A vibration fault early warning device for rotating machinery

Abstract

The invention discloses a rotating machinery vibration fault early warning device, which includes a wave detection circuit, a frequency modulation amplitude modulation circuit and a voltage stabilizing transmission circuit. Capacitor C3-capacitor C8 and transistor Q1 form a frequency modulation circuit to adjust the signal frequency, and then use the op amp AR2, op amp AR4, diode D1, and diode D2 to form a peak circuit to filter the peak signal, and through the limiting circuit composed of diode D3 and diode D4 After limiting the signal potential, input it into the regulated transmitter circuit, and use the triode Q2, triode Q3 and operational amplifier AR4 to form a feedback circuit to further adjust the signal waveform. Finally, the regulated transmitter circuit uses the regulator D5 to stabilize the signal and pass it through the signal transmitter. E1 is transmitted to the early warning in the control terminal, which realizes the early warning function of the rotating machinery vibration fault.

Description

technical field [0001] The invention relates to the technical field of rotating machinery, in particular to a vibration fault early warning device for rotating machinery. Background technique [0002] At present, fault identification technology is fundamentally pattern recognition, which is an important prerequisite for fault diagnosis. Its research methods mainly include classic analysis methods such as time domain analysis, frequency domain analysis, and amplitude domain analysis, as well as neural networks, fuzzy identification, Modern analysis methods such as support vector machines and Bayesian. For the classic signal analysis methods, although the theory is mature and the algorithm is simple to implement, it is sometimes difficult to distinguish and identify complex vibration signals through time-domain statistical characteristics or spectral characteristics; for modern analysis methods such as neural networks, although It has powerful data analysis and discrimination...

AI Technical Summary

Problems solved by technology

For the classic signal analysis methods, although the theory is mature and the algorithm is simple to implement, it is sometimes difficult to distinguish and identify complex vibration signals through time-domain statistical characteristics or spectral characteristics; for modern analysis methods such as neural networks, although Possess powerful data analysis and discrimination capabilities, but the complicated model building process and parameter debugging process make it difficult to apply to engineering practice

[0003] The fault diagnosis system for rotating machinery can diagnose faults according to fault waveforms combined with parameters such as temperature, pressure, vacuum, load, excitation current, and excitation voltage. The theory of vibration faults of rotating machinery, based on this theory, can realize the early warning of vibration faults of rotating machinery by using waveform signal analysis, but currently there is no early warning device for vibration faults of rotating machinery designed using this theory

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