Determination of the diagnosis thresholds is crucial for the fault diagnosis of industry assets. Rotor machines under different working conditions are especially challenging because of the dynamic torque and speed. In this paper, an advanced machine learning based signal-processing innovation termed the multivariate state estimation technique is proposed to improve the accuracy of the diagnosis thresholds. A novel preprocessing technique called vibration resonance spectrometry is also applied to achieve a low computation cost capability for real-time condition monitoring. The monitoring system that utilizes the above methods is then applied for prognostics of a fan model as an example. Different levels of radial unbalance were added on the fan and tested, and then compared with the health state. The results show that the proposed methodology can detect the unbalance with a good accuracy and low computation cost. The proposed methodology can be applied for complex engineering assets for better predictive monitoring that could be processed with on-premise edge devices, or eventually a cloud platform due to its capacity for loss-less dimension reduction.

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