ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES

By Biswanath Samanta, K. R. Al-Balushi. 2003

786 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/3a56e0e4943434e22477487c604942f14bcc5347

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. Jay H. Lee
3. Yanyang Zi
4. Po-Chun Chen
5. Zhengjia He
6. G. You
7. Chih-Chiang Wei
8. Yuan-Heng Wang

In Mechanical Engineering

1. Yaguo Lei
2. Yanyang Zi
3. Po-Chun Chen
4. Zhengjia He
5. G. You
6. Chih-Chiang Wei
7. Yuan-Heng Wang
8. Jianbo Yu

Citations in the corpus

• Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan (2017, methodology): “…is the order of the mother wavelet function, which was determined in previous papers using trialand-error methods based on the intrinsic characteristics of the data (Wang and McFadden, 1995; Samanta and Al-Balushi, 2003; Peter et al., 2004; Liu, 2005; Kar and Mohanty, 2006; Rafiee et al., 2007).”
• A new approach to intelligent fault diagnosis of rotating machinery (2008, background): “Demodulation detection makes the diagnosis process a little more independent of a particular machine since it focuses on the low-amplitude high-frequency broadband signals characterising bearing conditions (Benko, Petrovčič, Juričic´, Tavčar, & Rejec, 2005; Samanta & Al-Balushi, 2003).”
• Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods (2007, background): “Some have classified bearing conditions and fault diagnosis using fuzzy logic concepts [6], and neural networks approaches [7–9], while others have focused on wavelet analysis approaches to detect bearing faults [2,3,10,11].”
• Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures (2020, neutral): “ANNs [71] and SVMs [72] are two typical machine learning algorithms.”