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Wheelset intermittent over-creeps (WIOs), i.e., slips or slides, can decrease the overall traction and braking performance of Electric Multiple Units (EMUs). However, they are difficult to detect and isolate due to their small magnitude and short duration. This paper presents a new index called variable-to-minimum difference (VMD) and a new technique called weighted moving average (WMA). Their combination, i.e., the WMA-VMD index, is used to detect and isolate WIOs in real time. Different from the existing moving average (MA) technique that puts an equal weight on samples within a time window, WMA uses correlation information to find an optimal weight vector (OWV), so as to better improve the index's robustness and sensitivity. The uniqueness of the OWV for the WMA-VMD index is proven, and the properties of the OWV are revealed. The OWV possesses a symmetrical structure, and the equally weighted scheme is optimal when data are independent. This explains the rationale of existing MA-based methods. WIO detectability and isolability conditions of the WMA-VMD index are provided, leading to an analysis of the properties of two nonlinear, discontinuous operators, $\min$ and $\textrm{VMD}_i$. Experimental studies are conducted based on practical running data and a hardware-in-the-loop platform of an EMU to show that the developed methods are effective.