The effects of intermediate annealing (IA) and the final cold rolling (CR) condition on the microstructure and sagging resistance during brazing were investigated using three layer clad sheets composed of the Al-7.5 wt.%Si alloy (filler, thickness: 10 μm)/Al-1.3 wt.%Mn based alloy (core, 80 μm)/Al-7.5 wt.%Si alloy (filler, 10 μm). Also, the effect of 1.2~2 wt.% Zn addition into the core on the sagging resistance of the clad sheets was determined. It was revealed that all the clad sheets fabricated by the optimum condition (IA at 690 K and CR to 20~45%) show excellent sagging resistance with a limited erosion due to the formation of a coarsely recrystallized grain structure in the core during brazing. It was also revealed that the recrystallization behavior of the Al-1.3 wt.%Mn based alloy is hardly affected by the addition of 1.2~2 wt.%Zn during the brazing cycle. Therefore, the sagging resistance of the clad sheets is found to be governed not by the Zn content added in the Al-1.3wt.%Mn based core, but by the intermediate annealing and final cold rolling condition.

A feature extraction and fusion algorithm was constructed by combining principal component analysis (PCA) and linear discriminant analysis (LDA) to detect a fault state of the induction motor. After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment, the reference data were used to produce matching values. In a diagnostic step, two matching values that were obtained by PCA and LDA, respectively, were combined by probability model, and a faulted signal was finally diagnosed. As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief, it shows excellent performance under the noisy environment. The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.

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