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.

Due to the heavy background noise and strong interference, a blurry or even false time-frequency (TF) representation (TFR) by synchrosqueezing wavelet transform (SST) always results in inaccurate or meaningless gear fault extractions. To solve this problem, a novel TF analysis (TFA) method termed dual-core denoised SST is proposed in this article. The concepts critical to the proposed method are: 1) The dual-core denoising is proposed. First, the measured signal is transformed to the binary tree structure of the prewhitening and pseudo-characterizing signals by cepstrum editing. Second, two empirical mode decomposition (EMD)-based denoising techniques are, respectively, proposed for each binary-tree signal, focused on extracting different gear features such as the modulation and transients. Then, the purified feature signal is obtained by mixing the dual-core denoising results. 2) The multistep denoising strategy is studied for further improving the denoising accuracy. The steps are determined by the Rényi entropy. 3) By the multistep dual-core denoising, the denoised TFR with such meaningful TF signatures of gear faults as the accurate demodulation components and distinct transients could be obtained for gear fault detection. The repeatable simulations and engineering applications are used to verify the performance of denoising and TF resolution enhancement for gear fault detection.

This paper investigates the rheological properties of magnetorheological shear thickening fluid (MRSTF) and proposes a phenomenological theory-based constitutive model. Multiple samples with varying mass fractions and particle sizes were prepared. Then, the rheological properties were experimentally studied, including the effects of silicon particle size and concentration, carbonyl iron powder concentration and magnetic field on rheological properties. Next, the sedimentation stability experiment was also carried out by the static observation method. Finally, based on phenomenological theory, a constitutive model called the M-S model was derived through geometric transformation of shear stress curves. The results show that there is an inhibitory relationship between magnetorheological and shear thickening effect, and the correlation between composition and rheological properties was established, which can provide guidance for preparation of MRSTF with required properties. It also shows that the MRSTF under specific preparation scheme could effectively avoid sedimentation, and its performance is clearly superior to traditional materials in terms of sedimentation resistance and damping adjustment. The accuracy and universality of the proposed model are fully verified by fitting shear stress curves and calculating the goodness of fit values. All these investigations can offer an effective guidance for further study of MRSTF in controllable damping equipment development.

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.