Estimation of Unmeasurable Vibration of a Rotating Machine Using Kalman Filter

Neda Neisi (Corresponding Author), Vesa Nieminen, Emil Kurvinen, Ville Lämsä, Jussi Sopanen

    Research output: Contribution to journalArticleScientificpeer-review


    Rotating machines are typically equipped with vibration sensors at the bearing location and the information from these sensors is used for condition monitoring. Installing additional sensors may not be possible due to limitations of the installation and cost. Thus, the internal condition of machines might be difficult to evaluate. This study presents a numerical and experimental study on the case of a rotor supported by four rolling element bearings (REBs). As such, the study resembles a complex real-life industrial multi-fault scenario: a lack of information, uncertainties, and nonlinearities increase the overall complexity of the system. The study provides a methodology for modeling and analyzing complicated systems without prior information. First, the unknown model parameters of the system are approximated using measurement data and the linearized model. Thereafter, the Unscented Kalman Filter (UKF) is applied to the estimation of the vibration characteristics in unmeasured locations. As a result, the estimation of unmeasured vibration characteristics has a reasonable agreement with the rotor whirling, and the estimated results are within a 95% confidence interval. The proposed methodology can be considered as a transfer learning method that can be further used in other identification problems in the field of rotating machinery.
    Original languageEnglish
    Article number1116
    Number of pages23
    Issue number12
    Publication statusPublished - 24 Nov 2022
    MoE publication typeA1 Journal article-refereed


    • rotating machinery
    • state estimation
    • unscented Kalman filter (UKF)
    • measurement
    • simulation


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