Open Access

ARTICLE

Anomaly Diagnosis Using Machine Learning Method in Fiber Fault Diagnosis

Xiaoping Yang^1,2,3, Jinku Qiu^2,3,4, Xifa Gong⁵, Jin Ye⁵, Fei Yao^5,*, Jiaqiao Chen⁶, Xianzan Luo⁶, Da Qin⁶

1 College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin, 541004, China
2 Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, 541004, China
3 Guangxi Engineering Research Center for Optoelectronic Information and Intelligent Communication Technology, Guilin University of Technology, Guilin, 541004, China
4 College of Computer Science and Engineering, Guilin University of Technology, Guilin, 541004, China
5 Guilin G-Link Technology Co., Ltd., Guilin, 541004, China
6 Guilin Saipu Electronic Technology Co., Ltd., Guilin, 541004, China

* Corresponding Author: Fei Yao. Email:

Computers, Materials & Continua 2025, 85(1), 1515-1539. https://doi.org/10.32604/cmc.2025.067518

Received 06 May 2025; Accepted 02 July 2025; Issue published 29 August 2025

Abstract

In contemporary society, rapid and accurate optical cable fault detection is of paramount importance for ensuring the stability and reliability of optical networks. The emergence of novel faults in optical networks has introduced new challenges, significantly compromising their normal operation. Machine learning has emerged as a highly promising approach. Consequently, it is imperative to develop an automated and reliable algorithm that utilizes telemetry data acquired from Optical Time-Domain Reflectometers (OTDR) to enable real-time fault detection and diagnosis in optical fibers. In this paper, we introduce a multi-scale Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) deep learning model for accurate optical fiber fault detection. The proposed multi-scale CNN-BiLSTM comprises three variants: the Independent Multi-scale CNN-BiLSTM (IMC-BiLSTM), the Combined Multi-scale CNN-BiLSTM (CMC-BiLSTM), and the Shared Multi-scale CNN-BiLSTM (SMC-BiLSTM). These models employ convolutional kernels of varying sizes to extract spatial features from time-series data, while leveraging BiLSTM to enhance the capture of global event characteristics. Experiments were conducted using the publicly available OTDR_data dataset, and comparisons with existing methods demonstrate the effectiveness of our approach. The results show that (i) IMC-BiLSTM, CMC-BiLSTM, and SMC-BiLSTM achieve F1-scores of 97.37%, 97.25%, and 97.1%, (ii) respectively, with accuracy of 97.36%, 97.23%, and 97.12%. These performances surpass those of traditional techniques.

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Keywords

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