An interactive method for predicting industrial equipment defects

As a major aspect of vibratory verification and the recognition of imperfections and defects in rotating machines, gears have been the subject of much research. These elements are very requested and likely to present defects which evolve rapidly to the rupture. An approach is provided for fault identification on the premise of time-frequency signal analysis techniques. It is shown that the new technique is significant of the energy concentration on the instantaneous frequency of the individual components in the vibration signal, which allows monitoring of the signal amplitude and frequency modulation with a high degree of accuracy and in a small range of frequencies. The analysis of gear defects made on the measured signals can link the observed effects of vibration to material causes that generate them and provided a very powerful tool for maintenance purposes, especially in industry where competition is expressed by the quality and the costs. The main objective of this work is to improve the gear safety, the identification, and the development of vibration analysis techniques for the detection and diagnosis of defects in critical security on the systems gear transmission. To confirm the theoretical results, we have taken an example of furnace gear reducer.

[1]  Yu Zhang,et al.  Incipient Fault Diagnosis of Roller Bearing Using Optimized Wavelet Transform Based Multi-Speed Vibration Signatures , 2017, IEEE Access.

[2]  Diego Cabrera,et al.  Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning , 2016, Sensors.

[3]  Michael J. Brennan,et al.  An Illustration of Some Methods to Detect Faults in Geared Systems Using a Simple Model of Two Meshed Gears , 2017 .

[4]  Mohamed El Badaoui,et al.  Contribution au Diagnostic Vibratoire des Réducteurs Complexes à Engrenages par l'Analyse Cepstrale , 1999 .

[5]  Babak Eftekharnejad,et al.  Seeded fault detection on helical gears with acoustic emission , 2009 .

[6]  Vice President,et al.  An Introduction to Time Waveform Analysis , 1999 .

[7]  M. Elforjani,et al.  Condition monitoring of worm gears , 2012 .

[8]  Hou Chunguang,et al.  Analysis on vibration and acoustic joint mechanical fault diagnosis of high voltage vacuum circuit based on wavelet packet energy relative entropy , 2016, 2016 27th International Symposium on Discharges and Electrical Insulation in Vacuum (ISDEIV).

[9]  Komi Midzodzi Pekpe,et al.  Vibration-based fault detection of meshing shafts , 2015 .

[10]  M. Zuo,et al.  Gearbox fault detection using Hilbert and wavelet packet transform , 2006 .

[11]  Marc Thomas,et al.  On the use of time synchronous averaging, independent component analysis and support vector machines for bearing fault diagnosis , 2007 .

[12]  G. Diana.  Diagnostics of rotating machines in power plants : proceedings of the CISM/IFToMM Symposium, October 27-29, 1993, Udine, Italy , 1994 .

[13]  Anand Parey,et al.  Gearbox fault diagnosis under non-stationary conditions with independent angular re-sampling technique applied to vibration and sound emission signals , 2017, Applied Acoustics.

[14]  E. P. de Moura,et al.  Applications of detrended-fluctuation analysis to gearbox fault diagnosis , 2009 .

[15]  Alessandro Paolo Daga,et al.  Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine , 2017 .

[16]  Xiaodong Jia,et al.  A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery , 2017 .