Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference

By Long Zhang, G. Xiong, Hesheng Liu, Huijun Zou, Weizhong Guo. Expert systems with applications, 2010

334 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/56edb503c73b696ffca7f2c6c6d57edda91e96e0

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. Changhong Yan
2. Jun Yan
3. Ping Xie
4. Guoqian Jiang
5. Jiangtao Wen
6. Haibo He
7. Jiedi Sun
8. Pingfeng Wang

In Mechanical Engineering

1. Haidong Shao
2. Hongkai Jiang
3. Zhuyun Chen
4. Weihua Li

Citations in the corpus

• Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning (2018, methodology): “With regard to the method for fault classification, some artificial intelligence methods are adopted [15], such as artificial neural network (ANN), support vector machine (SVM) [13], [16], [17], k-nearest neighbor [18], fuzzy inference system [11], and random forest [19].”
• Time Series Analysis Using Composite Multiscale Entropy (2013, background): “…analyses of the human gait dynamics [2], heart rate variability [3,4], electroencephalogram [5], postural control [6], vibration of rotary machine [7,8], rainfall time series [9], time series of river flow [10], electroseismic time series [11], time series of traffic flow [12], social dynamics…”
• Failure diagnosis using deep belief learning based health state classification (2013, methodology): “Zhang [38] proposed a bearing fault diagnosis methodology using multi-scale entropy (MSE) and adaptive neuro-fuzzy inference system.”
• Dispersion Entropy: A Measure for Time-Series Analysis (2016, methodology): “Hence, it is very important to identify the existence and severity of fault in order to plan for the repairs [23], [24].”
• Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox (2019, methodology): “MSE is used for calculating the sample entropy over multiple coarse-grained signals from different time scales of a raw vibration signal.”
• Hierarchical diversity entropy for the early fault diagnosis of rolling bearing (2021, methodology): “Then, the adaptive neuro-fuzzy inference system was used to automatically recognize different bearing fault [9].”