Condition Monitoring of Ball Bearings Using Vibration Analysis

Abstract: In today’s industry, whether it is run-to-failure, preventive, or predictive, mainte-
nance is one of the major expenses in the production process. Ball bearings are
one of the most vital elements in machinery and maintenance cost for replacement
of those elements with interrupting the production is one of the most expensive.
Establishing predictive maintenance for those rolling bearings by detecting the pos-
sible defects and monitoring the current condition will enable the industry to use the
maximum life span of those mechanical devices and reduce the cost of maintenance
considerably.
Within the ongoing project of condition monitoring by using vibration analysis at
Rubico AB, this thesis work aims to understand and implement a new algorithm
step-by-step, first as off-line processing, and then on a fixed-point digital signal
processor to analyze the measured data from industry. Different approaches for
maximizing the performance of the algorithm are tested, compared; and the results
from both off-line floating point precision and fixed-point implementation are eval-
uated.
By running the method on different data sets from industry, it has been shown
that the patented algorithm manages to detect the defects on the inner or outer
race of the ball bearings without a priori knowledge about the measurement object
and environment. The concept for implementation on a fixed-point digital signal
processor is also proven.