to point out the existing issues, including class imbalance, generalization ability, interpretability, few-shot learning, and model selection. By these works, we release a unified code framework for comparing and testing models fairly and quickly, emphasize the importance of open source codes, provide the baseline accuracy (a lower bound) to avoid useless improvement, and discuss potential future directions in this field. The code library is available at <https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark>.'
address: 'School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China'
author:
- Zhibin Zhao
- Tianfu Li
- Jingyao Wu
- Chuang Sun
- Shibin Wang
- Ruqiang Yan
- Xuefeng Chen
bibliography:
- 'References/Reference.bib'
title: 'Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study'
---

Deep learning ,machinery fault diagnosis ,open source codes ,benchmark study

Introduction {#S:1}
============

Prognostics health management (PHM) is one of the most essential systems in modern industrial equipment, such as helicopter, aero-engine, wind turbine, and high speed train. The main function of PHM systems used in rotating machinery is intelligent fault diagnosis for condition-based maintenance. Intelligent fault diagnosis is the key component of PHM systems and has been studied widely. Traditional intelligent diagnosis methods mainly consist of the feature extraction using various signal 