Method and system for predicting remaining useful life of analog circuit

Patent metadata

Field	Value
Publication number	US-2022222409-A1
Application number	US-202117507750-A
Country	US
Kind code	A1
Filing date	Oct 21, 2021
Priority date	Jan 12, 2021
Publication date	Jul 14, 2022
Grant date	—

Abstract

A method and a system for predicting remaining useful life of an analog circuit are provided. A simulation model of the analog circuit is built, and an output voltage is selected as a degradation variable. Different degradation cycles are set to extract degradation features of the output voltage. Key features that can reflect a degradation trend of a circuit component are selected. Multi-feature fusion and similarity model are adopted to construct a health indicator curve to characterize a degradation process of a full life cycle of different circuit components. A prediction model is established based on a temporal convolutional network and an attention mechanism, and preferably selected features and a constructed health indicator database are used as an input of a TCN-attention network to predict the remaining useful life of the circuit component.

First claim

What is claimed is:

1 . A method for predicting remaining useful life of an analog circuit, comprising:
step (1) of establishing a simulation model of the analog circuit, simulating a degradation process of a circuit component of the analog circuit through adjusting a value of the circuit component to gradually deviate from a nominal value, and selecting an output voltage of the analog circuit as a degradation variable; step (2) of setting a tolerance range and a degradation threshold of the circuit component, collecting the degradation variable of each degradation cycle, and extracting corresponding degradation features; step (3) of establishing a feature parameter optimal rule for extracting various analog circuits, and preferably selecting key features that quantitatively characterize a degree of degradation of the circuit component; step (4) of calculating feature parameter deviations between different degradation states and healthy states of the circuit component to construct a health indicator curve for quantifying the degree of degradation of the circuit component; and step (5) of adopting a prediction model based on a temporal convolutional network (TCN) and an attention mechanism to learn preferably selected key feature data and corresponding health indicator curve data, and predicting the remaining useful life of the circuit component.

2 . The method according to claim 1 , wherein step (2) specifically comprises:
adopting a deep learning feature extraction method to extract intermediate layer information as initial features for the degradation variable collected in each degradation cycle; adopting a feature extraction method based on statistical theory to analyze and process the extracted initial features to obtain the degradation features of the analog circuit; adopting a feature extraction method based on time domain analysis to analyze and process the extracted initial features to obtain the degradation features of the analog circuit; and adopting a feature extraction method based on amount of information to analyze and process the extracted initial features to obtain the degradation features of the analog circuit.

3 . The method according to claim 1 , wherein step (3) specifically comprises:
step (3.1) of comprehensively integrating an optimal feature indicator based on monotonicity of the degradation features of the circuit component and trend of the degradation features of the circuit component to eliminate redundant degradation features that do not change along with the degradation cycle and obtain retained degradation features; and step (3.2) of adopting a maximum information coefficient (MIC) to calculate a correlation between the retained degradation features to filter out the key features that have deep non-linear correlation between each other in the entire degradation cycle through the maximum information coefficient (MIC), wherein a MIC with higher value represents a higher correlation between the degradation features.

4 . The method according to claim 3 , wherein step (3.2) specifically comprises:
establishing a correlation symmetric matrix,

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Assignees

• Univ Wuhan

Inventors

• He Yigang
• Du Bolun
• Wang Lei
• He Liulu
• Xing Zhikai

Classifications

• G06F2119/04

  Ageing analysis or optimisation against ageing · CPC title

• G06F2119/12

  Timing analysis or timing optimisation · CPC title

• G06F30/36 Primary

  Circuit design at the analogue level · CPC title

• G06F30/27

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title