Fault monitoring method for rotating equipment

Abstract

The invention relates to the technical field of rotating equipment monitoring, in particular to a rotating equipment fault monitoring method, which comprises the following steps: S1, acquiring an acceleration signal; s2, performing frequency spectrum editing integration processing on the acceleration signal to generate a speed signal and a displacement signal; and S3, judging whether the rotating equipment has a fault or not according to the speed signal and the displacement signal. The method has the beneficial effects that the frequency spectrum editing processing is performed based on the sensor characteristics, the ground noise in the acceleration signal is effectively suppressed, interference components are effectively suppressed according to the size of the signal ground noise and the peak value distribution characteristics, and the effective signal is reserved. The method is low in calculation complexity, accurate in effect and capable of better adapting to an online monitoring scene.

Description

technical field [0001] The invention relates to the technical field of rotating equipment monitoring, in particular to a fault monitoring method for rotating equipment. Background technique [0002] At present, in my country's metallurgy, petroleum, chemical and other industries, rotating machinery and equipment account for about 80%. Many of these devices work in harsh environments such as high speed and heavy load, and are prone to failures. The unplanned downtime caused by equipment damage has brought huge losses to the enterprise. Therefore, it is of great significance to monitor and diagnose the faults of the above rotating machinery. [0003] In the existing fault diagnosis of rotating machinery, the monitoring technology based on vibration signals is the most direct and effective method. The vibration caused by gear and bearing faults in the equipment is easily captured by the acceleration sensor, and the faults such as unbalanced, misaligned, and loose rotors are eas...

AI Technical Summary

Problems solved by technology

However, existing methods have encountered some challenges due to signal distortion problems caused by DC components, temperature drift, unstable low-frequency performance of sensors, environmental interference, background noise, etc.

Time domain integration has the problem that the trend item cannot be completely removed, and error accumulation is prone to occur during the integration process; the frequency domain integration method is simple in principle and can effectively avoid the cumulative amplification effect of time domain integration on small error signals, but there is still noise floor The component cannot completely remove the problem, and it is easy to amplify the noise floor at low frequencies, causing the real component to be overwhelmed by the noise

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