The prediction of the remaining useful life (RUL) bearings is great significance for reducing cost and increasing efficiency mechanical equipment ensuring healthy operation. Most traditional RUL methods based on deep learning only analyze single sensor data, which makes accuracy fluctuate greatly reliability low, contributions data collected by different sensors to are inconsistent, low utilization lead poor results. To solve above problems, a new framework with convolutional attention...

In practical engineering scenarios, machines are seldom in a faulty operating state, so it is difficult to get enough available sample data train the fault diagnosis model, leading problem of small and unbalanced number rotating machinery samples low accuracy. To solve this problem, paper introduces novel approach diagnosis. This involves integration Convolutional Attention Residual Network (CBAM-ResNet) with Graph Neural (GCN). Firstly, comprehensively exploit time-domain information from...

In recent years, the unsupervised domain adaptation (UDA) technique has achieved remarkable success in cross-domain fault diagnosis of rotating machinery. UDA, three pivotal pieces information—namely, class labels, and data structures, play a critical role establishing connection between labeled samples source unlabeled target domain. Most research methods use only one or two these types information, ignoring importance structure. addition, global adaptive techniques are typically used,...

Motor imagery-based brain-computer interface (MI-BCI) inefficiency phenomenon is one of the biggest challenges in MI-BCI research. BCI subject defined as who cannot achieve classification accuracy higher than 70% since considered to be minimum for communication by BCI. About 15-30% people are according investigation. Most existing studies used common spatial patterns (CSP) extract motor imagery feature and identify based on obtained accuracy. We think performance maybe suppressed because CSP...

In this research, an innovative state observer of gasoline engine based on the combination Luenberger and sliding mode technique is proposed. This designed to track crankshaft angular speed estimate combustion torque experimental a four-cylinder Spark Ignition (SI) engine. Then, new advance in application Artificial Neural Networks (ANNs) estimated results automated dynamic misfire fault diagnosis both under steady non-stationary condition discussed detailed. order effectively obtain data...

In an actual engineering environment, some rotating machines are usually in normal operation, but their time a fault state is very short, which leads to serious imbalance the diagnosis datasets for machinery, and gives traditional network model shortcomings of poor stability low accuracy practical applications. To solve this problem, we propose method based on combination new Dual-stage Attention-based Recurrent Neural Network (DA-RNN) depth residual dispersion self-calibration convolution...

The remaining useful life (RUL) prediction is important for improving the safety, supportability, maintainability, and reliability of modern industrial equipment. traditional data-driven rolling bearing RUL methods require a substantial amount prior knowledge to extract degraded features. A large number recurrent neural networks (RNNs) have been applied RUL, but their shortcomings long-term dependence inability remember historical information can result in low accuracy. To address this...

Readiness potential (RP) based on electroencephalograms (EEG) has been studied extensively in recent years, but no studies have investigated the influence of reference electrode RP. In order to investigate effect, 10 subjects were recruited and original vertex (Cz) was used record raw EEG signal when performed a motor preparation task. The then transformed common average (CAR) standardization technique (REST) reference, we analyzed RP waveform voltage topographies calculated classification...

Numerous model-based techniques have been proposed to estimate the state of charge (SOC) lithium-ion batteries. In automotive applications, algorithms are subjected changing load profiles, requiring investigations into their general performance under various working conditions. this study, three different patterns derived from a customized dynamic driving profile, standard cycle, and constant discharge used for experimental verification. Four selected including Ampere-hour counting, extended...

In the electro-hydraulic servo control system, there are some problems, such as low position accuracy, uncontrollable speed, speed impact, and asymmetric due to asymmetry of hydraulic cylinder. To solve above this paper proposes a Fuzzy PID algorithm with load force compensation based on improved PSO optimization. This particle swarm optimization introduces crossover mutation operations in genetic improve performance traditional algorithm. The mathematical model valve controlled cylinder is...

This paper presents a Luenberger-sliding mode observer to estimate selective catalyst reduction (SCR) system mid-catalyst ammonia concentration. In order measure the concentration at middle of catalyst, two-cell SCR model was utilized. The robustness with respect sensor measurement uncertainties were theoretical analyzed. And stability shown based on sliding approach. performance validated simulation platform. Simulation studies showed that proposed had good and tracked target very well....

Place cells are considered to be the basic unit of cognitive maps, which play an important role in spatial cognition for many animals. To investigate neural mechanisms underlying birds, we designed four tasks with or without goals, using pigeons as animal model. We examined place hippocampus 12 while they performed each task. measured their response properties and comparatively analyzed field between goals qualitative quantitative measures. The results revealed that reliability fields...

This study presented a method for modeling the nonlinear system of planetary gearbox and fault diagnosis crack in gear based on Volterra series theory. First, exponential Hilbert reproducing kernel its fast optimization algorithm was proposed deduced theory, solution fourth-order successfully solved. Second, model estimation compared with least squares actual collected signals from time-domain output signal estimated using neural network. The accuracy superiority were then verified. At same...

In order to collect the fault signal of slewing bearing, design and built up bearing test rig system. Because is weak, containing characteristics was resolved reconstructed with wavelet theory. With application Hilbert transform in demodulation detailed spectrum analysis, characteristic frequency extracted, judged. All above work shows that Wavelet analysis combined effective diagnosis rotary local fault.

Existing end-to-end cloud registration methods are often inefficient and susceptible to noise. We propose an point network model, Point Transformer for Registration Network (PTRNet), that considers local global features improve this behavior. Our model uses clouds as inputs applies a method extract their features. Using K-Nearest Neighbor (K-NN) topology, our then encodes the of integrates them with obtain cloud’s strong Comparative experiments using ModelNet40 data set show offers better...

<title>Abstract</title> In ABAQUS simulations, the Young's modulus and Poisson's ratio are set to vary with temperature by assigning fixed values. This approach can impact reliability accuracy of simulation results. this paper, temperature-dependent functions incorporated into using its secondary development capabilities. And simulate ultrasonic vibration-assisted cutting (UVAC) process titanium alloy TC4. Based on analysis results, fitting function operates within range simulation, which is...

Background: The segmentation of electroencephalography (EEG) signals into a limited number microstates is significant importance in the field cognitive neuroscience. Currently, microstate analysis algorithm based on global power has demonstrated its efficacy clustering resting-state EEG. task-related EEG was extensively analyzed brain–computer interfaces (BCIs); however, primary objective classification rather than segmentation. Methods: We propose an innovative for analyzing spatial...