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This paper presents an experimental study of the dynamic performance of a self-developed shear thickening fluid (STF) damper and its mechanical model was proposed by nonlinear fitting. First, STF samples with different mass fraction and dispersion medium were fabricated by nano fumed silica and polyethylene glycol, and its rheological properties were investigated by a rheometer. Second, a smart STF damper was developed and manufactured. Its dynamic properties were experimentally investigated by establishing a vibration test bench, and results indicated that the STF damper can output variable damping force by controlling the loading frequency, loading amplitude and fluid gap. Third, the Bouc–Wen model was proposed to address the dynamic properties of STF damper, and mechanical model analysis was carried out by comparing several fitting functions. It verified that the Bouc–Wen hysteresis model can be better used to describe the nonlinear stiffness, nonlinear damping and rate-dependence characteristics of the STF damper. All these investigations can offer an effective guidance for further theoretical and application study of the smart STF damper in energy dissipation fields.

The meaningful data-based fault diagnosis is beforehand revealing the potential faults to reduce the costly breakdowns, one challenging of which is extracting the weak features from the complicated signals.Ensemble noise-reconstructed EMD (ENEMD) is an intelligent method by the nice integration of adaptively decomposing and naturally denoising. However, ENEMD still suffers from such issues as the false possible noise-only IMFs and the universal minimax threshold, reducing the precision of the critical noise estimation for the weak feature extraction. Thus, the dual-mode noise-reconstructed EMD method is proposed for weak feature extraction and fault diagnosis of rotating machinery. First, the possible noise-only IMF selection rule is redesigned according to the noise characteristic and the correlation evaluation, to eliminate the redundant slowly oscillating IMFs mistakenly chosen for noise estimation. Second, the adaptive local minimax threshold is proposed in the noise estimation technique for the low SNR signal, to overcome the drawback of additionally keeping some critical but weak fault features into the estimation noise. Hereinto, the local threshold is respectively performed in each sliding window defined by the demodulated rotating-related feature frequency. Third, the proposed method is addressed with the flowchart. Finally, two engineering case studies are implemented to demonstrate the feasibility and effectiveness of the method. The analytic results show that the method could effectively extract the periodic impulses generating by the early local damage in the gearbox of a hot strip finishing mill. Meanwhile, the method could successfully reveal the weak rubbing-impact faults along with alleviating the mode mixing phenomenon in the refined results for fault diagnosis of a heavy oil catalytic cracking unit. Hence, the method could provide a promising tool for weak feature extraction and fault diagnosis of rotating machinery.

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