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.

Transducer components are crucial in optimizing the sensitivity of microphones. Cantilever structure is commonly used as a structural optimization technique. Here, we present a novel Fabry-Perot (F-P) interferometric fiber-optic microphone (FOM) using a hollow cantilever structure. The proposed hollow cantilever aims to reduce the effective mass and spring constant of the cantilever, thereby enhancing the sensitivity of the FOM. Experimental results demonstrate that the proposed structure outperforms the original cantilever design in terms of sensitivity. The sensitivity and minimum detectable acoustic pressure level (MDP) can reach 91.40 mV/Pa and 6.20 µPa/Hz at 1.7 kHz, respectively. Notably, the hollow cantilever provides an optimization framework for highly sensitive FOMs.

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Video security monitoring has become the focus of social research and development; however, since the camera cannot automatically rotate, there is a blind spot in traditional security monitoring. Considering the abnormal often happens accompanied by corresponding sounds (e.g., where there is an explosion , there will be the sound of explosions), therefore, for compensating the blind spot , the auditory function can be added to the camera to track the direction of sound source automatically which requires the two-dimensional (2-D) localization of sound source to complete , at the mean time , the localization algorithm should be capable of tracking all of the source signals ,as well as be real-time to make the video tracking to be achieved by turning the camera toward sound source timely. This paper realizes the localization of wideband speech signal in video monitoring by using modern signal processing method, linear microphone array, positioning thought based on time delay estimation, frequency domain transform, and spectrum-search method based on energy value. Both the early simulation and late DSP-based embedded system platform have verified the feasibility of the method.