No abstract

In this paper, a damage detection method based on a combination of wavelet analysis and an interval type-2 fuzzy logic system (IT-2FLS) is proposed. Firstly, synthesizing IT-2FLSs as a data-driven model is proposed. The structure is then divided into elements and excited to be vibrated to measure vibration signal. Average quantity signal of wavelet transform coefficient (AQWTC) of vibration signal with a used-scale-sheet is established. The IT-2FLS is utilized to identify the structure at its undamaged time via AQWTC signal. At each surveying time, AQWTC at each element is calculated to estimate difference of corresponding AQWTCs between two cases: undamaged status and the status at the checked time. By applying the AQWTC's contrast at two these times, a damage coefficient is described which is used to estimate status of the structure. Besides, in order to predict structure's status, the time-series prediction using the IT-2FLS and the calculated damage coefficient are also presented. The effectiveness of the proposed method is demonstrated by experiment via data sources measured from dynamic response of a real structure.

Singular spectrum analysis (SSA) has been employed effectively for analyzing in the time-frequency domain of time series. It can collaborate with data-driven models (DDMs) such as Artificial Neural Networks (ANN) to set up a powerful tool for mechanical fault diagnosis (MFD). However, to take advantage of SSA more effectively for MFD, quantifying the optimal component threshold in SSA should be addressed. Also, to exploit the managed mechanical system adaptively, the variation tendency of its physical parameters needs to be caught online. Here, we present a bearing fault diagnosis method (BFDM) based on ANN and SSA that targets these aspects. First, a multi-feature is built from pure mechanical properties distilled from the vibration signal of the system. Relied on SSA, the measured acceleration signal is analyzed to cancel the high-frequency noise. The remaining components take part in building a multi-feature to establish a database for training the ANN. Optimizing the number of the kept components is then carried out to obtain a dataset called Tr_Da. Based on Tr_Da, we receive the optimal ANN (OANN). In the next period, at each checking time, another database called Test_Da is set up online following the same way of building the Tr_Da. The compared result between the encoded output and the output of the OANN corresponding to the input to be Test_Da provides the bearing(s) health information. An experimental apparatus is built to evaluate the BFDM. The obtained results reflect the positive effects of the method.