Feature Extraction of Lubricating Oil Debris Signal Based on Segmentation Entropy with an Adaptive Threshold.

Ferromagnetic debris in lubricating oil, serving as an important communication carrier, can effectively reflect the wear condition of mechanical equipment and predict the remaining useful life. In practice application, the detection signals collected by using inductive sensors contain not only debris signals but also noise terms, and weak debris features are prone to be distorted, which makes it a severe challenge to debris signature identification and quantitative estimation. In this paper, a debris signature extraction method established on segmentation entropy with an adaptive threshold was proposed, based on which five identification indicators were investigated to improve detection accuracy. The results of the simulations and oil experiment show that the proposed algorithm can effectively identify wear particles and preserve debris signatures.

Core-Shell Structured Silica-Coated Iron Nanowires Composites for Enhanced Electromagnetic Wave Absorption Properties.

In this study, we successfully prepared core-shell heterostructured nanocomposites (Fe NWs@SiO2), with ferromagnetic nanowires (Fe NWs) as the core and silica (SiO2) as the shell. The composites exhibited enhanced electromagnetic wave absorption and oxidation resistance and were synthesized using a simple liquid-phase hydrolysis reaction. We tested and analyzed the microwave absorption properties of Fe NWs@SiO2 composites with varied filling rates (mass fractions of 10 wt%, 30 wt%, and 50 wt% after mixing with paraffin). The results showed that the sample filled with 50 wt% had the best comprehensive performance. At the matching thickness of 7.25 mm, the minimum reflection loss (RLmin) could reach -54.88 dB at 13.52 GHz and the effective absorption bandwidth (EAB, RL < -10 dB) could reach 2.88 GHz in the range of 8.96-17.12 GHz. Enhanced microwave absorption performance of the core-shell structured Fe NWs@SiO2 composites could be attributed to the magnetic loss of the composite, the core-shell heterogeneous interface polarization effect, and the small-scale effect induced by the one-dimensional structure. Theoretically, this research provided Fe NWs@SiO2 composites with highly absorbent and antioxidant core-shell structures for future practical applications.

Segmentation of Online Ferrograph Images with Strong Interference Based on Uniform Discrete Curvelet Transformation.

Through real-time acquisition of the visual characteristics of wear debris in lube oil, an on-line visual ferrograph (OLVF) achieves online monitoring of equipment wear in practice. However, since a large number of bubbles can exist in lube oil and appear as a dynamically changing interference shadow in OLVF ferrograms, traditional algorithms may easily misidentify the interference shadow as wear debris, resulting in a large error in the extracted wear debris characteristic. Based on this possibility, a jam-proof uniform discrete curvelet transformation (UDCT)-based method for the binarization of wear debris images was proposed. Through multiscale analysis of the OLVF ferrograms using UDCT and nonlinear transformation of UDCT coefficients, low-frequency suppression and high-frequency denoising of wear debris images were conducted. Then, the Otsu algorithm was used to achieve binarization of wear debris images under strong interference influence.

A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography.

Wear debris in lube oil was observed using a direct reflection online visual ferrograph (OLVF) to monitor the machine running condition and judge wear failure online. The existing research has mainly concentrated on extraction of wear debris concentration and size according to ferrograms under transmitted light. Reports on the segmentation algorithm of the wear debris ferrograms under reflected light are lacking. In this paper, a wear debris segmentation algorithm based on edge detection and contour classification is proposed. The optimal segmentation threshold is obtained by an adaptive canny algorithm, and the contour classification filling method is applied to overcome the problems of excessive brightness or darkness of some wear debris that is often neglected by traditional segmentation algorithms such as the Otsu and Kittler algorithms.

Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD.

Incipient Fault Detection of Rolling Bearing with heavy background noise and interference harmonics is a hot topic. In this paper, a new method based on parameter optimized fast EEMD (FEEMD) and Maximum Autocorrelation Impulse Harmonic to Noise Deconvolution (MAIHND) method is proposed for detecting the incipient fault of rolling bearing. Firstly, the FEEMD method with parameters optimization is used to reduce the noise and eliminate the interference harmonics of the fault signal. As a noise assistant improved method, the FEEMD can reduce the mode mixing and enhance the calculation efficiency significantly. Secondly, a new indicator is developed to select the sensitive IMF. Finally, a novel MAIHND method is employed to extract impulse fault feature from the sensitive IMF. Simulation and experiments results indicated that the proposed parameter optimized FEEMD-MAIHND method can effectively identify the weak impulse fault feature of rolling bearing. Moreover, the excellent performance of the proposed indicator for sensitive IMF component selection and MAIHND method is verified.