A difference-frequency coherent optical time domain reflectometry (DF-COTDR) technique is proposed for distributed acoustic sensing (DAS) systems with coherent detection and ultra-low sampling rate. In this research, we first analyze the power spectrum of the beat signal and propose a method of using local oscillator (LO) light to modulate the frequency to reduce the requirement for high sampling rate. The results show that the corresponding linearity of DFCOTDR reaches 99%. And the demodulation duration is shortened by more than four times. In addition, this method can be generalized to existing COTDR systems with a few modifications.
This research proposes a real-time detection method for the health status of heavy-haul railways based on distributed fiber optic acoustic sensing (DAS). The DAS system detects the wheel-rail acoustic signals using the communication optical cable along the heavy-haul railway, multiple features are mixed to construct an eigenvector and a classifier to realize the typical disaster identification of the heavy-haul railway. The experimental results show that this system can realize the identification and classification of typical track diseases such as rolling contact fatigue (RCF), corrugation, unsupported sleepers on the railway, the achieved average identification rate of disease events is as high as 97.3%, and the identification time of a single event sample is 1ms. This work can achieve real-time detection of track diseases, which can be used as an important basis for workers to maintain and repair. In addition, the DAS system has also successfully monitored the train's running speed and wheel anomalies status information. This work provides a long-term online monitoring method for rail safety operation and maintenance of railway transportation and monitoring of train running status, and does not require any additional sensor arrangement.
Distributed optical fiber acoustic sensing (DAS) can serve as an excellent tool for real-time condition monitoring of a variety of industrial and civil infrastructures. This paper presents a belt conveyor roller fault abnormal monitoring method based on DAS, for the low accuracy and efficiency of the existing belt conveyor rollers fault detection. This method uses the Rayleigh Backscatter of coherent pulsed light to detect and reconstruct the fault signal, and proposes a method based on the combination of power spectrum features and peak detection to recognize and locate abnormal signals under intense background noise. The field test verifies the effectiveness of the real-time monitoring scheme of the industrial conveyor belt system, with a detection accuracy rate of over 87% for simulated fault signals, and a location accuracy of ±2.5 m. It provides a new passive distributed monitoring method for the all-weather structural health monitoring of the rollers in the industrial belt conveyor systems.
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