Rolling element bearing is one of the important components in rotary machines. Although a significant quantum of work has been done on bearing defect monitoring, estimation of defect size in bearing elements is still a challenge. Vibration signals resulting from rolling element bearing defects, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the fault. The proposed research work is examined under laboratorial set-up keeping rotating speed and or load variation remains unchanged. Hence this paper represents the online monitoring approach of identifying outer, inner race and ball defects in the ball bearing based on the location, count and size of the defects by incorporating PLC with LABVIEW platform. A monitoring tool acquires experimental data from a bearing vibration control test rig. An accelerometer captures the signal from the bearing outer ring then it is processed using PCI-4451 National Instruments data acquisition board and LabVIEW software. To analyze the peak value of the vibration signals during faults, the following 0.5, 1-and 2-mm defect size were undertaken. Through real-time experimentation the minimum and maximum range of vibration amplitude values were observed by varying the defect counts as single and multi-nature basis on three zones like inner, outer race and ball of the bearings. Its experimental numerical results reveal that of ball bearing without defects holds values between 10 mV to 16 mV and it also confirms that the ball defects hold higher amplitude vibration on comparison with defects on the inner and outer race vibration signals.
This paper aims to explore experimental studies on the NOx removal process by using pilot plant packed column experimental hardware. Physical modeling based on chemical absorption equations is used to estimate the diameter concerning the height and L/G ratio. Hydrogen peroxide is used as the additive for achieving high NOx removal efficiency. The absorbent entering into the packed column has been controlled by varying its flow rate through the fractional order controller. The FOCDM-PIλDµ controller tuning parameters such as KP, τI, τD are determined using CDM (Coefficient Diagram Method) PID control strategy and the additional parameters of FOCDM-PIλDµ controller such as λ and µ are determined based on the PSO algorithm. The comparative analysis is performed with classical controllers like ZN-PID along with the CDM-PID controllers.
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