Research On Noise Reduction Method Of Inductive Debris Monitoring Sensor

Oil debris monitoring technology is an important means to achieve the health status monitoring of mechanical equipment.By monitoring the debris in the oil to obtain the equipment’s wear condition,it is possible to realize the early warning and life prediction of the mechanical equipment and effectively ensure the safe operation of the mechanical equipment.The inductive debris monitoring sensor is a usual method for monitoring debris of equipment,with the advantages of simple structure,low input cost,and rich debris information obtained,which has received widespread attention.However,the inductive debris monitoring sensors are susceptible to noise interference in the actual application,which limits the reliability and stability of the sensor monitoring results.In order to improve the anti-noise performance of the inductive debris monitoring sensor,the noise reduction method of the inductive debris monitoring sensor is researched from two aspects: the optimization of the sensor structure and the improvement of the signal processing algorithm.First,this thesis introduces the structure and working principle of a single-coil inductive debris monitoring sensor based on a static magnetic field.This dissertation proposes a debris monitoring sensor structure based on dual induction coils to improve the noise immunity of the sensor.The finite element simulation software is used to analyze the magnetic field of the parallel dual induction coil sensor and the output signal of the induction coil.This treatise analyzes the sensor’s output signal and establishes a mathematical model of the signal.At the same time,the magnetic pole shape of the sensor is optimized,and the magnetic field intensity in the sensor detection area effectively improves the output signal of the sensor.Secondly,to improve the sensor output signal’s signal-to-noise ratio,this thesis proposes an adaptive noise reduction method based on the two-way LMS(Least Mean Square)algorithm.A low-pass filter is used to eliminate high-frequency random noise and high-frequency harmonic noise in the signal.The discrete spectrum correction algorithm constructs the harmonic estimation signal,and the harmonic estimation signal is superimposed with the sensor output signal to achieve harmonic suppression.Simultaneously,the LMS adaptive algorithm is improved,and a two-way LMS adaptive algorithm is proposed to improve the noise reduction performance of the algorithm in the convergence process.The detection signal and reference signal after low-pass filtering and harmonic suppression are used as the input signal of the two-way LMS adaptive algorithm,thereby improving the signal-to-noise ratio of the signal and suppressing impulse noise.Finally,the sensor structure and noise reduction method based on dual induction coils are verified through debris monitoring experiments.The oil debris monitoring experiment shows that the noise reduction method based on the dual induction coil sensor proposed in this paper can effectively improve the signal-to-noise ratio of the sensor output signal and maintain the integrity of the residue signal,which improves the actual application of the oil wear particle monitoring sensor reliability and stability.