Recent advances in sensor technology and computing power allow for the generation/utilization of larger and more diverse sets of data. These developments enable the creation of data-driven models that can support real-time decision making. Such a decision aid can allow for predictive maintenance (PdM) to be undertaken on a much greater scale in manufacturing plants. PdM includes data-driven prognostics and health management (PHM). To enhance the performance of a prognostic model, one key task is to collect high-quality data, and in the past this has often involved using a feature extraction method to get meaningful information from a large noisy dataset. However, such methods may not handle noisy data well or address measurement errors adequately. Consequently, extracted features may not represent a degradation process suitably as a machine approaches a failure or fault. Also, effects of sensor types on the feature extraction and prediction model have not been much explored yet. To overcome this limitation, dynamic feature extraction is proposed to mitigate the effect of noisy statistical features in a monotonic trend by introducing a statistical penalty. Then, the features extracted through the method are used to construct a health indicator (HI). With the available historical HI values, a probabilistic regression model may be used to forecast the time to failure (TTF) of rotating machinery with uncertainty propagation. To validate the proposed method, acceleration data were collected from rotating machinery for several run-to-failure cases. The proposed method is demonstrated to provide excellent forecasts of TTF for both accelerometer types.