A new nonparametric degradation modeling method for truncated degradation signals by axis rotation

•A new nonparametric degradation modeling method is proposed for truncated degradation signals by axis rotation.•The functional principal component analysis (FPCA)-based degradation model is reformulated on the definition of first pass time.•The Gaussian distribution assumption of the FPCA-based model is broken through.•A particle filtering-based algorithm is developed to update the model and predict the remaining useful life. Nonparametric degradation modeling can automatically formulate the principal degradation pattern of condition monitoring signals. It performs higher flexibility than parametric modeling in describing complicated degradation processes of machinery. Functional principal component analysis (FPCA) is a commonly used nonparametric modeling technique. A basic requirement of FPCA is that different units must share the same scale in X-axis. A FPCA-based degradation modeling method has been specially designed for truncated degradation signals whose amplitudes are truncated at the failure threshold. This method formulates the degradation time as a function of state level by axis rotation. Since the degradation signals of different units are truncated at the same failure threshold level, the axis rotation strategy ensures the same scale in X-axis. However, in cases of noisy signals, one state level may correspond to multiple time points, which means that the time function depending on state level does not exist. In addition, this method assumes that the noise term as well as the functional principal component (FPC) scores follow the Gaussian distribution, which restricts the flexibility in real practice. Aiming at the above two issues, this paper develops a new degradation modeling method based on the FPCA by axis rotation. The proposed method formulates the degradation model on the definition of first pass time (FPT), which ensures the existence of the time function depending on state level. The noise term and the FPC scores are not restricted to the Gaussian distribution. A particle filtering based updating algorithm is developed to deal with the non-Gaussian state estimation issue of the FPC scores. The effectiveness of the proposed method is verified using an experiment of milling cutter wear.