Industrial signal measurement and processing are increasingly being deployed in control performance assessment, particularly in oscillation monitoring applications. In this paper, we present a novel oscillation detector, which mainly leverages the recently developed fast multivariate empirical mode decomposition (FMEMD). Our use of FMEMD is motivated by the following facts: (i) considerably fewer techniques are now available for monitoring both single-loop and plant-wide oscillations; (ii) the presence of noise and signal intermittency can severely deteriorate the detecting performance of most decomposition-based time-frequency methods; and (iii) the currently popular multivariate empirical mode decomposition (MEMD)-based methods are limited by high computational load and overdecomposition. The proposed approach is tested against the related techniques, including empirical mode decomposition (EMD), direct multivariate intrinsic time-scale decomposition (DMITD), and the noise-assisted MEMD (NA-MEMD) by extensive simulations, and is shown to offer considerable progress, especially in noise robustness, mode-mixing reduction, and intermittent and nonlinearity-induced oscillation extraction. The validity of this work is finally demonstrated by industrial cases.