Journal ArticleDOI
Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN
TLDR
The proposed fault diagnosis technique based on acoustic emission (AE) analysis with the Hilbert-Huang Transform (HHT) and data mining tool can increase reliability for the faults diagnosis of ball bearing.Abstract:
This paper presents a fault diagnosis technique based on acoustic emission (AE) analysis with the Hilbert-Huang Transform (HHT) and data mining tool HHT analyzes the AE signal using intrinsic mode functions (IMFs), which are extracted using the process of Empirical Mode Decomposition (EMD) Instead of time domain approach with Hilbert transform, FFT of IMFs from HHT process are utilized to represent the time frequency domain approach for efficient signal response from rolling element bearing Further, extracted statistical and acoustic features are used to select proper data mining based fault classifier with or without filter K-nearest neighbor algorithm is observed to be more efficient classifier with default setting parameters in WEKA APF-KNN approach, which is based on asymmetric proximity function with optimize feature selection shows better classification accuracy is used Experimental evaluation for time frequency approach is presented for five bearing conditions such as healthy bearing, bearing with outer race, inner race, ball and combined defect The experimental results show that the proposed method can increase reliability for the faults diagnosis of ball bearingread more
Citations
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Journal ArticleDOI
Artificial intelligence for fault diagnosis of rotating machinery: A review
TL;DR: This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications.
Journal ArticleDOI
Applications of machine learning to machine fault diagnosis: A review and roadmap
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
Journal ArticleDOI
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
TL;DR: A novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN), which can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.
Journal ArticleDOI
A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
Akhand Rai,S. H. Upadhyay +1 more
TL;DR: In this article, the authors have presented the various signal processing methods applied to the fault diagnosis of rolling element bearings with the objective of giving an opportunity to the examiners to decide and select the best possible signal analysis method as well as the excellent defect representative features for future application in the prognostic approaches.
Journal ArticleDOI
Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks
TL;DR: In this article, a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery is presented, which incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy.
References
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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
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A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings
Naresh Tandon,A. Choudhury +1 more
TL;DR: Vibration measurement in both time and frequency domains along with signal processing techniques such as the high-frequency resonance technique have been covered and recent trends in research on the detection of defects in bearings have been included.
Journal ArticleDOI
Artificial neural network based fault diagnostics of rolling element bearings using time-domain features
TL;DR: The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing, leading to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.
Journal ArticleDOI
A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size
Abdullah M. Al-Ghamd,David +1 more
TL;DR: In this paper, an experimental investigation reported in this paper was centred on the application of the acoustic emission (AE) technique for identifying the presence and size of a defect on a radially loaded bearing.
Proceedings Article
Metric Learning to Rank
Brian McFee,Gert R. G. Lanckriet +1 more
TL;DR: A general metric learning algorithm is presented, based on the structural SVM framework, to learn a metric such that rankings of data induced by distance from a query can be optimized against various ranking measures, such as AUC, Precision-at-k, MRR, MAP or NDCG.
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