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Equipment fault diagnosis and anomaly detection method and system based on multi-Gaussian model

A multi-Gaussian model, equipment failure technology, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as high algorithm complexity, inability to identify unknown abnormal states, etc., to improve robustness, high fault diagnosis accuracy, The effect of strong anti-noise interference ability

Active Publication Date: 2020-12-22
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the above problems, the first aspect of the present disclosure provides a multi-Gaussian model-based equipment fault diagnosis and anomaly detection method, which can solve the problem of inability to identify unknown abnormal states and algorithm complexity in actual equipment operation status detection in the prior art High problems, while improving the accuracy of equipment fault diagnosis and the robustness of the detection system, to provide a reliable reference for ensuring the normal and safe operation of equipment

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  • Equipment fault diagnosis and anomaly detection method and system based on multi-Gaussian model
  • Equipment fault diagnosis and anomaly detection method and system based on multi-Gaussian model
  • Equipment fault diagnosis and anomaly detection method and system based on multi-Gaussian model

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Experimental program
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Embodiment 1

[0040] Such as figure 1 As shown, a multi-Gaussian model-based equipment fault diagnosis and abnormal detection method in this embodiment includes:

[0041] Step 1: Extract the time-domain and frequency-domain features of each type of vibro-acoustic signal samples to characterize the time-frequency characteristics of the samples;

[0042] Before this step, also include:

[0043] The vibro-acoustic signal is overlapped and framed, and a Hanning window is added to each type of vibro-acoustic signal frame to obtain a sample set of each type of vibro-acoustic signal.

[0044] Specifically, the vibro-acoustic signal of the equipment is framed and windowed, and the continuous vibro-acoustic signal is decomposed into overlapping signal frames through the Hanning window. The window function makes the signal frame retain the time-frequency characteristics of the original signal while avoiding Spectrum leakage caused by frame edge truncation is eliminated. When framing the signal, a ...

Embodiment 2

[0100] A multi-Gaussian model-based equipment fault diagnosis and anomaly detection system in this embodiment corresponds to the multi-Gaussian model-based equipment fault diagnosis and anomaly detection method in Embodiment 1. Specifically include:

[0101] (1) Feature extraction module, which is used to extract the time-domain and frequency-domain features of each type of vibro-acoustic signal sample to characterize the time-frequency characteristics of the sample;

[0102] In the feature extraction module, the extracted time-domain features of each type of vibro-acoustic signal samples include root mean square features, kurtosis features and first-order difference peak features.

[0103] In the feature extraction module, the extracted frequency-domain feature of each type of vibro-acoustic signal sample is the Mel cepstral coefficient feature.

[0104] The system also includes:

[0105] The vibroacoustic signal sample set construction module is used to overlap and divide ...

Embodiment 3

[0113] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the following figure 1 The steps in the multi-Gaussian model-based equipment fault diagnosis and anomaly detection method are shown.

[0114] This embodiment solves the problem of low fault diagnosis accuracy and insensitivity to unknown abnormal states of the equipment in the existing technology in the actual equipment working environment. In actual operation, under the objective fact that sufficient and comprehensive abnormal working state information cannot be collected, Traditional diagnostic methods cannot predict and alarm unknown abnormal states in a timely manner. The multi-Gaussian model-based equipment abnormality detection method disclosed in this disclosure extracts time-domain and frequency-domain features from equipment vibration and sound signals, and establishes multiple Gaussian model, based on the average ...

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Abstract

The present disclosure provides a multi-Gaussian model-based equipment fault diagnosis and abnormality detection method and system. Among them, the multi-Gaussian model-based equipment fault diagnosis and anomaly detection method extracts time-domain and frequency-domain features from the vibration-acoustic signal of the equipment, and establishes multiple Gaussian models for the distribution of feature dimensions. The mean value of the probability density obtained establishes the Gaussian model for the final anomaly determination. The final anomaly detection judgment result is given through the results given by the model and the set fault tolerance threshold. The disclosure has the advantages of strong anti-noise interference capability, high fault diagnosis accuracy and adaptability to various unknown abnormal detection requirements.

Description

technical field [0001] The disclosure belongs to the field of equipment fault diagnosis and abnormal detection, and in particular relates to a method and system for equipment fault diagnosis and abnormal detection based on a multi-Gaussian model. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] The vibration and sound signals generated during the operation of the equipment usually contain a wealth of equipment operating status information. By extracting the effective features of the time domain and frequency domain of the vibration and sound signals, the features are trained with a suitable fault classifier, and finally passed the fault classifier. That is, the status monitoring of the equipment can be completed. However, in actual situations, all operating states of the equipment cannot be fully extracted. During equipment operation, unk...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/00
CPCG06F2218/12G06F18/2415
Inventor 常发亮蒋沁宇
Owner SHANDONG UNIV