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• W2946111806 endingPage "168781401985130" @default.
• W2946111806 startingPage "168781401985130" @default.
• W2946111806 abstract "Traditional industry is shifting towards the ‘industry 4.0 factory’ that incorporates automatic fault detection and correction. Industry 4.0 also includes online condition monitoring to make maintenance decisions on the basis of the health of a single machine. This research article presents order analysis for detecting two common problems in rotating machinery – misalignment and cracks. The systematic and detailed experimentations were performed on SpectraQuest’s Machinery Fault Simulator™, and the time domain data acquired through the accelerometers mounted on motor and rotor inboard and outboard bearing housing were transformed into spectra using a fast Fourier transform. Two sets of experiments have been performed for misalignment and one set for shaft cracks. Three levels of misalignment with two types of loading conditions have been analysed. The faulty vibration data were compared with the healthy shaft. The misaligned shaft shows higher vibration amplitude at 2× running speed and harmonic vibration behaviour. The slit repair and V-notch crack shaft models were used to analyse the effect of cracks on vibrations and the resulting vibration spectra showed peaks at 2× and 3× running speed. These results indicate that order analysis is helpful in detecting misaligned and cracked shafts, supporting industry 4.0 by facilitating the automatic detection of faults." @default.
• W2946111806 created "2019-05-29" @default.
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• W2946111806 date "2019-05-01" @default.
• W2946111806 modified "2023-10-03" @default.
• W2946111806 title "Experimental condition monitoring for the detection of misaligned and cracked shafts by order analysis" @default.
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• W2946111806 doi "https://doi.org/10.1177/1687814019851307" @default.
• W2946111806 hasPublicationYear "2019" @default.
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