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<p>Rolling bearings are critical components in rotating machinery, but conventional fault diagnosis methods often struggle to maintain accuracy under time-varying speed conditions and severe background noise. To address these challenges without relying on physical tachometers, this paper proposes a novel approach: the Improved Teager Energy Operator-Sideband Reconstruction (ITEO-SR) method. The proposed framework advances existing techniques through three key innovations: (1) a Dynamic Path Optimization (DPO) algorithm is utilized to extract the instantaneous rotational speed directly from the time–frequency representation of the highly corrupted vibration signal; (2) an adaptive-scale Improved Teager Energy Operator (ITEO) featuring O(1) time complexity is introduced to amplify transient fault impulses while adaptively suppressing global false peaks and abrupt noise spikes; and (3) a Teager Energy Traversal Parameter (TETP) index is developed to automate the reconstruction of Characteristic Frequency Spectrograms (CFS) for high-resolution fault coefficient identification. Numerical simulations (SNR = −10&#xa0;dB) and experimental validations under linear acceleration (0–1200&#xa0;rpm) confirm the superiority of the proposed method. Quantitative results demonstrate that the proposed method achieves highly accurate Fault Characteristic Coefficient (FCC) identification with a minimal relative error of 1.9%. Furthermore, it maintains superior local feature prominence (a Q value of 19.94, compared to 17.82 for traditional Hilbert methods) and reduces computational demodulation time by nearly three-fold (1.24 vs. 3.65&#xa0;s for conventional envelope techniques). This robust, computationally efficient, and tacho-less approach offers a highly practical solution for the real-time condition monitoring of variable-speed machinery.</p>

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Bearing fault diagnosis based on ITEO-SR method under time-varying speed conditions

  • 期刊论文

  • 发表日期：

    2026年7月2日

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  • Cong Feiyun,
  • Wu Junzhan,
  • Wu Jiani,
  • Yang Bo,
  • Zhao Weizheng,
  • Wu Henggang,
  • Lin Wei

摘要

Rolling bearings are critical components in rotating machinery, but conventional fault diagnosis methods often struggle to maintain accuracy under time-varying speed conditions and severe background noise. To address these challenges without relying on physical tachometers, this paper proposes a novel approach: the Improved Teager Energy Operator-Sideband Reconstruction (ITEO-SR) method. The proposed framework advances existing techniques through three key innovations: (1) a Dynamic Path Optimization (DPO) algorithm is utilized to extract the instantaneous rotational speed directly from the time–frequency representation of the highly corrupted vibration signal; (2) an adaptive-scale Improved Teager Energy Operator (ITEO) featuring O(1) time complexity is introduced to amplify transient fault impulses while adaptively suppressing global false peaks and abrupt noise spikes; and (3) a Teager Energy Traversal Parameter (TETP) index is developed to automate the reconstruction of Characteristic Frequency Spectrograms (CFS) for high-resolution fault coefficient identification. Numerical simulations (SNR = −10 dB) and experimental validations under linear acceleration (0–1200 rpm) confirm the superiority of the proposed method. Quantitative results demonstrate that the proposed method achieves highly accurate Fault Characteristic Coefficient (FCC) identification with a minimal relative error of 1.9%. Furthermore, it maintains superior local feature prominence (a Q value of 19.94, compared to 17.82 for traditional Hilbert methods) and reduces computational demodulation time by nearly three-fold (1.24 vs. 3.65 s for conventional envelope techniques). This robust, computationally efficient, and tacho-less approach offers a highly practical solution for the real-time condition monitoring of variable-speed machinery.