Identification of Thrust of Ship Propulsion Shaft by Kalman Filter and Strain Signal
Online,real-time,and accurate monitoring of propeller thrust is of great significance to the hull-engine-propeller matching design,rapid prediction of the ship,and health management of the shaft.However,due to the influence of measurement noise such as shafting vibration,environmental interference,and so on,the weak strain signal generated by the propeller thrust is easily submerged by the measurement noise,which makes it difficult to measure the thrust accurately.Currently,some commonly used signal denoising methods,such as the Fourier analysis and the wavelet analysis,only consider the measurement data,without considering the mechanical mechanism hidden in the measured data.Unlike this kind of denoising method,the Kalman filter can consider both the measurement data and the mechanic's mechanism,thus realizing minimum-variance unbiased estimation.Therefore,it has higher estimation accuracy.In this study,a high-precision online identification method of propeller thrust is proposed using the Kalman filter and strain measurement signal.Taking the three working conditions of constant speed,variable speed and low frequency fluctuating speed as examples,the proposed method's thrust identification accuracy and robustness under different signal-to-noise ratios are studied.The research shows that when the signal-to-noise ratio is only 20 dB,the maximum relative error of thrust identification is only 4.85%.Hence,the proposed method still has high identification accuracy and robustness at a low signal-to-noise ratio.Besides,the method proposed in this paper belongs to the time domain identification method.It can track the thrust change in real time under sudden conditions,such as the sudden change of rotation speed and the twisting of the propeller with the fishing net,so it can be used for online and real-time monitoring of the propeller thrust and shafting state.
vibration and waveKalman filteringthrust identificationmeasurement of strainonline monitoring
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