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PSR-LSTM model for weak pulse signal detection

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Abstract

In this paper, a weak impulse signal detection method based on phase space reconstruction of chaotic time series and long-term and short-term memory neural network (LSTM) in the deep learning model is proposed. Reconstructing the phase space of chaotic signals can effectively extract the chaotic information in the sequence, and constructing LSTM neural network can effectively distinguish the signal points from the non-signal points to achieve better detection results. When detecting the weak pulse signal in Lorenz chaotic system and Rossler chaotic system, the model has high detection accuracy and can still maintain the detection performance when the signal-to-noise ratio is low. This paper compares it with other machine learning models and deep learning models, such as Support Vector Machine (SVM), Recurrent Neural Network (RNN), Extreme Learning Machine (ELM), and so on. The results show that the detection accuracy of this model is higher than other comparable models under different signal-to-noise ratios and has strong detection performance. In addition, the weak pulse signal in the sunspot sequence is detected, and the fault signal in the rolling bearing is diagnosed. These results show that this model can accurately detect the weak pulse signal in the chaotic background when the signal-to-noise ratio is low and is suitable for dealing with the weak signal detection problem in the chaotic background in real life and the fault diagnosis problem in the engineering application field. This not only reduces the detection threshold of weak signal detection but also widens the application field of weak signal detection.

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Acknowledgments

This study is supported by the National Natural Science Foundation of China (Grant No.11871124) and the Chongqing Education Commission (Grant KJQN202101125).

Data and program availability

The data used to support the findings of this study and the program in this study are available from the corresponding author upon request.

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Authors and Affiliations

  1. School of Sciences, Chongqing University of Technology, Chongqing, 400054, China

    Liyun Su, Mingliang Yin & Shengli Zhao

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  1. Liyun Su
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  2. Mingliang Yin
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  3. Shengli Zhao
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Correspondence to Liyun Su.

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Su, L., Yin, M. & Zhao, S. PSR-LSTM model for weak pulse signal detection. Multimed Tools Appl 82, 35853–35877 (2023). https://doi.org/10.1007/s11042-023-14987-w

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