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Strong noise environment machine operation fault detection method and detection system

A technology for machine operation and fault detection. It is used in the testing of machines/structural components, instruments, measuring devices, etc. It can solve problems such as large noise impact, poor real-time detection, and information loss, and achieve strong noise suppression capability and noise suppression capability. Strong and stable output signal

Pending Publication Date: 2020-12-18
CHENGDU UNIV
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0007] (1) The existing machine fault detection method has complex algorithms, poor detection adaptability, poor real-time detection, and poor application of historical data;
[0008] (2) Existing machine fault detection methods are difficult to detect weak signals with low signal-to-noise ratio; simultaneous detection inevitably results in signal corruption or information loss
[0012] Considering that the fault signal is weak in the early stage of machine operation, it is greatly affected by noise, and the detection performance of existing conventional technologies is poor or the technical complexity is high. By making full use of the random modulation function of noise, the influence of historical data and real-time monitoring data on the detection results, it can Timely detection of early operating failures has huge economic and social value for safe production and product quality

Method used

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  • Strong noise environment machine operation fault detection method and detection system
  • Strong noise environment machine operation fault detection method and detection system
  • Strong noise environment machine operation fault detection method and detection system

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Embodiment 1

[0077] (1) MUSIC algorithm

[0078] It is well known that for a noise-contaminated signal, all practical signal detection methods are limited by the noise power or signal-to-noise ratio. Equation (1) shows that sampling is composed of multiple harmonics, but the actual signal harmonics are only a small part. If the actual signal does not represent a signal-significant feature of the sequence, it cannot be distinguished. All detection methods are limited by this characteristic, and they differ from other methods by improving the salient features in the frequency domain.

[0079] Suppose the sampling data is Y=X+W, and its autocorrelation matrix is ​​R yy , whose eigenvalues ​​can be decomposed into

[0080]

[0081] Among them, U 1 is the signal eigenvector, U 2 is the noise feature vector, ∑ 1 , ∑ 2 is a diagonal matrix. For Gaussian white noise, if its length is sufficient, its noise characteristic value is in (1)

[0082] and

[0083] Clearly, noise modula...

Embodiment 2

[0096] For mechanical vibration signals with noise interference, there are many fault detection methods, but most of them are not suitable for online real-time detection. The FFT algorithm has fast calculation speed and strong adaptability, and is the preferred algorithm for industrial online detection. However, when industrial machinery operates in a strong random interference environment, the stability of its FFT spectrum is poor. In order to solve the problem of fault detection in strong interference environment, a fault detection method based on MUSIC algorithm is designed.

[0097] Step 1: Sampling the nth data sequence, selecting consecutive m points, and constructing a data array;

[0098] Step 2: Perform spectral analysis on the data array;

[0099] Step 3: Weighted summation;

[0100] Step 4: Log failure and update.

[0101] In this algorithm, the weight of MUSIC analysis coefficients not only plays the role of learning new frequency components, but also weakens t...

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Abstract

The invention belongs to the technical field of fault detection, and discloses a strong noise environment machine operation fault detection method and detection system, and the method comprises the steps: carrying out the N-time sampling of an nth data sequence of a mechanical operation vibration signal, selecting m continuous points, and constructing a data array through a signal sampling sequence; performing N-M times of spectral analysis on the constructed data array through noise modulation and the number of signals; performing weighted summation; based on the spectral analysis result, judging the machine operation state through historical data and instant data fusion; and if the machine has a fault, recording the fault and updating. According to the invention, the MUSIC algorithm withlow complexity and strong noise suppression capability is used as a core algorithm, so that the requirement of online detection is met; the improved MUSIC algorithm is based on white noise, the algorithm is not limited by the white noise, and the noise suppression capability is high; according to the invention, the joint production of historical data and instant data is fully considered, so thatthe output signal is more stable.

Description

technical field [0001] The invention belongs to the technical field of fault detection, and in particular relates to a fault detection method and a detection system for machine operation in a strong noise environment. Background technique [0002] At present, with the development of technology, the degree of automation of modern large-scale production is getting higher and higher, the structure of modern equipment is becoming more and more complex, the functions are becoming more and more perfect, and the connection between various components inside the equipment is getting closer. For dynamic systems, internal failures, unstable working conditions or performance disorders will not only cause huge economic losses, but also cause casualties and serious social impacts. [0003] Fault diagnosis is a technology that can understand and grasp the state of the machine during operation, judge whether it is normal or abnormal in whole or in part, find faults and their causes at an ea...

Claims

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Application Information

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IPC IPC(8): G01M99/00G01H17/00
CPCG01M99/005G01H17/00
Inventor 蒋毅
Owner CHENGDU UNIV
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