Weak fault parallel-connected random resonance detection method of low-speed heave-load device

Abstract

The invention discloses a weak fault parallel-connected random resonance detection method of a low-speed heave-load device for detecting weak fault signals of a device. The method comprises extracting a device vibration signal through a vibration sensor mounted on a position to be detected, amplifying and filtering the signals, and converting analogue signals into digital signals through an analog-digital converter, then processing by a parallel-connected random resonance method for output. The fault type is determined by extracting the weak characteristics of fault signals from the waveform of the output signal and the spectrogram thereof. The method combines random resonance method and self-correlation processing method, can effectively extract early weak fault characteristic signals in the low-speed heavy-load device so as to find fault hidden dangers ahead of time.

Description

technical field [0001] The invention relates to a fault detection method for low-speed heavy-duty equipment, in particular to a method for detecting weak faults in low-speed heavy-duty equipment in parallel with stochastic resonance. Background technique [0002] Low-speed heavy-duty equipment (such as bucket wheel stacker and reclaimer, cement rotary kiln ring gear, industrial excavator, subway tunnel boring machine, cement mill transmission equipment, coal unloading dumper, screw conveyor, port ship unloader, door Pedestal cranes, wind power speed-increasing gearboxes, large-scale slewing bearings for blast furnace top airtight boxes without material bells, ladle rotating towers, and steelmaking converters, etc.) are key equipment in modern industry. The reaction seriously affects the production and economic benefits of the enterprise. For example, the rotating current of the transmission gearbox on the top of a blast furnace in a factory increased. After the distributor ...

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Problems solved by technology

In recent years, there have been many reports on the use of stochastic resonance technology for the detection and identification of periodic signals. According to existing theories, after the signal passes through a single-stage stochastic resonance system, the high-frequency part of the signal is effectively suppressed, and the energy is transferred to the low frequency. However, the stochastic resonance system is more sensitive to the sampling frequency of the signal, parameter adjustment has always been an important factor to determine whether the signal frequency is obvious after the stochastic resonance system is output, and the use of two or more stochastic resonance systems in parallel as information detection and signal analysis technology is still not seen

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