Sacerdoti, D.; Strozzi, M.; Secchi, C. A Comparison of Signal Analysis Techniques for the Diagnostics of the IMS Rolling Element Bearing Dataset. Appl. Sci.2023, 13, 5977.
Sacerdoti, D.; Strozzi, M.; Secchi, C. A Comparison of Signal Analysis Techniques for the Diagnostics of the IMS Rolling Element Bearing Dataset. Appl. Sci. 2023, 13, 5977.
Sacerdoti, D.; Strozzi, M.; Secchi, C. A Comparison of Signal Analysis Techniques for the Diagnostics of the IMS Rolling Element Bearing Dataset. Appl. Sci.2023, 13, 5977.
Sacerdoti, D.; Strozzi, M.; Secchi, C. A Comparison of Signal Analysis Techniques for the Diagnostics of the IMS Rolling Element Bearing Dataset. Appl. Sci. 2023, 13, 5977.
Abstract
Condition monitoring is today a crucial and unavoidable practice related to the use of widespread mechanical components as rolling element bearings. Despite the diffusion of new data-driven techniques to analyze vibrational signals and detect the state of health of the system, mathematical tools, deriving more or less directly from the Fourier transform and defined either on the time or the frequency domain or on a combination of them (i.e. signal-based techniques), remain still essential and irreplaceable in the light of the deep and detailed information they provide. In this paper the diagnostic efficacy of an ample spectrum of these methods is investigated, applying them to study faults occurrence in the first bearing dataset released by NASA IMS Center. Thanks to their analytical definition, which is formulated taking into consideration to some extent the mathematical nature of the signal, faults signatures can be clearly identified and their temporal development can be fully traced, confirming their ability as powerful means to constantly monitor the state of a system even in real-time applications.
Keywords
vibrations; condition monitoring; rolling element bearings; signals; fault diagnosis
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
