The Recovery of Weak Impulsive Signals Based on Stochastic Resonance and Moving Least Squares Fitting

In this paper a stochastic resonance (SR)-based method for recovering weak impulsive signals is developed for quantitative diagnosis of faults in rotating machinery. It was shown in theory that weak impulsive signals follow the mechanism of SR, but the SR produces a nonlinear distortion of the shape of the impulsive signal. To eliminate the distortion a moving least squares fitting method is introduced to reconstruct the signal from the output of the SR process. This proposed method is verified by comparing its detection results with that of a morphological filter based on both simulated and experimental signals. The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with a good degree of accuracy, which leads to an accurate diagnosis of faults in roller bearings in a run-to failure test

Application of Phase Space Warping on Damage Tracking for Bearing Fault

Nowadays, the significance of keeping equipment function properly each time is obvious. If equipment fails during its use, it may have disastrous consequences. Estimating remaining useful life (RUL) of equipment is a key to prevent such calamities, improve its reliability, provide security and reduce unnecessary maintenance and operational cost. The evolution and tracking of damage is the foundation of RUL predicting, and also is one of the most important content of mechanical fault diagnosis. Slow-time variable process of mechanical damage would lead the phase space reconstructed by fast-time variable vibrate signals warping. Search the dynamics characteristic law of damage evolution analysis in the phase space, and build the relationship between fast-time variable signals and slow-time variable damage, and then damage evolution tracking is possible. To validate the theory, simulation model of bearing damage evolution is built, the outer-race fault evolution signals is obtained, and the trend of evolution of degradation of bearing fault is described with Phase Space Warping (PSW) theory and Smooth Orthogonal Decomposition (SOD). The results proved the feasibility of the methodology of PSW in damage evolution tracking

A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm

In this paper, a novel model for fault detection of rolling bearing is proposed. It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). The fundamental of multikernel least square support vector machine (MK-LS-SVM) is overviewed to identify a classifier that allows multidimension features from empirical mode decomposition (EMD) to be fused with high generalization property. Then the multidimension parameters of the MK-LS-SVM are configured by the SRPSO for further performance improvement. Finally, the proposed model is evaluated through experiments and comparative studies. The results prove its effectiveness in detecting and classifying bearing faults

Deep Electronic State Regulation through Unidirectional Cascade Electron Transfer Induced by Dual Junction Boosting Electrocatalysis Performance

Abstract Unidirectional cascade electron transfer induced by multi‐junctions is essential for deep electronic state regulation of the catalytic active sites, while this advanced concept has rarely been investigated in the field of electrocatalysis. In the present work, a dual junction heterostructure (FePc/L‐R/CN) is designed by anchoring iron phthalocyanine (FePc)/MXene (L‐Ti3C2‐R, R═OH or F) heterojunction on g‐C3N4 nanosheet substrates for electrocatalysis. The unidirectional cascade electron transfer (g‐C3N4 → L‐Ti3C2‐R → FePc) induced by the dual junction of FePc/L‐Ti3C2‐R and L‐Ti3C2‐R/g‐C3N4 makes the Fe center electron‐rich and therefore facilitates the adsorption of O2 in the oxygen reduction reaction (ORR). Moreover, the electron transfer between FePc and MXene is facilitated by the axial Fe─O coordination interaction of Fe with the OH in alkalized MXene nanosheets (L‐Ti3C2‐OH). As a result, FePc/L‐OH/CN exhibits an impressive ORR activity with a half‐wave potential (E1/2) of 0.92 V, which is superior over the catalysts with a single junction and the state‐of‐the‐art Pt/C (E1/2 = 0.85 V). This work provides a broad idea for deep regulation of electronic state by the unidirectional cascade multi‐step charge transfer and can be extended to other proton‐coupled electron transfer processes

Fabrication and Kinetic Study of a Ferrihydrite-Modified BiVO4 Photoanode

In spite of great progress in the surface modification of semiconductor photoelectrodes, the role of the metal oxide cocatalyst on photoelectrochemical (PEC) performance is still not well understood. In this study, ferrihydrite (Fh) as a novel cocatalyst was decorated on a wormlike nanoporous BiVO4 photoanode. A surface kinetics study of Fh/BiVO4 by intensity-modulated photocurrent spectroscopy (IMPS) evidences the primary role of Fh on PEC performance enhancement, varying with the loading of Fh. It was found that dispersed Fh le nanoparticles accelerate hole transfer for water oxidation, but the resulting photoanode suffers from poor stability. The thick layers of Fh address the stability of the electrode by suppressing surface charge recombination but result in reduced hole transfer rates. Modification of a BiVO4 film with optimally thick layers of discrete nanoflakes effectively reduces charge recombination without compromising stability, leading to a high AM 1.5 G photocurrent of 4.78 mA/cm(2) at 1.23 V versus the reversible hydrogen electrode and an applied bias photon to current efficiency of 1.81% at 0.61 V. These values are comparable to the best results reported for undoped BiVO4