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.
The low permeability sandstone reservoir in the Ordos Basin displays heterogeneity with sedimentation and tectonic origins, which is mainly manifest by interbedding of sandstone and mudstone, bedding, and fractures (). There is a clear difference between this type of heterogeneity and pore heterogeneity and diagenetic heterogeneity. At present, academia pays less attention to this kind of heterogeneity and lacks a quantitative evaluation method. The imaging log can describe this kind of heterogeneity directly. The Tamura texture features (TTF) method was used to calculate the roughness of different heterogeneous intervals. It is found that the fracture has the largest roughness, followed by the oblique bedding and the horizontal bedding section, and the massive bedding has the smallest roughness. The GR curve roughness calculated by EMD is consistent with that calculated by TTF. Therefore, TTF can be used to quantitatively evaluate the heterogeneity of low permeability sandstone reservoirs based on the imaging log when the imaging log has the same size. The roughness of the imaging log calculated by the TTF method has a strong coupling with the sedimentary cycle. This method is accurate, objective, and easy to understand. This is another important application of TTF in addition to quantitative evaluation of the heterogeneity of low permeability sandstone reservoirs.
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