth of cover in inch

0.637

Age

Numerical

The pipe’s age in a year

0.647

Area

Categorical

The area of the laid pipe

0.651

Coat

Categorical

The pipe coating type

0.655

Diameter

Numerical

The nominal pipe size in inch

0.689

Length

Numerical

The length of isolation segment in ft

0.741

Class

Categorical

The pipe class (class 1, class 2, and class 3)

0.842

Failure

Categorical

The status of the pipe (fail or not fail)

1

 

        Table 3 displays how each algorithm performed on the testing data by creating the confusion matrix for each algorithm. The rows in a confusion matrix correspond to what the machine learning algorithm predicted, and the columns correspond to the historical data. Both algorithms perform very well in predicting at least 93.5% of the failed pipes and 80% of the not-failed pipes. BLR algorithm performs slightly better than the RF algorithm as BLR identifies 4 more failed pipes correctly, bringing the percentage of correctly identified failed pipes to 97.2%.

Table 3 Confusion matrix obtained from the BLR and RF algorithms

Algorithm:

BLR

Predicted

Not Fail

Fail

Actual

Not Fail

16 (80.0%)

3 (2.8%)

Fail

4 (20.0%)

104 (97.2%)

Algorithm:

RF

Predicted

Not Fail

Fail

Actual

Not Fail

16 (80.0%)

7 (6.5%)

Fail

4 (20.0%)

100 (93.5%)

 

To further compare the performance of the two studied algorithms, we compute the accuracy, sensitivity, specificity and F1 score according to the following formula: 

       Table 4 presents a summary of the accuracy, sensitivity, precision, and F1 score of both studied algorithms. Based on the results, logistic regression is preferred over the random forest model due to its superior performance in terms of accuracy, precision, recall, and F1 score. The higher F1 score of the logistic regression model suggests that it is more effective in predicting outcomes and is more stable than the random forest model.

Table 4 The performance of machine learning algorithms

Performance

Binary Logistic Regression

Random Forest

Accuracy

0.945

0.913

Specificity

0.842

0.696

Precision

0.972

0.935

Recall

0.963

0.962

F1 Score

0.967

0.948

Figure 2 ROC curve for pipe failures obtained from the application of binary logistic regression and random forest algorithms. The values of AUC are also indicated.

ROC curves, which plot the true positive rate (sensitivity/recall) in the y-axis as a function of the false positive rate (1?specificity) in the x-axis, from the computations using each algorithm are shown in Figure 2. The ROC curves for both methods can be categorized as excellent since the area under the curve (AUC) value is higher than 0.9. (Hosmer and Lemeshow, 2000). Again, the BLR algorithm demonstrates a slightly better performance as the resulting AUC is larger than that of RF (0.89 vs 0.94).

As the binary logistic regression algorithm shows better performance, we employ it to predict the failures of the pipeline operated by the Tennessee Gas Pipeline. This pipeline is chosen because the available data match the input attribute requirements of the trained model. The results are mapped into the geographical location of the pipeline shown in Figure 3.  Our analysis predicts that 29% of the pipes are expected to break by 2025, 63% by 2030, and 83% by 2035. Interestingly, the percentage of predicted pipe breaks does not increase monotonically as a function of time, with the highest number of pipe breaks anticipated to occur between 2025 and 2030.