A Novel Demodulator Design for Signal Processing of Inductive Oil Debris Sensors

Abstract Metal debris from bearing wear occurred in the lubrication system of aircraft engines represents valuable information, which can be utilized to assess the current engine condition and further enable the life-cycle prediction. The demodulation of debris signals consists of two parts: mixing and filtering. In traditional methods, the mixing process is complex to adjust and prone to failure, while the filtering employs the fixed low-pass filtering, which significantly affects the demodulation performance when the debris flow rate changes. To address these issues, this paper proposes a novel demodulation model to enhance the demodulation performance and increase the signal-to-noise ratio of chip signals. Firstly, the circuit innovatively adopts a self-frequency mixing method to avoid the complex phase adjustment required by traditional reference-frequency mixing methods for the improved stability of the circuit. Following that, a digital potentiometer is employed to implement the flow rate filtering, enabling precise control over the filter type and range, thus mitigating signal distortion caused by changes in flow rate and increasing the signal-to-noise ratio. Throughout extensive experiments and data analysis, the self-frequency mixing method improves the signal-to-noise ratio by 0.72 dB and reduces the signal standard deviation by 59.12% as compared to the reference-frequency mixing method. Considering the debris flow rate in the range of 0.2 to 0.5 m/s, the flow rate filtering method improves the signal-to-noise ratio by 19.71 dB and reduces the amplitude standard deviation by 66.18%, as compared to results by the traditional fixed low-pass filtering method. Results demonstrate that the aforementioned circuit design effectively enhances the signal amplitude and stability of inductive oil debris sensors in the wear assessment of mechanical systems and provides useful insights into the development of advanced sensors used in the field of structural health monitoring.