The traditional reliability analysis has such shortcomings that it relies on probability statistics with the large sample statistical data. This study proposes two operation reliability assessment methods which use running condition monitoring information to realize the reliability evaluation under small sample. They are the reliability evaluation methods based on the normalized wavelet information entropy and the damage quantitative identification respectively. Vibration signals of mechanical equipment are decomposed and reconstructed by means of second generation wavelet package to acquire decomposed signals in sub-frequency bands, so that full condition information of running equipment can be adequately used. We take relative energy in each sub-frequency band to calculate normalized information entropy. The reliability degree, an important reliability index, is transformed by using the normalized wavelet information entropy to assess operation reliability for running equipment. A new operation reliability assessment index called membership reliability degree is defined and an operation reliability assessment model is built based on quantitative damage diagnosis. Successful applications have been achieved to assess operation reliability of an oxy-generator compressor and the wheel bearings in electric locomotives, which demonstrated that the proposed approaches are reasonable and effective. The paper provides new approaches without large sample size, which are independent of probability to operation reliability assessment for large machinery.