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This paper presents an analysis of the fracture accident of a cylindrical roller bearing cage used in a charging pump in a nuclear power plant. The causes and mechanisms of bearing cage breakage were investigated by material failure analysis and simulation calculations. Macroscopic observation results confirmed that the cage fracture occurred at th...
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Context 1
... force contribution of the nylon cage under two lubrication conditions (Table 5) was also calculated. Under the same operating conditions as those of the steel cage, the maximum tensile stress of the cage was at the corner of the front cross beam with a value of 5.9 MPa, as shown in Figure 11a. ...Context 2
... force contribution of the nylon cage under two lubrication conditions (Table 5) was also calculated. Under the same operating conditions as those of the steel cage, the maximum tensile stress of the cage was at the corner of the front cross beam with a value of 5.9 MPa, as shown in Figure 11a. ...Context 3
... while its yield strength is 54.88 MPa, which is much higher than the calculation results above, it can be assumed that the nylon cage has a high safety margin. The force contribution of the nylon cage under two lubrication conditions (Table 5) was also calculated. Under the same operating conditions as those of the steel cage, the maximum tensile stress of the cage was at the corner of the front cross beam with a value of 5.9 MPa, as shown in Figure 11a. ...Citations
... Here, issues such as plastic deformation, wear, cracks, fractures arising also from insufficient lubrication or contamination can lead to failures in bearing components [5,6]. In nuclear power plants and gas turbines, bearings are critical components whose health is directly linked to the safety of the entire plant [7][8][9]. As a result, condition monitoring and fault diagnosis of rolling bearings has become an essential area of development and engineering research. ...
In this work, we present a diagnosis system for rolling bearings that leverages simultaneous measurements of vibrations and machine rotation speed. Our approach combines the robustness of simple time domain methods for fault detection with the potential of machine learning techniques for fault location. This research is based on a neural network classifier, which exploits a simple and novel preprocessing algorithm specifically designed for minimizing the dependency of the classifier performance on the machine working conditions, on the bearing model and on the acquisition system set-up. The overall diagnosis system is based on light algorithms with reduced complexity and hardware resource demand and is designed to be deployed in embedded electronics. The fault diagnosis system was trained using emulated data, exploiting an ad-hoc test bench thus avoiding the problem of generating enough data, achieving an overall classifier accuracy larger than 98%. Its noteworthy ability to generalize was proven by using data emulating different working conditions and acquisition set-ups and noise levels, obtaining in all the cases accuracies greater than 97%, thereby proving in this way that the proposed system can be applied in a wide spectrum of different applications. Finally, real data from an on-line database containing vibration signals obtained in a completely different scenario are used to demonstrate the distinctive capability of the proposed system to generalize.
