Abstract
Diagnosing rotor faults is considered as one of the most vital tools for health maintenance of rotating machinery. In this work, a smart diagnosis system based on automatic recognition of multiple rotor faults is developed. As rotor faults are comparatively complicated, the reasoning mechanism of composite nesting probability reasoning network knowledge expression and multiple reasoning methods are adopted. Besides, methods such as fuzzy pattern recognition and image analysis are also applied to conduct automatic recognition of fault symptoms like rotor vibration spectrum, shaft centerline orbit, and transient features. Also, this article attempted to propose artificial neuron network study and diagnosis methods on the basis of fractal features of faults, set up a smart diagnosis system based on automatic recognition of multiple rotor faults, and verify the feasibility of system diagnosis using a rotor practical fault diagnosis case.
Highlights
Rotors are key components or rotating machinery, and any failures in rotors may introduce unwanted downtime, expensive repair procedures, and even human casualties
Rotor faults will result in abnormal changes in rotor vibration which can be spotted through monitoring and analyzing vibration signals.[2,3,4]
The vibration analysis under the condition of rotor faults can be comprehensively conducted from various perspectives such as vibration spectrum, phase, shaft centerline orbit, time-domain waveform, transient information, and statistical features.[7]
Summary
Rotors are key components or rotating machinery, and any failures in rotors may introduce unwanted downtime, expensive repair procedures, and even human casualties. The vibration analysis under the condition of rotor faults can be comprehensively conducted from various perspectives such as vibration spectrum, phase, shaft centerline orbit, time-domain waveform, transient information, and statistical features.[7] These fault symptoms are usually shown in forms including spectral line, graph, or data, as well as determine diversified diagnosis knowledge forms.[8] This work developed a smart diagnosis system based on automatic recognition of multiple rotor faults.
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