Fan slight fault detection method based on vibration signal circulation stability

Abstract

The invention provides a fan slight fault detection method based on vibration signal circulation stability. The fan slight fault detection method comprises the following steps: step (1) acquiring an acceleration signal; step (2) guiding the acquired signal into a program which is to run, and detecting by using a correlation characteristic function based on circulation stability characteristics toobtain a circulation density spectrum; step (3) normalizing the circulation density to obtain a circulation correlation spectrum; step (4) obtaining a reinforced envelope spectrum according to the circulation correlation spectrum, storing the longitudinal coordinate data of the reinforced envelope spectrum, namely the data obtained by real-time treatment; step (5) calculating the correlation of the data obtained by real-time treatment and the data in a database by utilizing a correlation function; and step (6) judging whether the fault is normal or not or belongs to any fault according to a correlation coefficient fault distinguish standard, and adding a judgment result to the database. The fan slight fault detection method provided by the invention can be utilized for detecting and judging the fault type of a fan in real time, realizes more accurate detection and is quite high in practicability.

Description

technical field [0001] The invention belongs to the field of signal processing and fault detection, and in particular relates to a weak fault detection method of a fan based on the cycle stability of vibration signals. Background technique [0002] Cyclostationary signal processing is a new technology in mechanical signal processing that has emerged recently. A cyclostationary signal is a signal that contains hidden periodic information. The cyclostationary signal is a kind of non-stationary signal, which is closer to the actual signal than the traditional detection method, especially the signal generated by the rotating machinery. [0003] At present, the commonly used rotating machinery fault detection methods in the field of signal processing mainly include Fourier transform, short-time Fourier transform, wavelet transform, second-generation wavelet transform and multi-wavelet transform, etc., which can be said to be based on the principle of inner product. The character...

AI Technical Summary

Problems solved by technology

[0004] However, there are following shortcomings and deficiencies in the prior art: Fault detection methods such as Fourier transform, short-time Fourier transform, wavelet transform, second-generation wavelet transform and multi-wavelet transform are all based on the assumption that the signal is a stationary signal, and In reality, it is often a non-stationary signal, so these detection methods are unreasonable and unrealistic

At the same time, due to theoretical limitations, these traditional detection methods are difficult to detect some important features of rotating machinery, such as blade passing frequency BPF, blade ratio frequency BRF, etc., which have great limitations

Images