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

By Zhibin Zhao, Tianfu Li, Jingyao Wu, Chuang Sun, Shibin Wang, Ruqiang Yan, Xuefeng Chen. ISA transactions, 2020

588 citations recorded by the source corpus.

Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through tremendous progress, which can help reduce costly breakdowns. However, different datasets and hyper-parameters are recommended to be used, and few open source codes are publicly available, resulting in unfair comparisons and ineffective improvement. To address these issues, we perform a comprehensive evaluation of four models, including multi-layer perception (MLP), auto-encoder (AE), convolutional neural network (CNN), and recurrent neural network (RNN), with seven datasets to provide a benchmark study. We first gather nine publicly available datasets and give a comprehensive benchmark study of DL-based models with 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 it to the public for better comparisons. Third, we use specific-designed cases to point out the existing issues, including class imbalance, generalization ability, interpretability, few-shot learning, and model selection. Finally, 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), and discuss existing issues in this field. The code library is available at: https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark.

Subfields: Control and Systems Engineering, Mechanics of Materials, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/50dfbac5377d8debe5a2b413a0e16b2e65449806

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Chuang Sun
2. Xuefeng Chen
3. Ruqiang Yan
4. Shibin Wang
5. Zhibin Zhao

In Mechanics of Materials

1. Xuefeng Chen
2. Ruqiang Yan
3. Chuang Sun

Citations in the corpus

• WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis (2019, unknown): “In our recent work [22], we tested the performance of these deep neural networks in some open-source fault diagnosis datasets, the”
• Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study (2019, methodology): “Effective DL models, such as Convolutional Neural Network (CNN) [15], Sparse Autoencoder (SAE) [16], etc., for tasks in PHM have been validated successfully in current research, and a benchmark study is also given in [17] for better comparison and development.”