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The research work presented in this paper proposes a data-driven modelingmethod for bearings remaining useful life estimation based on Takagi-Sugeno(T-S) fuzzy inference system (FIS). This method allows identifying theparameters of a classic T-S FIS, starting with a small quantity of data. Inthis work, we used the vibration signals data from a small number of bearingsover an entire period of run-to-failure. The FIS model inputs are featuresextracted from the vibration signals data observed periodically on the trainingbearings. The number of rules and the input parameters of each rule of the FISmodel are identified using the subtractive clustering method. Furthermore, wepropose to use the maximum likelihood method of mixture distribution analysisto calculate the parameters of clusters on the time axis and the probabilitycorresponding to rules on degradation stages. Based on this result, weidentified the output parameters of each rule using a weighted least squareestimation. We then benchmarked the proposed method with some existing methodsfrom the literature, through numerical experiments conducted on availabledatasets to highlight its effectiveness.

Fuzzy model identification based on mixture distribution analysis for bearings remaining useful life estimation using small training data set