Research Article
Discovering New Prognostic Features for the Harmonic Reducer in Remaining Useful Life Prediction
Table 1
Summary of feature extraction.
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||
Illustrative example 1: Atamuradov et al. [29] proposed a new health indicator construction method for point machine prognostics composed of a hybrid feature selection, which extracted 8 time-domain features from the point machine sliding-chair CM data. Illustrative example 2: in study [36], the FFT method is applied to extract 12 time-frequency domain feature data, including root mean square, peak, and mean, from the vibration signal to fully mine the data information. Then, the PCA is used to calculate the eigenvalues of the multidimensional data index covariance matrix and feature vector as the new comprehensive index data, which be used as input data in the prediction model. Illustrative example 3: Deutsch and He achieved accurate RUL prediction based on deep belief network (DBN), which made use of the self-taught feature learning ability of the DBN to extract deep representation features [38]. |