Mechanical equipment fault diagnosis method based on machine learning classification algorithm

Abstract

The invention provides a mechanical equipment fault diagnosis method based on a machine learning classification algorithm. The mechanical equipment fault diagnosis method comprises the following stepsthat step 1, an acceleration sensor is used for collecting vibration signals of key points of mechanical equipment, and original waveforms of the vibration signals are stored; step 2, the collected vibration signals in the step 1 are screened and judged; step 3, the screened vibration signals are preprocessed; step 4, acceleration signals, velocity signals and envelope signals obtained in the step 3 are subjected to feature extraction; and step 5, the obtained feature vector in the step 4 is input into a fault classification model, and the model outputs fault diagnosis results corresponding to the equipment. The mechanical equipment fault diagnosis method based on the machine learning classification algorithm establishes an intelligent diagnosis model of the mechanical equipment, and intelligent diagnosis of fault of the mechanical equipment is further realized.

Description

technical field [0001] The invention belongs to the field of mechanical equipment fault diagnosis, in particular to a mechanical equipment fault diagnosis method based on a machine learning classification algorithm. Background technique [0002] With the rapid improvement of industrial modernization level, machinery and equipment are increasingly developing towards high speed, precision, automation and integration. Rotating parts in mechanical equipment, such as bearings, bearing bushes, main shafts, gearboxes, etc., have complex and changeable working environments, and are often prone to various failures due to heavy workloads, variable loads, and the influence of extreme external working environments. If the fault cannot be diagnosed and eliminated in a timely and effective manner, as the fault deteriorates and further develops, it will bring about major safety hazards and cause major economic losses. [0003] Traditional mechanical equipment fault diagnosis methods are m...

AI Technical Summary

Problems solved by technology

For the types of faults whose fault mechanism is complicated, the signal spectrum is complex, and the fault characteristics are not obvious, the traditional fault diagnosis method has poor diagnostic effect and low accuracy.

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