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

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 allows identifying the parameters of classic T-S FIS, starting with small quantity data. In work, we used vibration signals data from number over an entire period run-to-failure. FIS model inputs are features extracted observed periodically training bearings. rules and input each rule identified using subtractive clustering method. Furthermore, propose to use maximum likelihood mixture distribution analysis calculate clusters time axis probability corresponding degradation stages. Based result, output weighted least square estimation. We then benchmarked proposed some existing methods literature, through numerical experiments conducted available datasets highlight its effectiveness.