Torsional Vibration Extraction of Dual-Period Instantaneous Angular Speed Measurement of the Generalized Incremental Encoder

Torsional vibration is key information in monitoring the condition of the shaft system. Using the vector superposition principle, the relationship between the rotation motion and the torsional vibration of the shaft is analyzed. This paper proposes a generalized incremental encoder model and constructs a piecewise function to describe the principle of the pulse output type speed measuring device. The incremental encoder uses a fixed angular increment to stamp the time component of the angular motion of the shaft, thereby establishing a discrete relationship between the angular motion of the shaft and the time component. The relationship between the angular resolution of the encoder and the torsional vibration signal sampling theorem is deduced. The asymmetric under-sampling of the torsional vibration signals is explained from the perspective of signal sampling. According to the index period invariance of the reconstruction of the encoder disc angle sequence, a double-period instantaneous angular speed (IAS) calculation method is proposed, which uses all the time stamps, avoiding the sampling bandwidth idle caused by the single period method, causing the torsional vibration signal to obtain more detailed information, and its analysis bandwidth is twice that of the single-period method. Simulation and experiment verified the correctness and superiority of the research content. Finally, the calculation method was packaged as a functional module and embedded in an online torsional vibration monitoring device applied to two 1000Mw nuclear power turbine generator sets.

Encoder-based weak fault detection for rotating machinery using improved Gaussian process regression

Encoder signal analysis has proven to be a novel and cost-effective tool for the health monitoring of rotating machinery. Nevertheless, how to effectively detect the potential fault utilizing encoder information, especially at an early stage, remains a challenging issue. In light of this limitation, an improved Gaussian process regression analysis is proposed for the weak fault detection of rotating machinery via encoder signal. In this article, the Gaussian process regression model is first introduced to estimate the instantaneous angular speed and its confidence interval. Subsequently, to improve the robustness of Gaussian process regression under weak fault conditions, a spectral density complex kernel is constructed through modeling the spectral density with a mixture of Gaussians. Finally, built upon the eigenvalue decomposition, the optimal inference approach of improved Gaussian process regression is proposed. Compared with other regression methods, the major contribution is that the new method not only enhances the weak fault-related features but also sets their confidence interval adaptively. Using the proposed improved Gaussian process regression, the interference components are suppressed, while the fault-related instantaneous angular speed outliers are accurately detected. In addition, the significance of fault can be quantitatively evaluated according to the confidence level of the improved Gaussian process regression. The simulated and experimental analyses manifest that the proposed improved Gaussian process regression method can effectively identify the early weak fault. It may offer an effective tool for early fault detection of rotating machinery in industrial applications.