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<p>Stochastic resonance (SR) is a counterintuitive nonlinear phenomenon in which noise can enhance, rather than suppress, weak signal detection. Over the past five decades, SR has evolved from a theoretical concept into a practical signal processing tool with applications in mechanical fault diagnosis, biomedicine, geophysics, communications, and renewable energy systems. This paper reviews the latest advancements in the field of SR and organizes them around three core dimensions: (1) innovative system structures, including the evolution from single-stable to multi-stable, delayed, coupled, and cascaded configurations; (2) extended signal- and noise-driven stochastic resonance methods, with a particular focus on adaptive and intelligent strategies for signal detection under complex noise environments and across diverse signal types; and (3) state-of-the-art engineering applications, encompassing weak signal detection under complex noise, fault diagnosis, image denoising, renewable energy systems, and biomedical engineering. This work emphasizes adaptability, robustness, and intelligent integration, and systematically compares the performance of emerging SR models (e.g., Gaussian potential SR, multistable SR) with conventional frameworks. It also highlights the transition of SR from theoretical models to engineering-ready detection frameworks and identifies key challenges, such as the detection of non-periodic and multi-frequency signals under strong noise backgrounds. This updated review provides a critical reference for researchers and engineers seeking to advance SR theory and its practical implementation in complex, real-world systems.</p>

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Stochastic resonance: advanced theories, novel methodologies, and cutting-edge applications in weak signal detection

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Xiaoxiao Huang,
  • Gang Zhang,
  • Lianbing Xu,
  • Lin He

摘要

Stochastic resonance (SR) is a counterintuitive nonlinear phenomenon in which noise can enhance, rather than suppress, weak signal detection. Over the past five decades, SR has evolved from a theoretical concept into a practical signal processing tool with applications in mechanical fault diagnosis, biomedicine, geophysics, communications, and renewable energy systems. This paper reviews the latest advancements in the field of SR and organizes them around three core dimensions: (1) innovative system structures, including the evolution from single-stable to multi-stable, delayed, coupled, and cascaded configurations; (2) extended signal- and noise-driven stochastic resonance methods, with a particular focus on adaptive and intelligent strategies for signal detection under complex noise environments and across diverse signal types; and (3) state-of-the-art engineering applications, encompassing weak signal detection under complex noise, fault diagnosis, image denoising, renewable energy systems, and biomedical engineering. This work emphasizes adaptability, robustness, and intelligent integration, and systematically compares the performance of emerging SR models (e.g., Gaussian potential SR, multistable SR) with conventional frameworks. It also highlights the transition of SR from theoretical models to engineering-ready detection frameworks and identifies key challenges, such as the detection of non-periodic and multi-frequency signals under strong noise backgrounds. This updated review provides a critical reference for researchers and engineers seeking to advance SR theory and its practical implementation in complex, real-world systems.