Condition monitoring arises as a valuable industrial process in order to assess the health of rotating machinery, providing early and accurate warning of potential failures and allowing for the planning and effective realization of preventative maintenance actions. Nowadays machinery (gas turbines, wind turbines etc.) manufacturers adopt new business models, providing not only the equipment itself but additionally taking on responsibilities of condition monitoring, by embedding sensors and health monitoring systems within each unit and prompting maintenance actions when necessary. Among others, rolling element bearings are one of the most critical components in rotating machinery. In complex machines the failure indications of an early bearing damage are weak compared to other sources of excitations (e.g. gears, shafts, rotors etc.). Vibration analysis is most widely used and various methods have been proposed, including analysis in the time and frequency domain. In a number of applications, changes in the operating conditions (speed/load) influence the vibration sources and change the frequency and amplitude characteristics of the vibroacoustic signature, making them nonstationary. Under changing environments, where speed and load vary, the assumption of quasi-stationary is not appropriate and as a result a number of time-frequency and time-order representations have been introduced, such as the Short Time Fourier Transform and the Wavelets. Recently an emerging interest has been focused on modelling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. The classical cyclostationary tools, such as the Cyclic Spectral Correlation Density (CSCD) and the Cyclic Modulation Spectrum (CMS), can be used in order to extract interesting information about the cyclic behavior of cyclostationary signals, only under the assumption that the speed of machinery is constant or nearly constant. Global diagnostic indicators have been proposed as a measure of cyclostationarity under steady operating conditions. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency as well as diagnostic indicators of cyclo-non-stationarity in order to cover the speed varying operating conditions. The scope of this paper is to propose a novel approach for the analysis of cyclo-non-stationary signals based on the generalization of indicators of cyclo-non-stationarity in order to cover the simultaneous and independently varying speed and load operating conditions. The effectiveness of the approach is evaluated on simulated and real signals captured on a dedicated test rig.

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