  In the domain of rotating machinery, bearings are vulnerable to different
mechanical faults, including ball, inner, and outer race faults. Various
techniques can be used in condition-based monitoring, from classical signal
analysis to deep learning methods. Based on the complex working conditions of
rotary machines, multivariate statistical process control charts such as
Hotelling's $T^2$ and Squared Prediction Error are useful for providing early
warnings. However, these methods are rarely applied to condition monitoring of
rotating machinery due to the univariate nature of the datasets. In the present
paper, we propose a multivariate statistical process control-based fault
detection method that utilizes multivariate data composed of Fourier transform
features extracted for fixed-time batches. Our approach makes use of the
multidimensional nature of Fourier transform characteristics, which record more
detailed information about the machine's status, in an effort to enhance early
defect detection and diagnosis. Experiments with varying vibration measurement
locations (Fan End, Drive End), fault types (ball, inner, and outer race
faults), and motor loads (0-3 horsepower) are used to validate the suggested
approach. The outcomes illustrate our method's effectiveness in fault detection
and point to possible broader uses in industrial maintenance.
