doi: 10.52899/24141437_2026_01_105
UDK: 621.822:681.518

PF-Bearing Analyzer: Algorithmic Implementation of the Peak Factor Method for Diagnosing and Predicting the Technical Condition of Rolling Bearings

Хруцкий О. В., Егорова А. Д., Полежаев А. .
Article language:
Citation Link: Polezhaev A, Egorova AD, Khrutsky OV. PF-Bearing Analyzer: Algorithmic Implementation of the Peak Factor Method for Diagnosing and Predicting the Technical Condition of Rolling Bearings. Transactions of the Saint Petersburg State Marine Technical University. 2026;5(1):105–113. DOI: https://doi.org/10.52899/24141437_2026_01_105 EDN: UBBMKM

Annotation

BACKGROUND: The relevance of this work is determined by the need to develop open and mathematically substantiated tools for assessing the technical condition of power-mechanical equipment. Existing expensive commercial systems often operate as black boxes, which limits the possibilities for their analysis and flexible adjustment by engineers and researchers. AIM: To provide a detailed review and description of the PF-Bearing Analyzer algorithmic framework intended for diagnosing and predicting the technical condition of rolling support bearings. METHODS: The methodology is based on the analysis of vibration signals through calculation of the statistical peak factor parameter within a specified high-frequency band. The data-processing core incorporates digital filtering methods (a Butterworth filter implemented in SOS form), as well as robust regression methods (L1 regression) for forecasting and parameterization of the threshold model. RESULTS: The paper examines the theoretical foundations of the method, the physics of defect formation in rolling support bearings, the corresponding mathematical apparatus, and its algorithmic implementation. The presented material explains the operating principles of the framework and substantiates the choice of the data-processing algorithms used. CONCLUSION: The developed algorithmic framework provides specialists with an open, flexibly configurable tool for diagnosing rolling support bearings in power-mechanical equipment and represents an effective alternative to closed commercial systems. Keywords: vibration diagnostics; peak factor; rolling support bearings; equipment maintenance; forecasting; Python; digital filtering; statistical diagnostics

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