Bearing fault diagnosis based on wavelet transform and fuzzy inference

By X. Lou, K. Loparo. 2004

676 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/f239581bc0921874a16df9750259a9f33c2d4896

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. Yaguo Lei
2. Ruqiang Yan
3. Xuefeng Chen
4. Robert X. Gao
5. Jing Lin
6. Feng Jia
7. Steven X. Ding
8. Qing Zhao

In Mechanical Engineering

1. Yaguo Lei
2. Xuefeng Chen
3. Ruqiang Yan
4. Robert X. Gao
5. Jing Lin
6. Steven X. Ding
7. Qing Zhao
8. Geoff McDonald

Citations in the corpus

• An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data (2016, methodology): “The motor bearing signals provided by Case Western Reserve University [39] are analyzed in this section.”
• Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings (2015, methodology): “In the area of TF features, signal transforms such as the wavelet transform [7], [9], empirical mode decomposition [40], and TF distributions have been used [43].”
• Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference (2009, neutral): “Some examples of the combined algorithms are ANFIS with genetic algorithms (Lei et al., 2007) and ANFIS with wavelet transform (Lou and Loparo, 2004) for bearing fault diagnosis.”
• Wavelets for fault diagnosis of rotary machines: A review with applications (2014, background): “Lou and Laparo combined the DWT and an adaptive neural fuzzy inference system (ANFIS) to separate different bearing fault conditions subject to varying loads [96].”
• A new approach to intelligent fault diagnosis of rotating machinery (2008, methodology): “Successful implementations of ANFIS have been reported in the medical field (Gü ler & U ¨ beyli, 2005; U ¨ beyli & Gü ler, 2005), chemical field (Gulbag & Temurtas, 2006; Lo & Lin, 2005) and fault diagnosis (Lou & Loparo, 2004; Ye, Sadeghian, & Wu, 2006).”
• Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019, methodology): “To verify the performance of our few-shot learning algo-rithm for limited data fault diagnosis, we selected 12k drive end bearing fault data in the Case Western Reserve University (CWRU) Bearing Datasets [44], [45] as the original experiments data.”
• Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection (2017, background): “Often these me thods are combined together [19, 8], and are often used in conjunction with machine learning as features to identify faults [3, 21, 22, 23].”
• Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods (2007, background): “Hybrid approaches, that combine neural networks and fuzzy logic, are also used for monitoring and diagnostic purposes [3,12].”