A method of bearing fault diagnosis using singular spectrum analysis, sparse filtering and anfis

Abstract

Bearing is an important machine detail participating in almost all mechanical systems. Estimating online its operating condition to exploit actively the systems, therefore, is one of the most urgent requirements. This paper presents an online bearing damage identifying method named ASBDIM based on ANFIS (Adaptive Neuro-Fuzzy Inference System), Singular Spectrum Analysis (SSA) and sparse filtering. This is an online estimating process operated via two phases, offline and online one. In the offline period, by using SSA and sparse filtering, a database signed Off_DaB is built whose inputs are features extracted from the measured data stream typed big data, while its outputs are values encoding the surveyed bearing damage statuses. The ANFIS is then employed to identify the dynamic response of the mechanical system corresponding to the bearing damage statuses reflected by the Of_DaB. In the online period, first, at each estimating time, another database called On_DaB is established using the way similar to the one used for building the Of_DaB. The On_DaB participates as inputs of the ANFIS to generate its outputs which are then compared with the corresponding encoded outputs to specify bearing real status at this time. Survey results based on different data sources showed the effectiveness of the proposed method.