TY  - EJOU
AU  - Yang, Xiaoping 
AU  - Qiu, Jinku 
AU  - Gong, Xifa 
AU  - Ye, Jin 
AU  - Yao, Fei 
AU  - Chen, Jiaqiao 
AU  - Luo, Xianzan 
AU  - Qin, Da 

TI  - Anomaly Diagnosis Using Machine Learning Method in Fiber Fault Diagnosis
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 85
IS  - 1
SN  - 1546-2226

AB  - 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.
KW  - Multiscale; BiLSTM; OTDR; multiclass classification; machine learning; fiber fault

DO  - 10.32604/cmc.2025.067518