The benefits of applying the vibration analysis program are well known and have been so for decades. A large number of contributions have been produced discussing new diagnostic, signal treatment, technical parameter analysis, and prognosis techniques. However, to obtai... View more
Bari, H. M., Deshpande, A. A., Patil, S. S., Sinha, J. K.. Availability improvement by early detection of motor bearing failure using comprehensive condition monitoring techniques at DTPS.
Vibration Engineering and Technology of Machinery: Proceedings of VETOMAC X 2014, held at the University of Manchester, UK, September 9–11, 2014. 2015; 23: 1101-1111
López-Escobar, C., González-Palma, R., Almorza, D., Mayorga, P., Carnero, M. C.. Statistical quality control through process self-induced vibration spectrum analysis.
International Journal of Advanced Manufacturing Technology. 2012; 58 (9–12): 1243-1259
Precup, R.-E., Angelov, P., Costa, B. S. J., Sayed-Mouchaweh, M.. An overview on fault diagnosis and nature-inspired optimal control of industrial process applications.
Computers in Industry. 2015; 74: 75-94
Jardine, A. K. S., Lin, D., Banjevic, D.. A review on machinery diagnostics and prognostics implementing condition-based maintenance.
Mechanical Systems and Signal Processing. 2006; 20 (7): 1483-1510
Lee, W. G., Lee, J. W., Hong, M. S., Nam, S.-H., Jeon, Y., Lee, M. G.. Failure diagnosis system for a ball-screw by using vibration signals.
Shock and Vibration. 2015; 2015-9
Carnera, M. C.. Selection of diagnostic techniques and instrumentation in a predictive maintenance program. A case study.
Decision Support Systems. 2005; 38 (4): 539-555
Yang, Z.. Automatic condition monitoring of industrial rolling-element bearings using Motor's vibration and current analysis.
Shock and Vibration. 2015; 2015-12
Mahmood, S. T..
Use of vibrations analysis technique in condition based maintenance [Ph.D. thesis]. 2011
Qu, J., Zhang, Z., Gong, T.. A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion.
Neurocomputing. 2016; 171 (1): 837-853
Costa, B. S. J., Angelov, P. P., Guedes, L. A.. Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier.
Neurocomputing. 2015; 150: 289-303