  The research work presented in this paper proposes a data-driven modeling
method for bearings remaining useful life estimation based on Takagi-Sugeno
(T-S) fuzzy inference system (FIS). This method allows identifying the
parameters of a classic T-S FIS, starting with a small quantity of data. In
this work, we used the vibration signals data from a small number of bearings
over an entire period of run-to-failure. The FIS model inputs are features
extracted from the vibration signals data observed periodically on the training
bearings. The number of rules and the input parameters of each rule of the FIS
model are identified using the subtractive clustering method. Furthermore, we
propose to use the maximum likelihood method of mixture distribution analysis
to calculate the parameters of clusters on the time axis and the probability
corresponding to rules on degradation stages. Based on this result, we
identified the output parameters of each rule using a weighted least square
estimation. We then benchmarked the proposed method with some existing methods
from the literature, through numerical experiments conducted on available
datasets to highlight its effectiveness.
