Frequency Energy Analysis in Detecting Rolling Bearing Faults

Abstract

Component failure analysis is sometimes difficult to directly detect due to the complexity of an operating system configuration. Raw time series data is not enough in some cases to understand the type of fault or how it is progressing. The conversion of data from the time domain to the frequency domain assists researchers in making a more discernible difference for detecting failures, but depending on the manufacturing equipment type and complexity, there is still a possibility for inaccurate results. This research explores a method of classifying rolling bearing faults utilizing the total energy gathered from the Power Spectral Density (PSD) of a Fast Fourier Transform (FFT). Using a spectrogram over an entire process cycle, the PSD is swept through time and the total energy is computed and plotted over the periodic machine cycle. Comparing with a baseline set of data, classification patterns emerge, giving an indication of the type of fault, when a fault begins and how the fault progresses. There is a separable difference in each type of fault and a measurable change in the distribution of accumulated damage over time. A roller bearing is used as a validating component, due to the known types of faults and their classifications. Traditional methods are used for comparison and the method verified using experimental and industrial applications. Future application is justified for more complex and not so well-understood systems.