Vibration of Shaft Supported by Ball Bearing (Vibration of Rotating Machine)

1969 ◽  
Vol 72 (610) ◽  
pp. 1532-1536
Author(s):  
Akiyoshi TAMURA
Keyword(s):  
Ball Bearing ◽  
Special Issue ◽  
Rotating Machine

Vibration of a High-Speed Ball Bearing (Vibration of Rotating Machine)

1969 ◽  
Vol 72 (610) ◽  
pp. 1586-1593
Author(s):  
Akiyoshi TAMURA ◽  
Katsuaki KIKUCHI ◽  
Michio OMURA
Keyword(s):  
High Speed ◽  
Ball Bearing ◽  
Special Issue ◽  
Rotating Machine

Analysis of Vibration of Super Precision Roller Bearing (Vibration of Rotating Machine)

1969 ◽  
Vol 72 (610) ◽  
pp. 1579-1585
Author(s):  
Yoshiaki GOTO ◽  
Toshiaki WATANABE ◽  
Toshiyuki SAKATA
Keyword(s):  
Roller Bearing ◽  
Special Issue ◽  
Rotating Machine

On the Measuring Weight to Check the Run-Out of the Angular Contact Ball Bearing

1970 ◽  
Vol 73 (619) ◽  
pp. 1177-1178
Author(s):  
Masanori HONDA ◽  
Kinjiro HIGUCHI ◽  
Tamehiro KOTORII
Keyword(s):  
Ball Bearing ◽  
Special Issue ◽  
Angular Contact Ball Bearing

scholarly journals Use of the Correlation Coefficient for Rotating Machine Monitoring

2021 ◽  
pp. 30-37
Author(s):  
Aimé Joseph Oyobé Okassa ◽  
Colince Welba ◽  
Jean Pierre Ngantcha ◽  
Pierre Ele
Keyword(s):  
Correlation Coefficient ◽  
Ball Bearing ◽  
Production Systems ◽  
Rotating Machines ◽  
Decision Tools ◽  
Vibration Data ◽  
Rotating Machine ◽  
Reference Window ◽  
Approximation Coefficients ◽  
Types Of Information

The use of electronics and computer technology in production systems has greatly improved the quality of our industrial products. The productivity of these installations is a function of the maintenance quality applied to the equipment. Several methods are used to monitor the functioning of industrial installations. One of these methods is vibration analysis. The vibration signals from the rotating machines support several types of information related to the working state of the production tool. The processing of this information makes it possible to have decision tools for maintenance. In this work, we propose a method of anticipating the maintenance of rotating machines. The algorithm we propose starts with the removal of 512 point windows during the running time of the ball bearing. Each signal is decomposed by DWT: we obtain the approximation coefficients. These coefficients make it possible to determine the correlation coefficient between the so-called reference window and the other windows following the functioning of the ball bearing. The correlation coefficient is then the fundamental element of the decision. This algorithm has been applied to real vibration data and the results are encouraging.

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