Vibration analysis is the most widely used tool in industrial application for machine’s
health condition assessment. Bearings, however, are very sensitive and require special
attention. Vibration analysis employs various signal processing methods such as spectral
analysis, time-scale, time frequency analysis, etc. these methods are used to analyze
bearings’ vibratory behavior by monitoring the evolution of statistical
indicators.However, diagnosing the bearing depending on traditional features only isn’t
sufficient to assure effective or reliable assessment of the component’ health condition.
This paper proposes a multi-features online dynamic classification as a new method for
fault detection and health condition monitoring for bearings; this technique uses multiple
features, including traditional features extracted from the raw signal, two special
features extracted by wavelet analysis, the spectral kurtosis, coupled with a nonlinear
principal component analysis and a dynamic classification to capitalize on the hidden
information in the time evolution of the features.Through this article, we introduce
different measures and techniques used to characterize the health state of rolling, then
we deploy a methodology using dynamic classification to detect early defect. To ensure an
almost continuous surveillance, this methodology is based on a real-time analysis, and
uses specific statistical indicators adapted to the experimental bench. Then, the
monitoring of the degradation is achieved through the resulting class of the state of
degradation. New parameters such as the speed of the class, the position of the class, the
shape of the class will be discussed to inform the state of damage. The suggested
methodology is validated by analyzing several fatigue tests from a fatigue bench bearing
thrust ball referenced SNR51207.