A novel fault detection and diagnosis method based on kernel generalized discriminant analysis (kernel GDA, KGDA) is proposed in order to solve the problem of turbine fault detection and diagnosis. Through kernel GDA, the data is mapped from original space to the high-dimensional feature space. Then the statistic distance between normal data and test data is constructed to detect whether a fault is occurring. If a fault has occurred, similar analysis is used to identify type of the faults. The proposed method is scalable to different steam turbine and rotating machineries. Its effectiveness is evaluated by simulation results of vibration signal fault dataset.
In this study, the fermentation broth of the recombinant Pichia pastoris strain ncy-2 was studied. After pretreatment, separation, and purification, lysozyme was optimized using biofilm and ion exchange separation. Finally, lysozyme dry enzyme powder was prepared by concentrating and vacuum drying. The removal rate of bacterial cells was 99.99% when the fermentation broth was centrifuged at low temperature. The optimum conditions were: transmembrane pressure of 0.20 MPa, pH 6.5, 96.6% yield of lysozyme, enzyme activity of 2612.1 u/mg, which was 1.78 times higher than that of the original enzyme; D152 resin was used for adsorption and elution. Process conditions were optimized: the volume ratio of resin to liquid was 15%; the adsorption time was 4 h; the concentration of NaCl was 1.0 mol/L; the recovery rate of lysozyme activity was 95.67%; the enzyme activity was 3879.6 u/mL; and the purification multiple was 0.5, 3.1 times of the original enzyme activity. The enzyme activity of lysozyme dry enzyme powder was 12,573.6 u/mg, which had an inhibitory effect on microsphere lysozyme. Its enzymatic properties were almost the same as those of natural lysozyme, which demonstrated good application prospects and production potential.
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