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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. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an integrated machining error compensation method based on polynomial neural network (PNN) approach and inspection database of on-machine-measurement (OMM) system. To improve the accuracy of the OMM system, geometric errors of the CNC machining center and probing errors are compensated. Machining error distributions of a specimen workpiece are measured to obtain error compensation parameters. To efficiently analyze the machining errors, two machining error parameters, Werr and Derr , are defined. Subsequently, these parameters can be modeled using the PNN approach, which is used to determine machining errors for the considered cutting conditions. Consequently, by using an iterative algorithm, tool path can be corrected to effectively reduce machining errors in the end-milling process. Required programs are developed using Ch language, and modified termination method are applied to reduce computation times. Experiments are carried out to validate the approaches proposed in this paper. The proposed integrated machining error compensation method can be effectively implemented in a real machining situation, producing much fewer errors. [Copyright &y& Elsevier]