Journal of vibration and acoustics · 2002 · 350 citations · 8 references
EngineeringMechanical EngineeringComputational MechanicsTime Series AnalysisVibrationDiagnosisFault DetectionFault DiagnosisMachine VibrationVibration DiagnosticsVibration MonitoringAutocorrelation FunctionBearing Fault DiagnosisBearing FaultsBearingsCyclostationary ProcessesGear Fault DiagnosisGearbox Fault DiagnosisRolling Element Bearings
This paper addresses vibration‑based diagnosis of rolling‑element bearings amid strong interfering gear signals typical of helicopter gearboxes. The study proposes an original diagnostic procedure for distinguishing gear and bearing faults. The method distinguishes gear signals as purely periodic from bearing signals, which are random and cyclostationary, by modeling vibration generation with a comprehensive model that differentiates localized versus distributed faults, cyclostationary versus pseudo‑cyclostationary processes, and additive versus multiplicative gear interactions. Its performance is illustrated on simulated, experimental, and real cases.
Abstract This paper deals with the vibration-based diagnosis of rolling element bearings in the presence of strong interfering gear signals, such as is typical of helicopter gearboxes. The key idea consists in recognizing gear signals as purely periodic, whereas bearing signals experience some randomness and are close to cyclostationary, i.e. with a periodic bivariate autocorrelation function. This assertion is demonstrated by introducing a comprehensive model for the vibration generating process of bearing faults: distinctions are made between localized and distributed faults, between cyclostationary and pseudo-cyclostationary processes, and between additive and multiplicative interactions with gear signals. Finally, an original diagnostic procedure is proposed and its performance illustrated using simulated, experimental and actual cases.
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Robert B. Randall, Jérôme Antoni, S. Chobsaard · Mechanical Systems and Signal Processing · 2001 · 728 citations
Engineering, Electrical Engineering, Time Series Analysis +8