Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

By Chen Lu, Zhenya Wang, Wei-Li Qin, Jian Ma. Signal Processing, 2017

723 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/1195a6bdb83368b186ec6d4580c66a676549837e

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. Liang Gao
2. Xinyu Li
3. Long Wen
4. Shen Zhang
5. Bingnan Wang
6. Shibo Zhang
7. Thomas G. Habetler
8. Changhong Yan

In Mechanics of Materials

1. Liang Gao
2. Xinyu Li
3. Long Wen
4. Hongyan Tao
5. Yong Guan
6. Ran Zhang
7. Shen Zhang
8. Lifeng Wu

Citations in the corpus

• Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox (2019, background): “Different deep models such as stacked denoising autoencoders [15], deep belief network [16], sparse filtering [17], convolutional neural networks (CNNs) [18], and recurrent neural network [19] have demonstrated state-of-the-art diagnosis performance com-”
• Machine learning methods for wind turbine condition monitoring: A review (2019, neutral): “general rotary machineries [20], [21]).”
• Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning (2018, methodology): “example, DNN is employed to deal with the raw data [36]–[39] and the transform domain data [40].”
• A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method (2018, neutral): “ing auto-encoder [14], [15], and sparse filtering [16].”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “Due to this limitation, some papers applied stacked denoising AE (SDAE) [127], [128], [137] that can effectively increase accuracy for bearing fault iden-tification with a small signal to noise ratio (SNR), i.e., SNR = 5 or 10.”
• Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery (2018, neutral): “…in Figure 5, for ELM, PNN and SVM, an obvious downward trend subsequently appears when the SNR is lower than 22 dB, while random forest still performs significantly well in a wider range of SNR owing to the strong anti-noise ability of bagging algorithm used in random forest (Lu et al., 2017).”