Diagnostics of rolling element bearings is usually performed by the analysis of vibration signal using suitable signal analysis tools, such as the most used and simplest method, Envelope Analysis. This method is based on the identification of bearing damage frequency components in the so-called Square Envelope Spectrum. If the assessment of the bearing health is quite a simple task, the on-line monitoring and the real-time evaluation of the trend of a suitable damage index is a complex task to be performed in an automatic way. The damage index must be robust against variations of system operating conditions and external vibration sources to avoid misleading results. The damage index should be also simple to be evaluated in the case of real-time applications. In the paper, the case of a rolling element bearing in which the defect develops until a permanent failure is described as well as the algorithm implemented for alarm signaling.
An On-Line Monitoring and Diagnostic Method of Rolling Element Bearing with AI