A lean model for performance assessment of machinery using second generation wavelet packet transform and Fisher criterion

The development of efficient on-line data processing and decision support algorithms is one of future trends of expert systems for machine condition monitoring research. This paper contributes to a lean model for machine performance assessment by combining an efficient signal processing algorithm, an effective feature selection criterion, and an intelligent assessment method. In the proposed model, firstly, a second generation wavelet packet transform is used to project raw signals into the wavelet domain; secondly, the Fisher criterion is applied to reduce redundant dimensions; eventually, a fuzzy c-means clustering method is used to assess and classify the performance of mechanical systems. The vibration signals from a rolling element bearing experiment has been used to verify both efficiency and effectiveness of the lean model. Compared with conventional methods, the lean model can reduce the time consumption of feature extraction by 49.7% and storage space or data transfer load related to the feature dimensionality by 97.7%, which indicates a great improvement in efficiency.

[1]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[2]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing, 2nd Edition , 1999 .

[3]  Jun Wang,et al.  Real-time tool condition monitoring using wavelet transforms and fuzzy techniques , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[4]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[5]  R. Keith Mobley,et al.  An introduction to predictive maintenance , 1989 .

[6]  S. Poyhonen,et al.  Signal processing of vibrations for condition monitoring of an induction motor , 2004, First International Symposium on Control, Communications and Signal Processing, 2004..

[7]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[8]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[9]  Zhongxiao Peng,et al.  Expert system development for vibration analysis in machine condition monitoring , 2008, Expert Syst. Appl..

[10]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[11]  Jian-Da Wu,et al.  Investigation of engine fault diagnosis using discrete wavelet transform and neural network , 2008, Expert Syst. Appl..

[12]  Jay Lee,et al.  Intelligent prognostics tools and e-maintenance , 2006, Comput. Ind..

[13]  Jay Lee,et al.  Robust performance degradation assessment methods for enhanced rolling element bearing prognostics , 2003, Adv. Eng. Informatics.

[14]  Irene Yu-Hua Gu,et al.  Support Vector Machine for Classification of Voltage Disturbances , 2007, IEEE Transactions on Power Delivery.

[15]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[16]  Wim Sweldens,et al.  The lifting scheme: a construction of second generation wavelets , 1998 .

[17]  Jay Lee,et al.  Degradation Assessment and Fault Modes Classification Using Logistic Regression , 2005 .

[18]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[19]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[20]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[21]  Chen Peng,et al.  Gearbox fault diagnosis using adaptive redundant Lifting Scheme , 2006 .

[22]  I. Daubechies,et al.  Factoring wavelet transforms into lifting steps , 1998 .

[23]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .