Impulse signal demodulation method based on all-digital peak detection

Abstract

The invention discloses a method for demodulating impact signals on the basis of all-digital peak detection, and belongs to the field of fault diagnosis for rotary machinery. The method has the advantages that in order to overcome shortcomings of poor instantaneity of an envelope solving procedure implemented in the traditional Hilbert demodulation method and low fault detection rate under a low signal-to-noise ratio condition, an all-digital peak detection algorithm with a structural element of AXe(-t / tau) is adopted in the method and is excellent in instantaneity, applicable to embedded equipment and high in noise immunity, excellent diagnosis results can be realized when the method is applied to rotary equipment working in complicated environments of industrial fields, and the method has an important economic significance and high engineering application value.

Description

technical field [0001] The invention belongs to method research in the field of mechanical fault diagnosis, and relates to an all-digital peak detection algorithm and a rotating mechanical fault diagnosis technology based on the algorithm. Background technique [0002] With the rapid development of modern industry, various rotating machines are widely used in various industrial fields. The failure of rotating machinery is an important issue related to the production safety of the national economy. Early diagnosis of failure is related to whether the equipment can operate safely and efficiently for a long time, and it is also an important guarantee for the normal production of the entire enterprise. Rolling bearings are vulnerable parts. Many faults in rotating machinery are related to rolling bearings. 70% of mechanical equipment faults are vibration faults, and about 30% of vibration faults are caused by bearings. In addition, the life of rolling bearings is very discrete....

AI Technical Summary

Problems solved by technology

But there are also shortcomings: 1) The process of obtaining the envelope is poor in real-time

For only 8K data, it is necessary to perform about 3.4e+7 times of complex multiplication, so the data processing complexity is very high, and the real-time performance is poor. For low-frequency and heavy-load bearing diagnosis, due to the large length of collected data, this method is not suitable. Be applicable

2) The fault detection rate is not high in the case of low signal-to-noise ratio

But it has shortcomings: the detection process is completely realized by hardware, and the time constant τ, which has the function of improving the signal-to-noise ratio, is determined by the resistance and capacitance values ​​in the circuit, so that the value of τ can only be in a few predetermined levels. bit selection, and is affected by the error of the resistive and capacitive components, and the peak energy it holds is also limited by the conduction threshold of the diode in the charging circuit

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