Drive axle gear bearing fault monitoring method based on ARMED and sideband energy

Abstract

The invention relates to a drive axle gear bearing fault monitoring method based on ARMED and sideband energy. The method comprises the steps that: a vibration signal of a drive axle is collected; ARMED filtering and local sideband energy enveloping are carried out on vibration signal data, and then a kurtosis index and sideband energy RMS are extracted; and according to the kurtosis index and the sideband energy RMS, whether a gear and a bearing have faults or not is judged. The ARMED filtering in the invention overcomes the end distortion problem of MED filtering, and removes deterministic components through AR filtering, thereby reducing the interference of gear processing errors and a rack gearbox on fault determination; based on gear sideband energy analysis, the fault state of the bearing can be indirectly reflected, the practical problems that the fault of the bearing is weak and difficult to extract are effectively avoided, and thus coupling fault analysis of the gear bearing is simply and efficiently achieved; and besides, when the fault monitoring method is applied to the fatigue test of a drive axle gear bearing rack, unattended operation of the fatigue test can be realized, and the labor cost is further reduced.

Description

technical field [0001] The application belongs to the technical field of gear bearing fault monitoring, and in particular relates to a fault monitoring method for drive axle gear bearings based on ARMED and sideband energy. Background technique [0002] The bench fatigue test of drive axle gear bearing is a key test item in the development process. During the test, if the online condition monitoring and fault early warning technology is not adopted, the final result of the test is mostly that the core components such as gears and bearings are severely damaged, so that the initial location and root cause of the fatigue failure cannot be identified. This, in turn, leads to difficulties in the subjective analysis of fatigue tests. This not only prolongs the development cycle and increases the development cost, but at the same time, the sudden failure shutdown will cause great damage to the test bench. [0003] At present, most of the fault monitoring systems for drive axle ge...

AI Technical Summary

Problems solved by technology

Especially in the low frequency band (0-1000Hz), under the interference of gear signals, it is very difficult to extract and judge bearing fault signals

At present, without manual order recognition or subjective judgment, the monitoring system cannot independently distinguish gear faults from bearing faults

At the same time, order analysis requires the introduction of key phase signal acquisition and speed calculation, which increases the complexity of the monitoring system software and hardware and commercial costs

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