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Recently, based on negentropy of squared envelope (SE) and of squared envelope spectrum (SES), extensions of spectral kurtosis, the infograms including the SE infogram and the SES infogram, were proposed to detect impulsive and cyclostationary transients. Moreover, they have abilities to detect transients in the cases, where impulsive noises exist or relaxation times of repetitive transients are lower than their repetition rate. Nevertheless, the infograms are fast filtering algorithms and cannot achieve optimal filtering for bearing fault feature identification. This paper aims to extend the infograms to novel Bayesian inference based optimal wavelet filtering for bearing fault feature identification. The innovations of this paper are summarized as follows. Firstly, a state space model of wavelet parameters is presented. Here, wavelet parameters are the states of the state space model. Monotonically increasing guess negentropy measurements are constructed. Secondly, either the SE infogram or the SES infogram is employed to initialize the state space model. Then, considering Gaussian disturbance on wavelet parameters, wavelet parameters are assumed to follow a joint Gaussian distribution. Thirdly, spherical cubature integration based Bayesian inference is introduced to iteratively establish posterior wavelet parameters distributions. At last, optimal wavelet parameters are determined from the posterior wavelet parameters distributions so as to conduct optimal wavelet filtering. Two instance studies including simulated and experimental bearing fault data were investigated to illustrate how the proposed Bayesian inference method works. The results show that the proposed Bayesian inference method is convergent and provides more fault signatures than the infogram. [ABSTRACT FROM AUTHOR]

This paper investigates a new probabilistic strategy for Bayesian model updating using incomplete modal data. Direct mode matching between the measured and the predicted modal quantities is not required in the updating process, which is realized through model reduction. A Markov chain Monte Carlo technique with adaptive random-walk steps is proposed to draw the samples for model parameter uncertainty quantification. The iterated improved reduced system technique is employed to update the prediction error as well as to calculate the likelihood function in the sampling process. Since modal quantities are used in the model updating, modal identification is first carried out to extract the natural frequencies and mode shapes through the acceleration measurements of the structural system. The proposed algorithm is finally validated by both numerical and experimental examples: a 10-storey building with synthetic data and a 8-storey building with shaking table test data. Results illustrate that the proposed algorithm is effective and robust for parameter uncertainty quantification in probabilistic model updating of buildings. [ABSTRACT FROM AUTHOR]