An Adaptive Neuro-Fuzzy Inference System-Based Approach for Oil and Gas Pipeline Defect Depth Estimation

Abstract-To determine the severity of metal-loss defects in oil and gas pipelines, the depth of potential defects, along with their length, needs first to be estimated. For this purpose, pipeline engineers use intelligent Magnetic Flux Leakage (MFL) sensors that scan the metal pipelines and collect defect-related data. However, due to the huge amount of the collected MFL data, the defect depth estimation task is cumbersome, timeconsuming, and error-prone. In this paper, we propose an adaptive neuro-fuzzy inference system (ANFIS)-based approach to estimate defect depths from MFL signals. Depth-related features are first extracted from the MFL signals and then are used to train the neural network to tune the parameters of the membership functions of the fuzzy inference system. A hybrid learning algorithm that combines least-squares and back propagation gradient descent method is adopted. Moreover, to achieve an optimal performance by the proposed approach, highly-discriminant features are selected from the obtained features by using the weight-based support vector machine (SVM). Experimental work has shown that encouraging results are obtained. Within error-tolerance ranges of ±15%, ±20%, ±25%, and ±30%, the depth estimation accuracies obtained by the proposed technique are 80.39%, 87.75%, 91.18%, and 95.59%, respectively. Moreover, further improvement can be easily achieved by incorporating new and more discriminant features

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research

Robust techniques and applications in fuzzy clustering

This dissertation addresses issues central to frizzy classification. The issue of sensitivity to noise and outliers of least squares minimization based clustering techniques, such as Fuzzy c-Means (FCM) and its variants is addressed. In this work, two novel and robust clustering schemes are presented and analyzed in detail. They approach the problem of robustness from different perspectives. The first scheme scales down the FCM memberships of data points based on the distance of the points from the cluster centers. Scaling done on outliers reduces their membership in true clusters. This scheme, known as the Mega-clustering, defines a conceptual mega-cluster which is a collective cluster of all data points but views outliers and good points differently (as opposed to the concept of Dave\u27s Noise cluster). The scheme is presented and validated with experiments and similarities with Noise Clustering (NC) are also presented. The other scheme is based on the feasible solution algorithm that implements the Least Trimmed Squares (LTS) estimator. The LTS estimator is known to be resistant to noise and has a high breakdown point. The feasible solution approach also guarantees convergence of the solution set to a global optima. Experiments show the practicability of the proposed schemes in terms of computational requirements and in the attractiveness of their simplistic frameworks. The issue of validation of clustering results has often received less attention than clustering itself. Fuzzy and non-fuzzy cluster validation schemes are reviewed and a novel methodology for cluster validity using a test for random position hypothesis is developed. The random position hypothesis is tested against an alternative clustered hypothesis on every cluster produced by the partitioning algorithm. The Hopkins statistic is used as a basis to accept or reject the random position hypothesis, which is also the null hypothesis in this case. The Hopkins statistic is known to be a fair estimator of randomness in a data set. The concept is borrowed from the clustering tendency domain and its applicability to validating clusters is shown here. A unique feature selection procedure for use with large molecular conformational datasets with high dimensionality is also developed. The intelligent feature extraction scheme not only helps in reducing dimensionality of the feature space but also helps in eliminating contentious issues such as the ones associated with labeling of symmetric atoms in the molecule. The feature vector is converted to a proximity matrix, and is used as an input to the relational fuzzy clustering (FRC) algorithm with very promising results. Results are also validated using several cluster validity measures from literature. Another application of fuzzy clustering considered here is image segmentation. Image analysis on extremely noisy images is carried out as a precursor to the development of an automated real time condition state monitoring system for underground pipelines. A two-stage FCM with intelligent feature selection is implemented as the segmentation procedure and results on a test image are presented. A conceptual framework for automated condition state assessment is also developed

A Machine Learning Approach for Big Data in Oil and Gas Pipelines

Abstract-Experienced pipeline operators utilize Magnetic Flux Leakage (MFL) sensors to probe oil and gas pipelines for the purpose of localizing and sizing different defect types. A large number of sensors is usually used to cover the targeted pipelines. The sensors are equally distributed around the circumference of the pipeline; and every three millimeters the sensors measure MFL signals. Thus, the collected raw data is so big that it makes the pipeline probing process difficult, exhausting and error-prone. Machine learning approaches such as neural networks have made it possible to effectively manage the complexity pertaining to big data and learn their intrinsic properties. We concentrate, in this work, on the applicability of artificial neural networks in defect depth estimation and present a detailed study of various network architectures. Discriminant features, which characterize different defect depth patterns, are first obtained from the raw data. Neural networks are then trained using these features. The Levenberg-Marquardt back-propagation learning algorithm is adopted in the training process, during which the weight and bias parameters of the networks are tuned to optimize their performances. Compared with the performance of pipeline inspection techniques reported by service providers such as GE and ROSEN, the results obtained using the method we proposed are promising. For instance, within ±10% error-tolerance range, the proposed approach yields an estimation accuracy at 86%, compared to only 80% reported by GE; and within ±15% error-tolerance range, it yields an estimation accuracy at 89% compared to 80% reported by ROSEN

Defect Detection and Classification in Sewer Pipeline Inspection Videos Using Deep Neural Networks

Sewer pipelines as a critical civil infrastructure become a concern for municipalities as they are getting near to the end of their service lives. Meanwhile, new environmental laws and regulations, city expansions, and budget constraints make it harder to maintain these networks. On the other hand, access and inspect sewer pipelines by human-entry based methods are problematic and risky. Current practice for sewer pipeline assessment uses various types of equipment to inspect the condition of pipelines. One of the most used technologies for sewer pipelines inspection is Closed Circuit Television (CCTV). However, application of CCTV method in extensive sewer networks involves certified operators to inspect hours of videos, which is time-consuming, labor-intensive, and error prone. The main objective of this research is to develop a framework for automated defect detection and classification in sewer CCTV inspection videos using computer vision techniques and deep neural networks. This study presents innovative algorithms to deal with the complexity of feature extraction and pattern recognition in sewer inspection videos due to lighting conditions, illumination variations, and unknown patterns of various sewer defects. Therefore, this research includes two main sub-models to first identify and localize anomalies in sewer inspection videos, and in the next phase, detect and classify the defects among the recognized anomalous frames. In the first phase, an innovative approach is proposed for identifying the frames with potential anomalies and localizing them in the pipe segment which is being inspected. The normal and anomalous frames are classified utilizing a one-class support vector machine (OC-SVM). The proposed approach employs 3D Scale Invariant Feature Transform (SIFT) to extract spatio-temporal features and capture scene dynamic statistics in sewer CCTV videos. The OC-SVM is trained by the frame-features which are considered normal, and the outliers to this model are considered abnormal frames. In the next step, the identified anomalous frames are located by recognizing the present text information in them using an end-to-end text recognition approach. The proposed localization approach is performed in two steps, first the text regions are detected using maximally stable extremal regions (MSER) algorithm, then the text characters are recognized using a convolutional neural network (CNN). The performance of the proposed model is tested using videos from real-world sewer inspection reports, where the accuracies of 95% and 86% were achieved for anomaly detection and frame localization, respectively. Identifying the anomalous frames and excluding the normal frames from further analysis could reduce the time and cost of detection. It also ensures the accuracy and quality of assessment by reducing the number of neglected anomalous frames caused by operator error. In the second phase, a defect detection framework is proposed to provide defect detection and classification among the identified anomalous frames. First, a deep Convolutional Neural Network (CNN) which is pre-trained using transfer learning, is used as a feature extractor. In the next step, the remaining convolutional layers of the constructed model are trained by the provided dataset from various types of sewer defects to detect and classify defects in the anomalous frames. The proposed methodology was validated by referencing the ground truth data of a dataset including four defects, and the mAP of 81.3% was achieved. It is expected that the developed model can help sewer inspectors in much faster and more accurate pipeline inspection. The whole framework would decrease the condition assessment time and increase the accuracy of sewer assessment reports

Integrated Condition Assessment of Subway Networks Using Computer Vision and Nondestructive Evaluation Techniques

Subway networks play a key role in the smart mobility of millions of commuters in major metropolises. The facilities of these networks constantly deteriorate, which may compromise the integrity and durability of concrete structures. The ASCE 2017 Report Card revealed that the condition of public transit infrastructure in the U.S. is rated D-; hence a rehabilitation backlog of $90 billion is estimated to improve transit status to good conditions. Moreover, the Canadian Urban Transit Association (CUTA) reported 56.6 billion CAD in infrastructure needs for the period 2014-2018. The inspection and assessment of metro structures are predominantly conducted on the basis of Visual Inspection (VI) techniques, which are known to be time-consuming, costly, and qualitative in nature. The ultimate goal of this research is to develop an integrated condition assessment model for subway networks based on image processing, Artificial Intelligence (AI), and Non-Destructive Evaluation (NDE) techniques. Multiple image processing algorithms are created to enhance the crucial clues associated with RGB images and detect surface distresses. A complementary scheme is structured to channel the resulted information to Artificial Neural Networks (ANNs) and Regression Analysis (RA) techniques. The ANN model comprises sequential processors that automatically detect and quantify moisture marks (MM) defects. The RA model predicts spalling/scaling depth and simulates the de-facto scene by developing a hybrid algorithm and interactive 3D presentation. In addition, a comparative analysis is performed to select the most appropriate NDE technique for subway inspection. This technique is applied to probe the structure and measure the subsurface defects. Also, a novel model for the detection of air voids and water voids is proposed. The Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Monte Carlo Simulation (MCS) are streamlined through successive operations to create the integrated condition assessment model. To exemplify and validate the proposed methodology, a myriad of images and profiles are collected from Montréal Metro systems. The results ascertain the efficacy of the developed detection algorithms. The attained recall, precision, and accuracy for MM detection algorithm are 93.2%, 96.1%, and 91.5% respectively. Whereas for spalling detection algorithm, are 91.7%, 94.8%, and 89.3% respectively. The mean and standard deviation of error percentage in MM region extraction are 12.2% and 7.9% respectively. While for spalling region extraction, they account for 11% and 7.1% respectively. Subsequent to selecting the Ground Penetrating Radar (GPR) for subway inspection, attenuation maps are generated by both the amplitude analysis and image-based analysis. Thus, the deteriorated zones and corrosiveness indices for subway elements are automatically computed. The ANN and RA models are validated versus statistical tests and key performance metrics that indicated the average validity of 96% and 93% respectively. The air/water voids model is validated through coring samples, camera images, infrared thermography and 3D laser scanning techniques. The validation outcomes reflected a strong correlation between the different results. A sensitivity analysis is conducted showing the influence of the studied subway elements on the overall subway condition. The element condition index using neuro-fuzzy technique indicated different conditions in Montréal subway systems, ranging from sound concrete to very poor, represented by 74.8 and 35.1 respectively. The fuzzy consolidator extrapolated the subway condition index of 61.6, which reveals a fair condition for Montréal Metro network. This research developed an automated tool, expected to improve the quality of decision making, as it can assist transportation agencies in identifying critical deficiencies, and by focusing constrained funding on most deserving assets

Applications of pattern classification to time-domain signals

Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the interaction of the fields with the materials and structures under study. often the method of interpreting the signal varies by the application, but automatic detection of events in signals is always useful in order to attain results quickly with less human error. One method of automatic interpretation of data is pattern classification, which is a statistical method that assigns predicted labels to raw data associated with known categories. In this work, we use pattern classification techniques to aid automatic detection of events in signals using features extracted by a particular application of the wavelet transform, the Dynamic Wavelet Fingerprint (DWFP), as well as features selected through physical interpretation of the individual applications. The wavelet feature extraction method is general for any time-domain signal, and the classification results can be improved by features drawn for the particular domain. The success of this technique is demonstrated through four applications: the development of an ultrasonographic periodontal probe, the identification of flaw type in Lamb wave tomographic scans of an aluminum pipe, prediction of roof falls in a limestone mine, and automatic identification of individual Radio Frequency Identification (RFID) tags regardless of its programmed code. The method has been shown to achieve high accuracy, sometimes as high as 98%

Uncertainty and error in laser triangulation measurements for pipe profiling

Underground pipeline infrastructure often receives insufficient attention and maintenance. Those responsible for ensuring the continuing functionality of this infrastructure primarily use subjective information in their decision making, and standards defining the level of damage acceptable before repair or replacement are difficult to implement. Laser pipe profiling is a relatively new technology that has emerged to take a step toward the objective assessment of buried assets. A laser profiler is a device that traverses a section of pipe, taking measurements of radius around the circumference of the inner pipe wall at multiple locations along the length of the pipe. The accuracy of the measurements obtained by a profiler is a critical piece of knowledge for the evaluation of its usefulness. Analytical measurement and uncertainty models were developed for three laser profiling configurations. These configurations involved a digital camera and a laser whose relative position and orientation were fixed relative to one another. The three configurations included (1) a conically projected laser aligned with the pipe axis, (2) a planar laser placed perpendicular to the pipe axis, and (3) a side-facing laser that projected a line onto the pipe wall parallel to the axis of the pipe. The models utilized normalized system parameters to compute pipe geometry from digital images that reveal the intersection of the laser light and the pipe wall; error propagation techniques were applied to compute the variation in measurement uncertainty as a function of position in the measurement space. Analytical evaluation of the conical projection configuration revealed infinite measurement error for a region of the measurement space; the unbounded error was eliminated by utilizing two conical lasers. The accuracy and uncertainty of the perpendicular plane and side facing configurations were much better than for the conical configuration. Physical models of these two configurations were constructed, and measurements were collected for a pipe section to validate the measurement and uncertainty predictions of the analytical models. The difference between observed worst-case laser measurement error and predicted uncertainty was on the order of 0.1% of nominal pipe radius. This work provides pipe profiler designers the analytical detail required to understand the relationship between system geometry, camera parameters and measurement accuracy. The work provides asset managers with a reference against which to evaluate laser profiling for their infrastructure condition monitoring needs