Induction motor fault classification via entropy and column correlation features of 2D represented vibration data
出版年份 2021 全文链接
标题
Induction motor fault classification via entropy and column correlation features of 2D represented vibration data
作者
关键词
-
出版物
Eksploatacja i Niezawodnosc-Maintenance and Reliability
Volume 23, Issue 1, Pages 132-142
出版商
Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
发表日期
2021-01-04
DOI
10.17531/ein.2021.1.14
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Engine valve clearance diagnostics based on vibration signals and machine learning methods
- (2020) Maciej Tabaszewski et al. Eksploatacja i Niezawodnosc-Maintenance and Reliability
- Curvature enhanced bearing fault diagnosis method using 2D vibration signal
- (2020) Weifang Sun et al. Journal of Mechanical Science and Technology
- A Technique for Frequency Converter-Fed Asynchronous Motor Vibration Monitoring and Fault Classification, Applying Continuous Wavelet Transform and Convolutional Neural Networks
- (2020) Tomas Zimnickas et al. Energies
- A novel bearing fault diagnosis method based on 2-D image representation and transfer learning–convolutional neural network
- (2019) Ping Ma et al. MEASUREMENT SCIENCE and TECHNOLOGY
- Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input
- (2019) Dongdong Zhao et al. SENSORS
- A New Fault Diagnosis Method of Bearings Based on Structural Feature Selection
- (2019) Wentao Mao et al. Electronics
- Entropy Based Fault Classification Using the Case Western Reserve University Data: A Benchmark Study
- (2019) Yongbo Li et al. IEEE TRANSACTIONS ON RELIABILITY
- Fault Detection for Vibration Signals on Rolling Bearings Based on the Symplectic Entropy Method
- (2017) et al. Entropy
- Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms
- (2017) Purushottam Gangsar et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions
- (2016) Sheraz Ali Khan et al. SHOCK AND VIBRATION
- Recognition of acoustic signals of induction motor using FFT, SMOFS-10 and LSVM
- (2015) Adam Głowacz Eksploatacja i Niezawodnosc-Maintenance and Reliability
- Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach
- (2015) Muhammad Amar et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- A systematic study of ball passing frequencies based on dynamic modeling of rolling ball bearings with localized surface defects
- (2015) Linkai Niu et al. JOURNAL OF SOUND AND VIBRATION
- Sound based induction motor fault diagnosis using Kohonen self-organizing map
- (2014) Emin Germen et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins
- (2013) Guangya Zhang et al. COMPUTATIONAL BIOLOGY AND CHEMISTRY
- Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis
- (2013) Md Rifat Shahriar et al. EURASIP Journal on Image and Video Processing
- Multi-sensor data fusion using support vector machine for motor fault detection
- (2012) Tribeni Prasad Banerjee et al. INFORMATION SCIENCES
- Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two-Dimension Domain
- (2011) Van Tuan Do et al. STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING
- Comparison between vibration and stator current analysis for the detection of bearing faults in asynchronous drives
- (2010) B. Trajin et al. IET Electric Power Applications
- Induction machine fault detection using clone selection programming
- (2008) Zhaohui Gan et al. EXPERT SYSTEMS WITH APPLICATIONS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started