The growing demand for predictive maintenance in industrial rotating machinery has accelerated the development of embedded intelligent bearings, a breakthrough technology that enables real-time, high-precision health monitoring. Unlike traditional empirical configurations, this study explicitly incorporates sensor detection sensitivity and linearity into the multi-physics evaluation system. Furthermore, Factor Analysis (FA) is used to reduce the dimensionality of multi-dimensional evaluation indicators, thereby establishing a comprehensive decision-making framework. A key finding is that configuring measurement points outside the bearing load zone yields negligible monitoring benefits. This paper presents an optimization methodology to determine the spatial configuration of dual measurement points in embedded intelligent bearings. The methodology integrates static mechanical analysis, thermal characterization, and sensor-detection modeling via coupled Finite Element Analysis (FEA). Validation studies using a 6306 deep-groove ball bearing demonstrate that the optimized dual-sensor configuration (40° from bearing bottom) significantly improves monitoring accuracy, exhibiting a 22.40% reduction in rotational speed monitoring error compared to unoptimized dual-sensor configurations (30°) and an 88.91% improvement over single-sensor solutions. Similarly, load monitoring error is reduced by 20.50% compared to unoptimized configurations and 74.12% compared to single-sensor systems. These findings validate the feasibility and effectiveness of the proposed optimization methodology in industrial settings.
Chen et al. (Mon,) studied this question.
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