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2 Polanowski S.

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Application of movable approximation and wavelet decomposition to smoothing-out procedure of ship engine indicator diagrams

Polanowski S.

Polish Maritime Research

2007

nr 2

12-17

In this paper - on the basis of indicator diagram processing taken as an example - were shown possibilities of the smoothing-out and decomposing of run disturbances with the use of the movable multiple approximation based on the least squares criterion. The notion was defined of movable approximating object and constraints used to form approximation features. It was demonstrated that the multiple approximation can be used to decompose disturbances out of an analyzed run. The obtained smoothing-out results were compared with those obtained from full-interval approximation of runs by means of splines as well as wavelet decomposition with using various wavelets, Wavelet Explorer and Mathematica software. Smoothing-out quality was assessed by comparing runs of first derivatives which play crucial role in the advanced processing of indicator diagrams.

Processing methods of measured runs of torque and angular speed of combustion engine propulsion system

Polanowski S.

Polish Maritime Research

2006

nr 4

15-22

In this paper four processing methods of cyclic data runs, namely : Fourier interpolation and transformation, least-squares approximation by means of trigonometric polynomials , synchronous averaging and movable approximation , were presented by using, as an example, discretely measured runs of torque and angular speed. It was demonstrated that for extracting a useful signal out of disturbance background and decomposing the disturbances the appropriate methods are the synchronous averaging and multiple movable approximation. To emerge significant spectral lines from a spectrum the least-squares approximation by using trigonometric polynomials can be applied. The method contains a criterion for signal filtration and is insensitive to disturbances, run truncation and sampling irregularity. Fast Fourier Transform (FFT) may have an auxiliary significance for preliminary spectral analysis. Its main disadvantage is that in order to build a smoothing-out filter its parameters should be determined in advance, e.g. by applying one of the remaining methods.