Induction motors are widely used in industrial plants for critical operations. Stator faults, bearing faults or rotor faults can lead to unplanned downtime with associated cost and safety implications. Different sensors may be used to monitor the health state of induction motors with each sensor typically being better suited to diagnosing different faults. Condition monitoring approaches which fuse data from multiple sensors have the potential to diagnose a greater number of faults. A sensor fusion approach based on the combination of a two-stage Bayesian method and Principal Component Analysis is proposed for diagnosing both electrical and mechanical faults in induction motors. Acoustic, electric and vibration signals are gathered from motors operating under different loading conditions and health states. The inclusion of the PCA step ensures robustness to varying loading conditions. The obtained results highlight that the proposed method performs better than equivalent single stage or feature-based Bayesian methods.
a b s t r a c tThe detection and diagnosis of faults in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using vibration analysis or other specific techniques. Conventional vibration-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements.In this paper, a technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions.
The interaction between eccentricity and an external forcing fluctuation in gear rattle response is investigated experimentally. The experimental rig consists of a 1:1 ratio steel spur gear pair, the input gear being controlled in displacement and the output gear being under no load. Gear transmission errors recorded using high accuracy encoders are presented. Large variations in backlash oscillation amplitude are observed as the relative phase of the input forcing and the sinusoidal static transmission error due to eccentricity is varied. A simplified mathematical model incorporating eccentricity is developed. It is compared with experimental findings for three different gear eccentricity alignments by way of plots relating backlash oscillation amplitude to forcing amplitude and phase relative to eccentricity sinusoid. It is shown that eccentricity does not fully account for the experimentally observed large variations in amplitude. Through analysis of the experimental data, it is suggested that further tooth profiling errors may explain the discrepancies.
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