In traditional sensory array-based acoustic emission methods that are used for gas leakage localization, the localization resolution depends on the spatial aperture of the array, that is, the number of sensors. Most of the existing methods use small arrays that can only achieve low-resolution localization results because of limitations such as the amplitude and phase consistency, the complexity and cost of the system. This paper reports the first application of a virtual phased array for gas leakage detection to obtain high-resolution localization results. This method uses a virtual linear ultrasonic sensor array composed of only two sensors to acquire leakage signals. Then, we use the virtual beamforming algorithm based on the cross-power spectrum to estimate the location of the leakage source. Several experiments were conducted to evaluate the effectiveness and operability of the proposed method. The impacts of various factors on the performance of the localization technique are compared and discussed, including factors such as the number of sensors and the distance between the leak hole and virtual array. The results demonstrate that the proposed method accurately and reliably localizes gas leakages.
Gas pressure regulators are widely applied in natural gas pipeline networks, correspondingly, establishing an efficient fault diagnosis approach of regulators plays a critical role in optimizing the safety and reliability of pipeline network systems. In our paper, considering that the outlet pressure signals of gas regulators are nonstationary and nonlinear, we propose a fault diagnosis approach combining a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and fuzzy c-means (FCM) clustering to classify three typical faults of gas regulators. First, we propose to apply the CEEMDAN approach for decomposing intrinsic mode functions (IMFs). Then feature vectors of the typical faults are established by Hilbert marginal spectrum (HMS) of IMFs. Finally, we adopt cluster centers and feature clustering algorithm to distinguish the types of faults. The experimental results indicate the high performance of the present fault diagnosis approach. The membership degrees of test samples obtained from the CEEMDAN algorithm are optimized to be within 0.9 to 1. INDEX TERMS Gas pressure regulators, fault diagnosis, CEEMDAN, feature extraction, spectral analysis, fuzzy c-means clustering.
Automatic digital pressure gauge calibration is challenging due to various unconstrained conditions. Although existing CNN-RNN based methods have been almost perfect on scene text recognition, they fail to perform well on digital pressure gauge calibration that requires to be extremely computationefficient and accurate. In this paper, we propose a light weight fully convolutional sequence recognition network for fast and accurate digital Pressure Gauge Calibration (PGC-Net). PGC-Net integrates feature extraction, sequence modelling and transcription into a unified framework. Experimental results show that PGC-Net runs 28 fps on CPU with 97.41% accuracy. Compared with previous methods, PGC-Net achieves better or comparable performance at lower inference time. Without bells and whistles, PGC-Net is capable of recognizing decimal points that usually appear in pressure gauge images, which evidently verifies the feasibility of PGC-Net. We collected a dataset that contains 17, 240 gauge images with annotated labels for automatic digital pressure gauge calibration. The dataset has been public for future research.
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