Fiber Break Prevention Using Machine Learning Approaches

Abstract

Modern fiber-optic communication systems are built around optical fiber, which allows data to be sent by emitting infrared light pulses. It is widely used by telecommunications firms and is essential to the smooth transmission of information in internet communication as well as the transmission of telephone signals. Nonetheless, optical fibers intrinsic fragility raises a problem, especially in areas where building projects are taking place. Especially nowadays construction-related impact and crushing pressures can cause physical damage that jeopardizes the fiber optic's integrity. Therefore, this research emphasizes the necessity of taking preventative and mitigating actions to reduce the possibilities of fiber optic breakages in response to these difficulties by using machine learning approaches. The data collected by an optical fiber sensor and a distributed acoustic sensing interrogator unit (DAS). Five tools are used to simulate fiber break threats on the road surface and the fiber optic signal is denoised by using the bandpass Butterworth filter. The filtered data is then transformed into spectrogram representation and trained by using the machine learning approaches. The results of the experiments in the research achieves the accuracy 99.78% which is a high accuracy which can be potentially applied in classifying the signals of the tools and preventing the breakage of the fiber optic cables.