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 cyclo-stationary, which is a particular class of nonstationary stochastic processes. 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 cyclo-stationarity (ICNS) in order to cover the speed-varying conditions. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN.
The laws concerning safety on worksite have become more strict year on year being that topic always more important. The main causes of injuries are workers unawareness and structural failures of machineries, i.e. cranes, or scaffolds. In order to prevent injuries, an accurate maintenance is mandatory. During the last years a new trend in maintenance has arisen in opposition to programmed maintenance. Monitoring the system, the condition based maintenance may prevent accidents avoiding unnecessary stops of the machineries and the corresponding reduced returns.
In this paper a health monitoring algorithm for a typology of construction machinery (the concrete displacing booms) is proposed. The proposed algorithm is based on the knowledge of geometrical and dynamical parameters of the boom, estimated through a stand-alone self-learning procedure. This feature makes the developed diagnostics system predisposed to be easily extended to other machineries or work fields. The common failure conditions, such as an overload or a crack propagation, are readily signaled.
The algorithm has been numerically and experimentally validated on a specific test rig which reproduces a reduced scale concrete displacing boom. The results, referred to the detection of two simulated common failure conditions, are presented and discussed.
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