Cover Image

Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis

Vibration-based signal processing is the most popular and effective approach for fault diagnosis of bearing. In this paper, time-frequency domain analysis, i.e. empirical mode decomposition (EMD) was applied to the raw vibration signal. Intrinsic mode function (IMF) containing the characteristics of...

Full description

Saved in:

Bibliographic Details
Main Authors:	Faysal, Atik, Keng, Ngui Wai, Lim, Meng Hee
Format:	Book Section
Published:	Springer Science and Business Media Deutschland GmbH 2022
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://eprints.utm.my/id/eprint/100528/ http://dx.doi.org/10.1007/978-981-33-4597-3_55
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis
by: Faysal, Atik, et al.
Published: (2022)

Performance evaluation of BPSO & PCA as feature reduction techniques for bearing fault diagnosis
by: Faysal, Atik, et al.
Published: (2021)

Performance Evaluation of BPSO & PCA as Feature Reduction Techniques for Bearing Fault Diagnosis
by: Faysal, Atik, et al.
Published: (2022)

Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis
by: Atik, Faysal, et al.
Published: (2021)

Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis
by: Faysal, Atik, et al.
Published: (2021)

Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis
by: Atik, Faysal
Published: (2022)

Blade fault diagnosis using artificial intelligence technique
by: Ngui, Wai Keng
Published: (2016)

Bayes' theorem for multi-bearing faults diagnosis.
by: Yeo, Siang Chuan, et al.
Published: (2023)

A hybrid method of support vector machine and Dempster-Shafer theory for automated bearing fault diagnosis
by: Lim, Meng Hee, et al.
Published: (2015)

Dempster-shafer evidence theory for multi-bearing faults diagnosis
by: Kar, Hoou Hui, et al.
Published: (2017)

Bearing fault diagnosis using deep sparse autoencoder
by: Saufi, S. R., et al.
Published: (2021)

Diagnosis of twisted blade in rotor system
by: Lim, Meng Hee, et al.
Published: (2015)

Comparative analysis of optimization algorithm on DSAE model for bearing fault diagnosis
by: Saufi, Syahril Ramadhan, et al.
Published: (2021)

Blade fault diagnosis using empirical mode decomposition based feature extraction method
by: Tan, C. Y., et al.
Published: (2019)

An improved image processing approach for machinery fault diagnosis
by: Lim, Meng Hee, et al.
Published: (2018)

Diagnosis of blade fault based on wavelet scalogram and blade pass vibration signature analysis
by: Lim, Meng Hee, et al.
Published: (2015)

Ensemble augmentation for deep neural networks using 1-D time series vibration data
by: Faysal, Atik, et al.
Published: (2023)

Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS
by: Lau, C.K., et al.
Published: (2013)

A hybrid artificial neural network with dempster-shafer theory for automated bearing fault diagnosis
by: Hui, Kar Hoou, et al.
Published: (2016)

Differential evolution optimization for resilient stacked sparse autoencoder and its applications on bearing fault diagnosis
by: Saufi, Syahril Ramadhan, et al.
Published: (2018)

Effectiveness of feature weight using BPSO in text-dependent writer identification
by: Abdl, Khaled Mohammed, et al.
Published: (2015)

Dempster-Shafer evidence theory for automated bearing fault diagnosis
by: Hui, K. H., et al.
Published: (2017)

Blade fault diagnosis using artificial intelligence technique
by: W. K., Ngui
Published: (2016)

Low-speed bearing fault diagnosis based on ArSSAE model using acoustic emission and vibration signals
by: Saufi, Syahril Ramadhan, et al.
Published: (2019)

A thermograph image extraction based on color features for induction motor bearing fault diagnosis monitoring
by: Norliana, Khamisan, et al.
Published: (2015)

Application of wavelet transform analysis for the diagnosis of blade faults in rotating machinery
by: Lim, Meng Hee
Published: (2010)

Bearing fault diagnosis using extreme learning machine based on artificial gorilla troops optimizer
by: Isham, Muhd. Firdaus, et al.
Published: (2023)

Wavelet decomposition for the detection and diagnosis of faults in rolling element bearings
by: Jalel, chebil
Published: (2009)

Training feedforward neural networks for fault diagnosis of ball bearing
by: Tuan Abdul Rahman, Tuan Ahmad Zahidi, et al.
Published: (2018)

A laboratory and field study of industrial gas turbine blade faults diagnosis
by: Lim, Meng Hee
Published: (2003)

Bearing fault diagnosis employing gabor and augmented architecture of convolutional neural network
by: Waziralilah, N. F., et al.
Published: (2019)

Bearing fault diagnosis employing gabor and augmented architecture of convolutional neural network
by: Waziralilah, N. F., et al.
Published: (2019)

Vibration based bearing fault diagnosis identification using neural network
by: Tan, Hwee Mean
Published: (2013)

Analysis of residual wavelet scalogram for machinery fault diagnosis
by: Lim, Meng Hee, et al.
Published: (2014)

Diagnosis of blade fault based on wavelet scalogram and blade pass vibration signature analysis
by: Hee, Lim Meng, et al.
Published: (2015)

Hybrid neural network - genetic algorithms approch for fault diagnosis of bearing system
by: Desa, Mohammad Ishak
Published: (2011)

Diagnosis of distributed faults in outer race of bearings via Park's transformation method
by: Irfan, M., et al.
Published: (2015)

Diagnosis of distributed faults in outer race of bearings via Park's transformation method
by: Irfan, M., et al.
Published: (2015)

Fault detection and monitoring using multiscale PCA
by: Mirin, S. N. S., et al.
Published: (2013)

An Automated Feature Extraction Algorithm for Diagnosis of Gear Faults
by: Irfan, M., et al.
Published: (2019)