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Time Series Data Generation Method with High Reliability Based on ACGAN.

Fang Liu¹, Yuxin Li¹, Yuanfang Zheng¹

¹School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China.

Entropy (Basel, Switzerland)

|February 26, 2025

Summary

This summary is machine-generated.

This study introduces a High Reliability ACGAN (HR-ACGAN) to generate industrial fault diagnosis data. The method enhances feature extraction and data reliability, effectively addressing small sample size issues in big data processing.

Keywords:

generative adversarial network long short-term memory network small sample problem time series data generation

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Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Big data processing, particularly in industrial fault diagnosis, faces challenges with small sample sizes.
Existing Generative Adversarial Network (GAN) methods often neglect temporal characteristics, leading to insufficient feature extraction and low reliability in generated data.
High overlap in generated data due to low category differentiation in real data further reduces reliability.

Purpose of the Study:

To propose a novel time series data generation method, High Reliability ACGAN (HR-ACGAN), for industrial fault diagnosis.
To enhance feature extraction capabilities by incorporating temporal characteristics.
To improve the reliability and category differentiation of generated data.

Main Methods:

Integration of a Bi-directional Long Short-Term Memory (Bi-LSTM) network layer into the discriminator to capture temporal features.
Design of an improved training objective function in the generator to minimize data overlap and boost reliability.
Application and simulation analysis on two representative industrial fault datasets.

Main Results:

The HR-ACGAN method successfully generates time series data with high similarity to real data.
Expansion of datasets using HR-ACGAN-generated data led to significant improvements in classification accuracy.
The method effectively mitigated issues related to dataset imbalance in industrial fault diagnosis.

Conclusions:

The proposed HR-ACGAN method offers a robust solution for generating reliable, high-quality synthetic data for industrial fault diagnosis.
The incorporation of temporal dynamics and improved training objectives enhances the capability of GANs for complex time series data.
HR-ACGAN provides effective technical support for practical applications, particularly in addressing data scarcity in fault diagnosis.