The incipient faults of rolling bearings are dynamically propagating in the service status. However, bearing material and size fault will affect its expansion trend direction. Furthermore, manufactured from different materials behave differently when they fail. Therefore, influence degree on dynamic characteristics crack propagation is investigated this paper. First, a model bearing, both under fault-free conditions with varying sizes outer ring, established by considering actual working...

As deuterium-tritium (D-T) fusion experiments progress, radiation shielding is a fundamental requirement for ensuring personnel safety of devices. This study utilizes neutron-photon coupling code to analyze the penetration high-energy neutrons through various materials in spatially constrained experimental The effectiveness neutron was evaluated transmission factor measurements. Following principle “moderation before absorption,” different material combinations were optimized enhance...

Purpose Several studies have shown the benefit of an accurate system modeling using Monte Carlo techniques. For state‐of‐the‐art whole‐body positron emission tomography (PET) scanners, Carlo‐based image reconstruction is associated with a significant computational cost to calculate matrix as well large memory capacity store it. In this article, authors present simulation‐reconstruction framework solve these problems on Philips Gemini GS PET scanner. Methods A fast, realistic simulation...

Abstract To accurately extract the bearing fault-induced impulse features from vibration signals corrupted by heavy noise and large-amplitude random impulses, an adaptive multi-band denoising model based on Morlet wavelet filter sparse representation is put forward. First, to locate desired frequency band associated with fault components, employed band-pass signal perspective of frequency-domain. Herein, improved Protrugram-based index, termed as windowed envelope spectral kurtosis, designed...

Thermal barrier coatings (TBCs) is a typical brittle, heterogeneous, multilayer structure material. The heat-loading effect during service will lead to premature peeling failure of the coating. main modes are cracking and interface spalling ceramic TBCs mainly caused by initiation, propagation connection microcracks. In this study, acoustic emission technique (AE) combined with microscopic morphology used study process under tensile load, fast Fourier transform (FFT) wavelet identify crack...

Fault diagnosis of rolling bearings is not a trivial task because fault-induced periodic transient impulses are always submerged in environmental noise as well large accidental and attenuated by transmission path. In most hybrid diagnostic methods available for bearings, the problems lie twofolds. First, optimization indices used individual signal processing stage do take periodical characteristic fault into consideration. Second, stages make use different resulting inconsistent directions...

In the existing rolling bearing performance degradation assessment methods, input signal is usually mixed with a large amount of noise and easily disturbed by transfer path. The time information ignored when model processes signal, which affects effect assessment. To solve above problems, an end-to-end railway axle box based on deep residual shrinkage network long short-term memory (DRSN-LSTM) proposed. proposed uses DRSN to extract local abstract features from denoises obtain denoised...

The reconvergence-phenomenon is common in modern circuit design. It occurs when the signal reconvenes at a certain point through multiple sensitized paths. affects soft error estimation and hardening. complicated to analyze accurately. In this paper, we propose method based on satisfiability problem (SAT) single event transient (SET) combinational circuits. Our translates SET reconvergence issue into an SAT. An SAT solver used determine whether could occur conditions for its occurrence....

Abstract Rolling bearings are critical components in modern mechanical equipment, and the health monitoring predictive maintenance of crucial for normal operation machinery. Hence, there is a compelling need to delve into advanced methodologies enhancing detection fault characteristics bearings. Faulty produce periodic impulses during constant-speed rotation, which can typically be detected through envelope analysis. However, some complex conditions, relevant frequencies may hidden within...

Data imbalances present a serious problem for intelligent fault diagnosis. They can lead to reduced diagnostic precision, which jeopardize equipment reliability and safety. Based on that, this paper proposes novel diagnosis method combining the denoising diffusion implicit model (DDIM) with new convolutional neural network framework. First, Gramian angular difference field (GADF) is used generate 2D images, are then augmented using DDIM. Next, by utilizing weight-sharing properties of...

Railway axle box bearing fault signal contains high Q-factor resonance and low transient shock components with periodic features that can characterize faults. However, extracting is usually difficult due to noise, transmission paths, high-amplitude accidental shocks. Therefore, address the abovementioned problems, multilevel feature extraction method for adaptive diagnosis of railway was proposed. This paper used maximum second-order cyclostationary blind convolution (CYCBD) weaken influence...

Given the complexity of application scenarios rolling bearing and severe scarcity fault samples, a solution to issue diagnosis under varying working conditions along with absence samples is required. A numerical model-driven cross-domain method targeting variable proposed based on cross-Domain Nuisance Attribute Projection (cDNAP). Firstly, simulation datasets consisting multiple types are constructed solve problem incomplete samples. Secondly, expanded by means generating adversarial...

Anomaly traffic detecting using Netflow data is one of important problems in the field network security. In this paper, we proposed an approach MapReduce model, which was realized by means entropy observation and DFN (Distinct feature number) distribution deviations features under anomalies at small time scales. The used to deal with huge amounts aid computer cluster processing. Experimental results show effectiveness approach.