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 Simulatorä, 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 3 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 3 and 3 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.

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This study proposes a statistical approach based on vibration energy at damage to detect multiple damages occurring in roller bearings. The analysis was performed at four different rotating speeds—1002, 1500, 2400, and 3000 RPM—following four different damages—inner race, outer race, ball, and combination damage—and under two types of loading conditions. These experiments were performed on a SpectraQuest Machinery Fault Simulator™ by acquiring the vibration data through accelerometers under two operating conditions: with the bearing loader on the rotor shaft and without the bearing loader on the rotor shaft. The histograms showed diversity in the defected bearing as compared to the intact bearing. There was a marked increase in the kurtosis values of each damaged roller bearing. This research article proposes that histograms, along with kurtosis values, represent changes in vibration energy at damage that can easily detect a damaged bearing. This study concluded that the vibration energy at damage-based statistical technique is an outstanding approach to detect damages in roller bearings, assisting Industry 4.0 to diagnose faults automatically.