  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.
