Algorithm for Estimating Online Bearing Fault Upon the Ability to Extract Meaningful Information From Big Data of Intelligent Structures

Bearing is an important machine detail that appears in almost all mechanical systems. Estimating its operating condition online in order to hold the initiative in exploiting the systems, therefore, is one of the most urgent requirements. In this paper, we propose an online bearing damage identifying method named ASSBDIM based on adaptive neuro-fuzzy inference system (ANFIS), singular spectrum analysis (SSA), and sparse filtering (SF). It is an online process with offline and online phases. In the offline phase, by applying SSA and SF to the measured data stream typed big data with noise, both preprocessing data and extracting valuable information are implemented to build two offline databases signed Off_DaB and Off_testDaB. The ANFIS identifies the dynamic response of the mechanical system via Off_DaB. Based on Off_testDaB, the parameters of the ASSBDIM are then optimized. In the online phase, at each time, another database called On_DaB is built in the way similar to the one used for building the input space of the two offline databases. On_DaB participates as inputs of the ANFIS to estimate 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.