| Citation: | Xue Qian, Wu Zhipeng, Yin Haicheng. Method for extracting signal of lubricating oil wear debris based on fluxgate sensor and CEEMDAN-DWT[J]. Journal of Aerospace Power, 2026, 41（8）：20240785 doi: 10.13224/j.cnki.jasp.20240785
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Online monitoring technology for oil debris is crucial for predicting and assessing wear failures in engine components. This paper designed a novel magnetic inductive sensor based on the principle of fluxgate. It converted the minute magnetic disturbances caused by wear debris into magnetic induction intensity variations, and further into measurable induced voltage signals. To detect the weak debris signals effectively, we proposed a method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and discrete wavelet transform (DWT) for extracting weak magnetic anomaly signals of debris. The CEEMDAN algorithm addressed the mode mixing issue in empirical mode decomposition (EMD) by adding adaptive white noise, thereby enhanced the accuracy and stability of decomposition. In addition, with the multi-scale analysis capability of DWT, the weak magnetic anomaly signals buried in noise were captured and extracted effectively. Prototype experiment results indicated that after extraction, the measured data of wear debris passing near the center of the pipeline (which is further from the sensor and has lower SNR) exhibited SNR enhancements ranging from 1 to 10 dB. With a signal fidelity range of 0.6 to 0.9, the denoising gain ranges from 0.5 to 1.
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