Most industrial systems have supervisory control and data acquisition (SCADA) systems that collect and store process parameters. SCADA data is seen as a valuable source to get and extract insights about the asset health condition and associated maintenance operations. It is still unclear how appliable and valid insights SCADA data might provide. The purpose of this paper is to explore the potential benefits of SCADA data for maintenance purposes and discuss the limitations from a machine learning perspective. In this paper, a two-year SCADA data related to a wind turbine generator is extracted and analysed using several machine learning algorithms, i.e., two-class boosted decision tree, two-class decision forest, k-means clustering on Azure ML learning studio. It is concluded that the SCADA data can be useful for failure detection and prediction once rich training data is given. In a failure prediction context, data richness means ensuring that fault features are presented in the training data. Moreover, the logs file can be used as labelled data to supervise some algorithms once they are reported in a more rigorous manner (timing, description).
As VIP passengers generally want to fly civil and executive jets where vibratory and acoustic environment is smoother than on the normal jets. Helicopter interior noise is generated by main and tail rotors, engines, main gearbox, and aerodynamic turbulence (Lu et al., 2018). Because of these sources, the tonal and broadband noise is incredibly high and needs to be reduced. Conventional passive system (soundproofing) is the best way to control the acoustic of the cabin whereas active systems (active vibration and noise control) are not completely reliable or applicable. The design of the soundproofing may be researched by simulation using one of these programs: ANSYS, SOLIDWORKS 2020 and ACOUSTIC analysis Vibroacoustic Monitoring (VAM) approach. The analyses were performed from frequency ranges, 5-10Hz and 0-2000Hz then transformed into frequency velocity domain using Proudman’s equations (Lu et al., 2017). Soundproofed ANSYS models are validated using instantaneous sound pressure levels measured within the helicopter during flight. The acoustic detection method for GAZELLE is also performed successfully in SOLIDWORKS for aluminum alloy and titanium alloy, this proves the relationship between acoustic power levels and material configuration. The noise coefficient responses of interior materials are used as main index for soundproofing helicopter interiors. The results of this research can be used for implementation of VAM approach for soundproofing helicopter interiors.
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