Abstract
In response to mechanical fault in feature extraction problem, this paper presents a Fisher discrimination sparse coding method. This method is achieved by optimizing an objective function that includes two steps. First, this objective function works well in denoising where signals need to be reconstructed. Second, another objective function is added to the sparse coding framework, the discrimination power of the Fisher discriminative methods with the reconstruction property, and the sparsity of the sparse representation that can deal with the fault signal which is corrupted. Finally, the feature is extracted. In rolling bearing fault classification experiments, the new method improves the accuracy of classification.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
He Z, Chen J, Wang T Theory and application of mechanical fault diagnosis. Higher Education Press first edition
Jiang H, He Z, Duan C, Chen X (2005) Wavelet construction based on lifting scheme and incipient fault feature extraction. J Xi’an Jiaotong Univ 39(5)
Jiang H, Dou D, He Z (2011) A new method for identifying ultrasonic echo signal features using adaptive second generation wavelet. J Northwest Polytechnical Univ 29(1)
Liu HN, Liu CL, Huang YX (2011) Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mech Syst Signal Process 25(2):558–574
Shi J, Jiang Z Elastic net sparse coding-based sparse object recognition. Acta Aeronaut et Astronaut Sin 34(5):1129–1139
Zhu Q, Yang Y, Huang M (2013) Bearing fault diagnosis based on a kernel-mapping sparse representation classification. J Vib Shock 32(11):30–34
Chen X, Du Z, Li J, Li X, Zhang H (2013) Compressed sensing based on dictionary learning for extracting impulse components. Signal Process 6 (online)
Lee H, Battle A, Raina R, Ng AY (2006) Efficient sparse coding algorithm. In: Proceedings of conference on neural information processing system, 2006
The Case Western Reserve University Bearing Data Center. Bearing data center fault test data [EB/OL]. http://www.eecs.cwru.edu/laboratory/bearing (2011-01-01)
Lei Y, He Z, Zi Y (2008) A new approach to intelligent fault diagnosis of rotating machinery. Expert Syst Appl 35(4):1593–1600
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, C., Wang, Z., Ding, C. (2014). Fault Diagnosis of Rolling Bearing Based on Fisher Discrimination Sparse Coding. In: Wang, J. (eds) Proceedings of the First Symposium on Aviation Maintenance and Management-Volume II. Lecture Notes in Electrical Engineering, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54233-6_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-54233-6_43
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54232-9
Online ISBN: 978-3-642-54233-6
eBook Packages: EngineeringEngineering (R0)