Reducer, crane and fault automatic judgment method for automatic fault judgment

Abstract

The present application discloses a speed reducer with automatic fault discrimination, which includes a reduction transmission mechanism, a transmission shaft, a transmission bearing, a box body, an automatic fault discrimination device and a plurality of first sensors, the first sensor is installed on at least one part of it, and is used to It is used to detect and upload the sensing signal of the corresponding part of the reducer; the automatic fault discrimination device is used to decompose, reconstruct and calculate the energy of the sensing signal of the training sample, and use the energy vector formed by the calculated signal energy as the training sample respectively Input the neural network of the corresponding signal type for training, and input the sensor signals of different reducer parts uploaded by multiple first sensors into the trained neural network of the corresponding signal type, and obtain the component part of the reducer output by the neural network where the fault occurs and its fault type information. The reducer improves the automation and intelligence of fault discrimination, and optimizes the weights and thresholds of the neural network.

Description

technical field [0001] The present application relates to the technical field of fault identification, in particular to a reducer, a crane and an automatic fault identification method for automatic fault identification. Background technique [0002] The failure rate of mechanical equipment during operation is a pain point in the operation of equipment. For this reason, the method used is to install corresponding sensors on the equipment to detect the operating parameters and external changes of the equipment, so as to judge whether the equipment fails. For example, for a reducer, it is possible to judge whether the equipment is faulty by detecting the vibration, temperature, pressure and other data of the equipment. [0003] However, at present, the degree of automation and intelligence of the fault identification of the reducer is not high, and it is necessary to manually compare and analyze the data chart and the fault icon to determine whether the reducer is faulty, which...

AI Technical Summary

Problems solved by technology

[0003] However, at present, the degree of automation and intelligence of the fault identification of the reducer is not high. It is necessary to manually compare and analyze the data chart and the fault icon to determine whether the reducer is faulty, which makes the efficiency of fault identification low.

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