Journal ArticleDOI
An Efficient Hilbert–Huang Transform-Based Bearing Faults Detection in Induction Machines
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TLDR
In this article, the authors proposed a bearing fault detection method based on stator currents analysis using the Hilbert-Huang transform (HHT) and empirical mode decomposition (EMD).Abstract:
This paper focuses on rolling elements bearing fault detection in induction machines based on stator currents analysis. Specifically, it proposes to process the stator currents using the Hilbert–Huang transform. This approach relies on two steps: empirical mode decomposition and Hilbert transform. The empirical mode decomposition is used in order to estimate the intrinsic mode functions (IMFs). These IMFs are assumed to be mono-component signals and can be processed using demodulation technique. Afterward, the Hilbert transform is used to compute the instantaneous amplitude (IA) and instantaneous frequency (IF) of these IMFs. The analysis of the IA and IF allows identifying fault signature that can be used for more accurate diagnosis. The proposed approach is used for bearing fault detection in induction machines at several fault degrees. The effectiveness of the proposed approach is verified by a series of simulation and experimental tests corresponding to different bearing fault conditions. The fault severity is assessed based on the IMFs energy and the variance of the IA and IF of each IMF.read more
Citations
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Journal ArticleDOI
Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network.
TL;DR: The experimental results demonstrate that the DLSTM model has a competitive performance in comparison with state-of-the-arts reported in literatures and other neural network models.
Journal ArticleDOI
Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines
TL;DR: A novel three-step degradation data-driven TTF prognostics approach for rolling element bearings (REBs) in electrical machines to show a more accurate prediction of TTF than the existing major approaches.
Journal ArticleDOI
A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis
Jia Luo,Jinying Huang,Hongmei Li +2 more
TL;DR: An imbalanced fault diagnosis method based on the generative model of conditional-deep convolutional generative adversarial network (C-DCGAN) is presented and could improve the accuracy of fault diagnosis and the generalization ability of the classifier in the case of small samples and display better fault diagnosis performance.
Journal ArticleDOI
Distinct Bearing Faults Detection in Induction Motor by a Hybrid Optimized SWPT and aiNet-DAG SVM
TL;DR: A novel hybrid approach based on Optimized Stationary Wavelet Packet Transform for feature extraction and artificial immune system nested within support vectors machines for fault classification and the motor current signatures analysis offers a cost-effective method for BFD is proposed.
Journal ArticleDOI
Faulty bearing detection, classification and location in a three-phase induction motor based on Stockwell transform and support vector machine
Megha Singh,Abdul Gafoor Shaik +1 more
TL;DR: F faulty bearing detection, classification and its location in a three-phase induction motor using Stockwell transform and Support vector machine is presented.
References
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Book
Pulse Width Modulation for Power Converters: Principles and Practice
D. Grahame Holmes,Thomas A. Lipo +1 more
TL;DR: In this paper, an integrated and comprehensive theory of PWM is presented and the selection of the best algorithm for optimum pulse width modulation is an important process that can result in improved converter efficiency, better load (motor) efficiency, and reduced electromagnetic interference.
Book
Introduction to spectral analysis
Petre Stoica,Randolph L. Moses +1 more
TL;DR: This chapter presents a meta-analyses of the nonparametric methods used in the construction of the Cramer-Rao Bound Tools, which were developed in the second half of the 1990s to address the problem of boundedness in the discrete-time model.
On empirical mode decomposition and its algorithms
TL;DR: Empirical Mode Decomposition is presented, and issues related to its effective implementation are discussed, and an interpretation of the method in terms of adaptive constant-Q filter banks is supported.
Book
Communication systems and techniques
TL;DR: An introductory, graduate-level look at modern communications in general and radio communications in particular, with valuable insights into the fundamental concepts underlying today's communications systems, especially wireless communications.