Leak Detection in Transport Pipelines Using Enhanced Independent Component Analysis and Support Vector Machines

Leak detection of oil pipelines is an important issue for safe operation of pipelines, reducing oil loss and environmental pollution. A new approach based on principal component analysis (PCA) method to detect leaks of oil pipelines is proposed in this paper. In order to detect leaks, a classifier is designed to recognize negative pressure wave curve by training set. Results indicate that the method can detect many leak faults from a pressure curve, which might not be effectively detected by a traditional signal processing based method but can be recognized easily by human visual perception.

Signal estimation from functional magnetic resonance imaging data (fMRI) is a difficult and challenging task that involves carefully chosen models that can be validated by domain experts. Blind source separation (BSS) techniques such as principal component analysis (PCA), independent component analysis (ICA), and tensor decomposition have been proposed for estimating the task-fMRI signals. However, few studies have investigated their efficacy for classifying different tasks. This paper proposes yet another novel method to validate the signal estimation using predictive performance of the methods. Three commonly used BSS techniques are used for estimating signals (spatio-temporal maps) from task-fMRI. The dataset (consisting of 4 tasks) is taken from the Human Connectome Project (HCP). The extracted temporal signals are used to train a classifier that classifies two different tasks. For a binary classification, a Constrained-PARAFAC tensor decomposition achieves 92% accuracy for differentiating gambling vs. relational tasks. In addition, results for other binary task classifications are also presented. Overall, the proposed Constrained-PARAFAC performs better in prediction performance compared to PCA and ICA.

In order to further improve monitoring safety of oil and gas pipelines, a leakage detection technology for natural gas pipelines with distributed Fiber Bragg Grating (FBG) is put forward. Optical fiber, set up along pipelines acting as FBG sensors, is used to acquire pressure and vibration signals created by some events such as leakage, mechanical disturbance. Through grating matching and automatic distinguish technique leakage can be detected and located. Moreover, independent component analysis (ICA) technique is studied and used to separate useful signal from noise. Theory analysis and experiments showed this method has good capacity to detect and locate leakage effectively in pipelines.

In this paper, two machine learning techniques are applied and compared in order to model leak detection in pipelines in noisy environments. A set of accelerometers, mounted on the surface of the pipes, was deployed for the data acquisition process. Measurements of noise during normal operating conditions were recorded as well as measurements of leaks, generated on various distances from the sensors. Using these measurement data, a training set was created from their time-domain and frequency-domain features. The leak detection process is then modeled as a binary classification problem (leak detection or not). For this problem, two machine learning classification techniques were evaluated, the support vector machines and the decision trees. The results for each learner are compared with the original data from the test dataset using representative performance indicators and, overall, high levels of accuracy are achieved.

Not alike to principal component analysis (PCA) based monitoring statistics (T2 and SPE), the control limits for independent component analysis (ICA) monitoring statistics (I2 , Ie 2 and SPE) cannot be determined directly from a particular approximation distribution due to latent variables do not follow Gaussian distribution. Lee et al. (2004) proposed to use kernel density estimation (KDE) to obtain the control limits. However, the KDE method is very sensitive to the choice of smoothing parameter. Therefore, this study utilizes the support vector machine (SVM) for process fault detection by taking information of ICA extracted statistics as inputs of SVM. The proposed method (named, ICA-SVM) will be implemented in the Tennessee Eastman Process. In which, several multivariate monitoring schemes such as PCA, ICA, modified ICA and ICA- PCA will be also compared to demonstrate the efficiency of proposed method.

We address the problem of Blind Source Separation (BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis (ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to estimate the original intensities in the stated situation. We combine the advantages of existing sparse methods and Kernel ICA in our technique, by proposing wavelet packet based sparse decomposition of signals prior to the application of Kernel ICA. Simulations and experimental results illustrate the effectiveness and accuracy of the proposed approach. The approach is general in the way that it can be tailored and applied to a wide range of BSS problems concerning one-dimensional signals and images (two-dimensional signals).

Massive multiple input multiple output (MIMO) is the key technology in the 5G communication network. Blind source separation (BSS) plays an important role in MIMO communication when information about the transmitted signals or channel is not available. Independent component analysis (ICA) is one of the well-known BSS technique applied in MIMO wireless communication. Existing BSS techniques like ICA assumes number of receiving antennas to be greater than or equal to the number of transmit antennas and hence cannot be applied to multiple input single output (MISO) communication scenario, that happens during downlink massive MIMO communication. In single channel blind source separation (SCBSS) the source signals are retrieved from a single observed mixture (single channel or single receiving antenna) of the source signals. In this paper, we investigate the application of SCBSS to MISO communication system. Using ensemble empirical mode decomposition (EEMD), the MISO communication system is converted to a pseudo MIMO system followed by the application of principal component analysis (PCA) and ICA algorithms to separate the transmitted signals. The algorithm is successfully applied to 2 X 1 MISO system and with a slight modification to n X 1 MISO system. Simulation results are provided to validate the performance of the algorithm.

Recently, independent component analysis (ICA) is the most popular and promising statistical technique for blind audio source separation. This paper proposes the minimum beta-divergence based ICA as an adaptive robust audio source separation algorithm. This algorithm explores local structures of audio source signals in which the observed signals follow a mixture of several ICA models. The performance of this algorithm is equivalent to the standard ICA algorithms if observed signals are not corrupted by outliers and there exist only one structure of audio source signals in the entire data space, while it keeps better performance otherwise. It is able to extract all local audio source structures sequentially in presence of huge outliers. Our experimental results also support the above statements.

In the field of camera-based photoplethysmography the application of blind source separation (BSS) techniques has extensively stressed to cope with frequently occurring artifacts and noise. Although said techniques can help to extract the cardiac component from a mixture of input sources, permutation indeterminacy inherit to BSS techniques often introduces inaccuracies or requires manual intervention. The current contribution focuses on methods to automatically select the cardiac component from the output of BSS techniques applied to camera-based photoplethysmograms. To that end, we propose simple Markov models to describe and subsequently identify cardiac components. It is shown that good results can be obtained by combining different simple Markov models.

Support Vector Machines (SVM) and Independent Component Analysis (ICA) are two powerful and relatively recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. ICA is a feature extraction technique which can be considered a generalization of Principal Component Analysis (PCA). ICA has been mainly used on the problem of blind signal separation. In this paper we combine these two techniques for the face recognition problem. Experiments were made on two different face databases, achieving very high recognition rates. As the results using the combination PCA/SVM were not very far from those obtained with ICA/SVM, our experiments suggest that SVMs are relatively insensitive to the representation space. Thus as the training time for ICA is much larger than that of PCA, this result indicates that the best practical combination is PCA with SVM.KeywordsSupport Vector MachineFace RecognitionFace ImageIndependent Component AnalysisFace DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Lithological classification is a pivotal aspect in the field of geology, and traditional field surveys are inefficient and challenging in certain areas. Remote sensing technology offers advantages such as high efficiency and wide coverage, providing a solution to the aforementioned issues. The aim of this study is to apply remote sensing technology for lithological classification and attempt to enhance the accuracy of classification. Taking a study area in Jixi, Heilongjiang Province, China, as an example, lithological classification is conducted using high-resolution satellite remote sensing data from GF-2 and texture data based on gray-level co-occurrence matrix (GLCM). By comparing the accuracy of lithological classification using different methods, the support vector machine (SVM) method with the highest overall accuracy is selected for further investigation. Subsequently, this study compares the effects of combining GF-2 data with different texture data, and the results indicate that combining textures can effectively improve the accuracy of lithological classification. In particular, the combination of GF-2 and the Dissimilarity index performs the best among single-texture combinations, with an overall accuracy improvement of 7.8630% (increasing from 74.6681% to 82.5311%) compared to using only GF-2 data. In the multi-texture combination dataset, the Mean index is crucial for enhancing classification accuracy. Selecting appropriate textures for combination can effectively improve classification accuracy, but it is important to note that excessive overlaying of textures may lead to a decrease in accuracy. Furthermore, this study employs principal component analysis (PCA) and independent component analysis (ICA) to process the GF-2 data and combines the resulting PCA and ICA datasets with different texture data for lithological classification. The results demonstrate that combining PCA and ICA with texture data further enhances classification accuracy. In conclusion, this study demonstrates the application of remote sensing technology in lithological classification, with a focus on exploring the application value of different combinations of multispectral data, texture data, PCA data, and ICA data. These findings provide valuable insights for future research in this field.

Digital twins are transforming the digitalization and automation of offshore gas pipeline systems by enabling realtime monitoring, predictive maintenance, and operational efficiency. This study introduces a novel adaptive digital twin framework designed for leak detection and localization in offshore gas pipelines. The framework integrates OLGA-generated synthetic data, validated experimental results, and advanced machine learning (ML) techniques, including transfer learning and ensemble models. The proposed framework achieves a classification accuracy of 98.2% for leak detection, with a mean absolute error (MAE) of 0.11 cm for leak size prediction and a mean absolute percentage error (MAPE) of 3.8% for leak localization. A core innovation of this framework is the calibration methodology, which recalibrates dimensionless nomographs and leak detection correlations for seamless adaptation to new pipeline geometries and operating conditions. Through systematic steps, the calibrated correlations predict leak size and location with high accuracy, leveraging pressure drop and mass flow difference data. Additionally, ML-driven models enable efficient generation of new nomographs for pipelines with varying configurations, enhancing scalability and reducing computational effort. The real-time implementation enables predictions with a latency of less than 2 seconds, significantly outperforming conventional methods in speed and accuracy. Also, the framework’s adaptability, supported by its digital twin visualization and real-time feedback mechanisms, significantly improves pipeline integrity management, operational safety, and environmental protection. The study demonstrates the framework’s robustness in handling complex flow dynamics and offers a scalable solution to enhance the digital transformation of offshore oil and gas operations.

In biomedical signal processing, it is often the case that many sources are mixed into the measured signal. The goal is usually to analyze one or several of them separately. In the case of multichannel measurements, several blind source separation techniques are available for decomposing the signal into its components [e.g., independent component analysis (ICA)]. However, only a few techniques have been reported for analyses of single-channel recordings. Examples are single-channel ICA (SCICA) and wavelet-ICA (WICA), which all have certain limitations. In this paper, we propose a new method for a single-channel signal decomposition. This method combines empirical-mode decomposition with ICA. We compare the separation performance of our algorithm with SCICA and WICA through simulations, and we show that our method outperforms the other two, especially for high noise-to-signal ratios. The performance of the new algorithm was also demonstrated in two real-life applications.

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.

Many methods have been proposed for leak detection in pipelines. The method based on the acoustic signals created by the leakage is a non-destructive method and has presented reliable results. However, applying such a method in noisy environments is still a challenge. In this paper, a leak detection method applied in pipes which are located in a highly noise environment of the process area of an oil refinery is introduced. The proposed detection algorithm takes into account the frequency contents of the noise and the signals of the leaks as well as some statistical characteristics of the noise. Processing of measurements in time windows of 1s are found enough for decision making, keeping the response time of leakage detection very low. Results have shown reliable detection of leaks in sufficient distances without false alarms while there is the ability for the discrimination of some accidental or periodic events from defining them as leaks. The limited processing and storage requirements of the algorithm make it appropriate for efficient implementation.