This paper presents a novel machine performance degradation scheme based on fixed cycle features test (FCFT). Instead of monitoring the machine under constant working load, FCFT introduces a new testing method which obtains data during the transient periods of different working loads. A novel performance assessment method based on those transient data without failure history is proposed. Wavelet packet analysis (WPA) is applied to extract features which capture the dynamic characteristics from the non-stationary vibration data. Principal component analysis (PCA) is used to reduce the dimension of the feature space. Gaussian mixture model (GMM) is utilized to approximate the density distribution of the lower-dimensional feature space which consists of the major principal components. The performance index of the machine is calculated based on the overlap between the distribution of the baseline feature space and that of the testing feature space. Bayesian information criterion (BIC) is used to determine the number of mixtures for the GMM and a density boosting method is applied to achieve better accuracy of the distribution estimation. A case study for a chiller system performance assessment is used as an example to validate the effectiveness of the proposed method.