Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition monitoring are used including oil debris monitoring, temperature monitoring and vibration monitoring. Among them, vibration monitoring is the most widely used technique. Machine vibro-acoustic signatures contain pivotal information about its state of health. The current work focuses on one part of the diagnosis stage of condition monitoring for engine bearing health monitoring as bearings are critical components in rotating machinery. A plethora of signal processing tools and methods applied at the time domain, the frequency domain, the time-frequency domain and the time-scale domain have been presented in order to extract valuable information by proposing different diagnostic features. Among others, an emerging interest has been reported on modeling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. A process x(t) is said to be nth-order cyclostationary with period T if its nth-order moments exist and are periodic with period T. Several tools, such as the Spectral Correlation Density (SCD) and the Cyclic Modulation Spectrum (CMS) can be used in order to extract interesting information concerning the cyclic behavior of cyclostationary signals. In order to measure the cyclostationarity from order 1 to 4, concise and global indicators have been proposed. However, in a number of applications such as aircraft engines and wind turbines the characteristic vibroacoustic signatures of rotating machinery depend on the operating conditions of the rotational speed and/or the load. During the last decades fault diagnostics of rotating machinery under variable speed/load has attracted a lot of interest. The classical cyclostationary tools can be used under the assumption that the speed of machinery is constant or nearly constant, otherwise the vibroacoustic signal becomes cyclo-non-stationary. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency. The goal of this paper is to propose a novel approach for the analysis of cyclo-nonstationary signals based on the generalization of indicators of cyclostationarity in order to cover the speed varying conditions. The proposed indicators of cyclo-non-stationarity (ICNS) are expected to summarize the information at various statistical orders and at lower computational cost compared to the Order-Frequency SCD or CMS. This generalization is realized by introducing a new speed-dependent angle averaging operator. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN, in the frames of SAFRAN contest which took place at the Surveillance 8 International Conference.

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