Abstract
The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (DBNICR) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine‐tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the DBNICR learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.
Highlights
Because of aging pipelines, corrosion, or welding defects, small leaks and slow leaks occur frequently; such leaks represent risks to the environment and can cause financial losses [1,2,3,4]
The denoised sound velocities of the ultrasonic signals are the inputs of the deep belief network with independent component regression (DBNICR) and deep belief network (DBN), and the five feature vectors are input into the least squares twin support vector machine (LSTSVM) [31], the least squares support vector machine (LSSVM), the support vector machine (SVM), and the back propagation neural network (BPNN)
The proposed method and the LSTSVM-based, LSSVM-based, SVM-based, and BPNNbased methods are used to identify the different leakage apertures. 5,000 trials are performed, where 80% of samples are randomly selected for training and other samples are used for testing
Summary
Corrosion, or welding defects, small leaks and slow leaks occur frequently; such leaks represent risks to the environment and can cause financial losses [1,2,3,4]. The supervised learning algorithm of DBN is based on the backpropagation algorithm with a gradient; as a result, the weight adjustment can fall into a local optimum and slow the learning speed, thereby affecting the classification accuracy. Determining how to select the optimal weights and avoid the gradient of the fine adjustment method is the key to improve the classification accuracy. To overcome these difficulties, it is necessary to find a learning algorithm without the gradient. A weight optimization method based on DBNICR is proposed, to avoid the local optimum brought by the gradient algorithm, and the classification accuracy of DBN is further improved.
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