Operational Effectiveness of Phase-Chronometric and Neurodiagnostic Methods for Controlling Rolling-Element Bearing Degr
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OPERATIONAL EFFECTIVENESS OF PHASE-CHRONOMETRIC AND NEURODIAGNOSTIC METHODS FOR CONTROLLING ROLLING-ELEMENT BEARING DEGRADATION
A. S. Komshin, K. G. Potapov, V. I. Pronyakin, and A. B. Syritskii
UDC 006.91:531.717:681.2.088
In the present study, problems associated with monitoring the condition of rolling-element bearings (REBs) – one of the most common technical devices of rotor units in machines and mechanisms – are considered. A novel approach to metrological support and assessment of the technical condition of rolling-element bearings during operation is presented. Existing approaches are analyzed, including methods of vibration diagnostics, envelope analysis, wavelet analysis, and others. The application of the phase-chronometric and neurodiagnostic methods for monitoring a bearing over its life cycle is considered. For this purpose, a unified format of measurement information was used. The possibility of providing REB diagnostics on the basis of measurement information obtained from the shaft and the cage is evaluated. Keywords: metrological support, measurement, degradation, rolling-element bearing, measuring technology, defect, mathematical model, phase-chronometric method, information efficiency, neurodiagnostics.
Introduction. Currently, rolling-element bearings (REBs) are the most common technical devices for supporting the rotor units of a wide variety of machines and mechanisms. The reliability and performance of these units are largely determined by the bearings, which are generally considered to be their weakest links, thus requiring constant monitoring. According to researchers, bearing failures may occur for several reasons. The most poorly-controlled defects involve those arising from the development of cracks and as a result of the impact of cyclic fatigue loads [1, 2]. The combination of these factors makes the service life of a bearing extremely unpredictable [3]. In [1], a diagram of a typical bearing failure is presented including the initiation stage of defect preceding bearing failure. In [4], the original kernel extreme learning machine (K-ELM) method, designed for analyzing the initial data of bearing vibration, is considered. The advantages of this method involve improved quality of training and the classification of measurement information about the functioning of the bearing for optimizing the various support vectors, i.e., key parameters of an improved K-ELM, as well as the application of a single-layer perceptron. Among the most common methods and tools are vibration diagnostics of bearing elements with envelope analysis under nonstationary conditions [5–8]. Thus, in [7], the application of digital technologies and spectral analysis variations according to the envelope method are demonstrated for obtaining a significant effect when diagnosing individual bearing defects. However, only certain problems of bearing diagnostics at the life-cycle stages are solved by this approach. Some studies in the field of bearing diagnostics are devoted not to the analysis of individual components
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