In mechanical transmission, the wear debris produced from different friction couplings is uniformly mixed in lubrication oil, which is a slow degradation process that can be observed by oil spectral analysis. The wear debris in a sample can be categorized into 15 groups of concentration (e.g., Fe, Cu and Mo) in parts per thousand using MOA II (atomic emission spectroscopy) during the sampling epochs. Its level is one of the most common data types used to monitor and evaluate the underlying health state. However, not all the oil spectral data can show the same degradation pattern. Only parts of the spectral oil data can provide useful information for degradation degree characterization. Using all the spectral oil data for condition monitoring will result in unreasonable degradation modeling for condition monitoring and unscheduled maintenance afterwards. Therefore, this article proposes a selection of degradation data based on information entropy to determine the appropriate degradation data for degradation modeling and remaining useful life prediction. Compared with the experiential selection method, the proposed method can characterize the degradation information contained in the multiple spectral oil dataset, leading to a quantitatively selecting the degradation data. The proposed method was verified through a case study involving a degradation dataset of multiple spectral oil data sampled from a power-shift steering transmission (PSST). The result shows that the proposed method can better characterize the degradation degree, which leads to an accurate estimation of the failure time when the transmission no longer fulfills its function.