A Percussion Method to Detect Erosion of Elbow Using Machine Learning Algorithms




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Elbows are widely used in many industries, especially in oil and gas industry. The purpose of elbow is to change the flow direction in pipeline systems. In some severe applications, elbows are employed to transport abrasive high-pressure multiphase flow medium. With the increase of the service time, the wall thickness of the elbow will become thinner due to erosion and wear, which may lead to piercing or bursting of the high-pressure piping system and cause negative impacts on both the economy and the environment. A novel method of using percussion and machine learning to detect the rate of elbow’s erosion was developed and discussed in this thesis. Three sets of elbow and pipe assembly were used as test specimens. Then, six different erosion levels were simulated by grinding off mass from the internal wall of the elbows. The elbow bottom location, where the simulated erosion was, was tapped to generate the percussion sound, which was recorded by a smart phone. The power spectral density (PSD) and mel-frequency cepstral coefficient (MFCC) were employed to extract features from the percussion sound. The k-nearest neighbor (KNN), the decision tree (DT), and the support vector machine (SVM) were implemented with PSD features to learn the training samples and predict test samples. By using the above three basic machine learning methods, the experiment achieved an average of 90% accuracy on training data and 80% on testing data. Then, the recurrent neural network (RNN), a deep learning method, was implemented with MFCC features to learn and train the data. This method achieved 100% accuracy on training data and 97% on testing data. Finally, the unsupervised clustering algorithms, k-means and Gaussian mixture model (GMM), were implemented with transformed MFCC features. The accuracy of k-means algorithm varied in a range from 49% to 68%, while the GMM clustering method achieved an accuracy of 76%. The results of this work have demonstrated the feasibility of the novel method of percussion and machine learning to detect the level of erosion of elbow in pipeline. Compared with the conventional method, the proposed method does not require installation of sensors or extra signal acquisition instruments. The erosion detection using percussion and machine learning brings great potential contribution to pipeline operating safety assurance.



Machine learning, Percussion, Erosion, Elbow