Diagnosing simultaneous faults using the local regularity of vibration signals
|Author:||Nissilä, Juhani1; Laurila, Jouni1|
1Intelligent Machines and Systems, Faculty of Technology, P.O. BOX 4200, FI-90014, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 11.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202001131835
|Publish Date:|| 2020-03-05
The regularity of the vibration signals measured from a rotating machine is often affected by the condition of the machine. The fractional order of regularity can be measured using the definition of Hölder continuity. In this paper, we review the connection between the pointwise Hölder regularity of a signal and its wavelet transform. We calculate the wavelet transform modulus of acceleration measurements from a test rig. The effects of different faults were recorded, such as unbalance, the coupling misalignment of a claw clutch, the absence of lubrication in a ball bearing, the absence of the bearing’s cage, and their combinations. An analysis of the estimated isolated pointwise regularities from the wavelet transform modulus maxima ridges shows that the faults often cause irregularities in the signals and that their locations and frequencies can be used in diagnosing the faults. Coupling misalignment and the absence of lubrication in a ball bearing both cause impact-like vibrations, but these impacts have positive and negative regularities in the case of a coupling misalignment and mainly negative in the case of a dry bearing. Unbalance is best diagnosed from the integrals of the acceleration signals using traditional methods. In diagnosing the misalignment, bearing problems and simultaneous faults, the local regularity analysis outperforms the use of high order norms of differentiated acceleration measurements (i.e. jerk and snap signals). Using just three features (the number of local irregularities in an acceleration signal, their mean Hölder regularity and the arithmetic mean of the absolute values of a velocity signal), a quadratic classifier can be constructed whose estimated classification error is only 0.3%.
Measurement science and technology
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
112 Statistics and probability
213 Electronic, automation and communications engineering, electronics
214 Mechanical engineering
For the first half of the year 2018, the first author's research has been funded by the Finnish Cultural Foundation, North Ostrobothnia Regional Fund. The first author also wishes to thank the Auramo-Foundation, Riitta and Jorma J. Takanen Foundation and Walter Ahlström Foundation for their support for his doctoral studies.
© 2019 IOP Publishing Ltd. This is an Accepted Manuscript version of an article published in Measurement Science and Technology. The Definitive Version of Record can be found online at: https://doi.org/10.1088/1361-6501/aaf8fa.