Study on Performance of Remanufactured Engine Based on EMD
According to the non-stationary property of vibration signal, the vibration signal is decomposed by empirical mode decomposition (EMD). And this method is applied to study the remanufacturing level of engine. Based on the decomposition of vibration signal, correlation coefficient is introduced to study the correlation between IMF (Intrinsic Mode Function) components and original signal, and dynamic structure of IMF components is analyzed by correlation dimension. In order to study the remanufacturing level of engine, correlation coefficient and correlation dimension are considered in the function of IMF, and an index of vibration intensity is proposed. A corresponding relationship is established between remanufacturing level and vibration intensity. This method has been proved that the running state of engine can be reflected by vibration intensity, and this index can be used to evaluate the remanufacturing level of engine. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3896
Study on Iterative Learning Control bandwidth tuning using particle swarm optimization technique for high precision motion
This paper utilized the improved particle swarm optimization (IPSO) technique for adjusting the gains of PID controller, Iterative Learning Control (ILC) and the bandwidth of zero-phase Butterworth filter of the ILC. The conventional ILC learning process has the potential to excite rich frequency contents and try to learn the error signals. However the learnable and unlearnable error signals should be separated for bettering control process as repetition goes. Producing high frequency error condition should be avoided before the phase margin caused any trouble. The filter bandwidth should be changed at every repetition. Thus adaptive bandwidth in the ILC controller with the aid of IPSO tuning is proposed here. Simulation results show the new controller can cancel the errors efficiently as repetition goes. The correlation coefficient validates the learnable compensated error signal for the trajectory is adaptively decomposed from previous error history via the bandwidth tuning mechanism in next repetition. The learnable error signals of the Intrinsic Mode Functions (IMFs) through the Empirical Mode Decomposition (EMD) correlate efficiently with reduced tracking error as repetition goes. Simulation results validate the application for positioning of a robot arm system for high precision motion control.
Evaluation of Impact of Yarn Count and Stitch Length on Pilling, Abrasion, Shrinkage and Tightness Factor of 1 ? 1 Rib Cotton Knitted Fabrics
This study investigates the impact of yarn count and stitch length of 100% cotton knitted fabrics. 1 × 1 ribs structured fabrics were used in this research work. The stitch length 2.50 mm, 2.52 mm, 2.60 mm, 2.70 mm, 2.80 mm and 2.96 mm were used of the yarn count of 26Ne, 28Ne and 36Ne. The areal densities of the grey fabrics were in various amounts. The impacts of yarn count and stitch length were measured concerning pilling resistance, abrasion resistance, and shrinkage and tightness factor. The result showed that the fabric pilling grade decreased while number of cycle increased. Abrasion resistance decreased but length wise shrinkage increased despite the fact that the stitch length increased. The results also exposed that the tightness factor of the fabric increased with the increase of the stitch length in both dyeing and washing state and finished state compared to grey state for the yarn count 26Ne and homogeneous result not found for yarn count 28Ne and 36 Ne. Evaluation of Impact of Yarn Count and Stitch Length on Pilling, Abrasion, Shrinkage and Tightness Factor of 1 × 1 Rib Cotton Knitted Fabrics