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Equipment fault diagnosis and abnormality 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 the problems of inability to recognize unknown abnormal states and high algorithm complexity, achieve high fault diagnosis accuracy, improve robustness, and anti-corruption. The effect of strong noise interference ability

Active Publication Date: 2019-08-16
SHANDONG UNIV
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  • Abstract
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  • 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 abnormality detection method and system based on multi-Gaussian model
  • Equipment fault diagnosis and abnormality detection method and system based on multi-Gaussian model
  • Equipment fault diagnosis and abnormality detection method and system based on multi-Gaussian model

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Embodiment 1

[0040] like figure 1 As shown, a method for equipment fault diagnosis and anomaly detection based on a multi-Gaussian model in this embodiment includes:

[0041] Step 1: 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;

[0042] Before this step, also include:

[0043] The vibration signals are overlapped and divided into frames, and a Hanning window is added to each type of vibration signal frame to obtain a sample set of each type of vibration signal.

[0044] Specifically, the device vibration signal is divided into frames and windows are added, and the continuous vibration signal is decomposed into overlapping signal frames through the Hanning window. The window function enables the signal frame to retain the time-frequency characteristics of the original signal while avoiding spectral leakage caused by frame edge truncation. When the signal is divided into...

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 the first embodiment. Specifically include:

[0101] (1) a 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 differential peak features.

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

[0104] The system also includes:

[0105] The vibro-acoustic signal sample set building module is used to overlap an...

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, realizes the following: figure 1 Steps in the shown multi-Gaussian model-based equipment fault diagnosis and anomaly detection method.

[0114] This embodiment solves the problems of low fault diagnosis accuracy and insensitivity to the unknown abnormal state of the equipment in the actual equipment working environment in the prior art. In actual operation, under the objective fact that sufficient and comprehensive abnormal working state information cannot be collected, The traditional diagnosis method cannot predict and alarm the unknown abnormal state in time. The equipment abnormality detection method based on the multi-Gaussian model of the present disclosure extracts the time domain and frequency domain features from the equipment vibration and sound signal, and establishes a plurality of The Gaussian model is used to e...

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Abstract

The invention provides an equipment fault diagnosis and abnormality detection method and system based on a multi-Gaussian model. The equipment fault diagnosis and abnormality detection method based onthe multi-Gaussian model comprises the following steps: extracting time domain and frequency domain characteristics from an equipment vibration sound signal, establishing the plurality of Gaussian models according to the distribution condition of characteristic dimensions, and establishing a final abnormality judgment Gaussian model according to a probability density mean value obtained by each type of data on the multi-Gaussian model. And a final abnormal detection judgment result is given through a result given by the model and a set fault-tolerant threshold value. The method has the advantages of being high in noise interference resistance, high in fault diagnosis precision and capable of meeting various unknown abnormal detection requirements.

Description

technical field [0001] The present disclosure belongs to the field of equipment fault diagnosis and abnormality detection, and in particular relates to a method and system for equipment fault diagnosis and abnormality 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 equipment usually contain rich equipment operating status information. By extracting the effective features of the vibration and sound signals in the time domain and frequency domain, the features are trained with a suitable fault classifier, and finally the fault classifier is passed. That is, the status monitoring of the equipment can be completed. However, in practice, all the operating states of the equipment cannot be fully extracted. During the operation of the eq...

Claims

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Application Information

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