# Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study

arXiv: https://arxiv.org/abs/2003.03315 · HTML: https://mlsanity.com/paper/2003.03315/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation](https://mlsanity.com/paper/2204.14068/) (2022)
- [Data-driven Machinery Fault Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2405.18843/) (2024)
- [CAFO: Feature-Centric Explanation on Time Series Classification](https://mlsanity.com/paper/2406.01833/) (2024)
- [TFN: An Interpretable Neural Network with Time-Frequency Transform Embedded for Intelligent Fault Diagnosis](https://mlsanity.com/paper/2209.01992/) (2022)
- [A novel asymmetrical autoencoder with a sparsifying discrete cosine Stockwell transform layer for gearbox sensor data compression](https://mlsanity.com/paper/2310.02862/) (2023)
- [BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration](https://mlsanity.com/paper/2307.16363/) (2023)
- [A Closer Look at Bearing Fault Classification Approaches](https://mlsanity.com/paper/2309.17001/) (2023)
- [Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts](https://mlsanity.com/paper/2310.11450/) (2023)
- [Interpreting What Typical Fault Signals Look Like via Prototype-matching](https://mlsanity.com/paper/2403.07033/) (2024)
- [Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis](https://mlsanity.com/paper/2306.14701/) (2023)
- [EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments](https://mlsanity.com/paper/2407.17117/) (2024)
- [Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis](https://mlsanity.com/paper/2312.02826/) (2023)
- [Advancing machine fault diagnosis: A detailed examination of convolutional neural networks](https://mlsanity.com/paper/2502.08689/) (2025)
- [PNN: A Novel Progressive Neural Network for Fault Classification in Rotating Machinery under Small Dataset Constraint](https://mlsanity.com/paper/2503.18263/) (2025)
- [FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments](https://mlsanity.com/paper/2505.06285/) (2025)
- [Syn-Diag: An LLM-based Synergistic Framework for Generalizable Few-shot Fault Diagnosis on the Edge](https://mlsanity.com/paper/2510.05733/) (2025)
- [Physics-Informed Graph Learning with Uncertainty Awareness for Open-Set Domain Generalization in Fault Diagnosis](https://mlsanity.com/paper/2607.04188/) (2026)

## Abstract (arXiv)

With the development of deep learning (DL) techniques, rotating machinery intelligent diagnosis has gone through tremendous progress with verified success and the classification accuracies of many DL-based intelligent diagnosis algorithms are tending to 100%. However, different datasets, configurations, and hyper-parameters are often recommended to be used in performance verification for different types of models, and few open source codes are made public for evaluation and comparisons. Therefore, unfair comparisons and ineffective improvement may exist in rotating machinery intelligent diagnosis, which limits the advancement of this field. To address these issues, we perform an extensive evaluation of four kinds of models, including multi-layer perception (MLP), auto-encoder (AE), convolutional neural network (CNN), and recurrent neural network (RNN), with various datasets to provide a benchmark study within the same framework. We first gather most of the publicly available datasets and give the complete benchmark study of DL-based intelligent algorithms under two data split strategies, five input formats, three normalization methods, and four augmentation methods. Second, we integrate the whole evaluation codes into a code library and release this code library to the public for better development of this field. Third, we use specific-designed cases 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.