Investigation on Spectral Structure of Gearbox Vibration Signals by Principal Component Analysis for Condition Monitoring Purposes

Spectral analysis is well-established analysis of vibrations used in diagnostics both in academia and industry. In general, one may identify components related to particular stages in the gearbox and analyze amplitudes of these components with a simple rule for decision-making: if amplitudes are increasing the condition becomes worse. However, usually one should analyze not single amplitude but at least several components, but: how to analyze them simultaneously? We have provided an example (case study) for planetary gearboxes in good and bad conditions (case B and case A). As diagnostic features we have used 15 amplitudes of spectral components related to fundamental planetary mesh frequency and its harmonics. Using Principal Component Analysis (PCA), it has been shown that amplitudes don't vary in the same way; change of condition affects not only amplitudes of all components in that sense, but also relation between them. We have investigated geometry of the data and it has been shown that the proportions of the explained total inertia of the three data sets ("good", "bad" and mixed good/bad) are different. We claim that it may be a novel diagnostic approach to employ multidimensional analysis for accounting not only directly observed values but also interrelations both within and between the two groups of data. Different structure of the data is associated with different condition of the machines and such assumption is specified for the first time in the literature. Obviously it requires more studies.