Abstract
Diagnostics of rotating machines is taking a new step forward with the development of intellectual technologies based on predictive and machine learning tools. Despite having a range of advantages compared to humans in terms of big data processing powers, correlation and various feature extraction possibilities, and swiftness of operation, such systems are limited by measurement system elements in terms of their parameters: sensors and ADCs with their sensitivity properties and accuracy restrictions, microprocessors with limitation of processing powers, etc. Experimental data is used to recreate experimental environment in simulation of induced unbalance. The imitation model is based on rotor dynamics equations of rotor motion, Reynolds equation to estimate reaction forces of a fluid-film bearing and takes into account sensor parameters and position, gear coupling effect and other measurement system elements parameters. The results show that under certain conditions it becomes impossible to successfully track unbalance which in real conditions could lead to malfunction or failure of a rotor machine.
Highlights
Importance of successful diagnostics cannot be overestimated in any field of study
The virtual sensor signals are utilized for model-based fault diagnosis in rotor systems, with focus on unbalances in rotors
In [3] combined rotor fault diagnosis in rotating machinery using empirical mode decomposition is presented with focus on the main fault modes: unbalance, misalignment, partial rub, looseness and bent rotor
Summary
Importance of successful diagnostics cannot be overestimated in any field of study. Abnormalities of operation are reasonably considered unwanted in any field of study as well. In [2] a very comprehensive study is presented on model-based fault diagnosis in rotor systems with self-sensing piezoelectric actuators. The virtual sensor signals are utilized for model-based fault diagnosis in rotor systems, with focus on unbalances in rotors. In [5] Identification and diagnosis of concurrent faults in rotor-bearing system is performed using wavelet packet transform and zero space classifiers. While these already classic approaches suit their goal very well in most cases, there is another field of study to take diagnostics on a new level – machine learning and artificial neural networks [6,7,8,9].
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