A new FPCA-based remaining useful life prediction method for truncated degradation signals

Functional principal component analysis (FPCA) is a commonly used nonparametric degradation 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. 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 according to the online condition monitoring signals. The effectiveness of the proposed method is verified using an experiment of milling cutter wear.

I. Introduction

With the popularization of sensor technology in industry, condition-based maintenance (CBM) has been a common strategy to protect the safe operation of machinery [1]. In CBM, sensor signals such as vibration and temperature are generally treated as manifestation of the unobservable health state. The degradation process of machinery is monitored by observing sensor signals or health indicators extracted from them. A big issue in CBM is how to model and predict the degradation behavior of machinery according to online condition monitoring signals [2]. Statistical degradation modeling is an effective way to deal with this issue, which uses mathematical models to formulate the degradation process of machinery with the statistical analysis of historical data [3], [4].