# A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis

arXiv: https://arxiv.org/abs/2202.08034 · HTML: https://mlsanity.com/paper/2202.08034/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2022.

## Built on by (most cited first, 3 of 3)

- [Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks](https://mlsanity.com/paper/2203.11727/) (2022)
- [Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory](https://mlsanity.com/paper/2203.12604/) (2022)
- [Evasion of IoT Malware Detection via Dummy Code Injection](https://mlsanity.com/paper/2602.08170/) (2026)

## Abstract (arXiv)

A novel multitask learning approach based on stacked bidirectional long short-term memory (BiLSTM) networks and convolutional neural networks (CNN) for detecting, locating, characterizing, and identifying fiber faults is proposed. It outperforms conventionally employed techniques.