Robustness evaluation of acceleration-based early rub detection methodologies with real fluid-induced noise

The early detection of rotor-casing rub allows to prevent permanent damage and even catastrophic failure of rotating machines, saving maintenance costs and decreasing downtime. Very often, aeroderivative gas turbines can be monitored only with accelerometers on the casing, which provide signals that contain both stochastic and deterministic noise that masks the vibration measurements. Although real vibration data from turbomachines present a background noise that is neither white nor Gaussian, the rule of thumb is to evaluate the robustness of fault detection under white signal noise, which may lead to estimation errors of such robustness. Here we evaluate and compare the performance of several signal processing methodologies for very early rub detection on accelerometer signals under fluid-induced signal noise due to turbulent fluid excitation. The independence between the fluid-induced vibrations and the rub and unbalance vibration was proved, and experimental data of fluid-induced noise were added as signal noise to clean rub signals. The robustness and sensitivity of rub detection under fluid-induced noise were calculated and compared. This study proves the superiority of reassigned time–frequency analysis methods such as the Wavelet Synchrosqueezed Transform for very early rub detection as compared with traditional time–frequency analysis methods and with the Fourier Transform. To the authors’ knowledge, this is the first time that non-Gaussian fluid-induced signal noise is used to evaluate fault detection performance.