Abstract Soft magnetic composites (SMCs) play a pivotal role in the development of high-frequency, miniaturization and complex forming modern electronics. However, they usually suffer from trade-off between high magnetization good softness (high permeability low core loss). In this work, utilizing order modulation strategy, critical state FeSiBCCr amorphous soft composite (ASMC), consisting massive crystal-like orders (CLOs, ∼1 nm size) with feature α -Fe, is designed. This critical-state...

The decomposition effect of variational mode (VMD) mainly depends on the choice number K and penalty factor α. For selection two parameters, empirical method single objective optimization are usually used, but aforementioned methods often have limitations cannot achieve optimal effects. Therefore, a multi-objective multi-island genetic algorithm (MIGA) is proposed to optimize parameters VMD apply it feature extraction bearing fault. First, envelope entropy (Ee) can reflect sparsity signal,...

This paper introduces a hybrid model for multivariate multi-wind farm wind speed prediction to reduce operational costs at control centers and enhance accuracy. Initially, parallel called BiTCN–Transformer–Cross-attention (BTTCA) is developed, which integrates spatiotemporal features using cross-attention. The BTTCA pre-trained historical data from four typical farms, with input consisting of related meteorological information. Subsequently, the models are deployed via transfer learning...

Accurate and reliable wind speed forecasting is crucial for farm planning grid operation security. To improve the accuracy of forecasting, a novel combined model proposed in this article. First, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) permutation entropy (PE) are employed to decompose original time series into sub-series with obvious complexity different; overcome disadvantage weak generalization ability single deep learning method when facing diversiform...

Aiming at the problem that it is difficult to extract fault features from nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads low diagnosis recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) multi-features proposed. The effect VMD limited by number decompositions selection penalty factors. This paper uses MIGA optimize parameters. used decompose signal into...

Water pollution seriously endangers people’s lives and restricts the sustainable development of economy. quality prediction is essential for early warning prevention water pollution. However, nonlinear characteristics data make it challenging to accurately predicted by traditional methods. Recently, methods based on deep learning can better deal with characteristics, which improves performance. Still, they rarely consider relationship between multiple indicators quality. The crucial because...

In order to reduce the curse of dimensionality massive data from SCADA (Supervisory Control and Data Acquisition) system remove redundancy, grey correlation algorithm is used extract eigenvectors monitoring data. The are as input vectors variables related unit state output vectors. genetic cross validation method optimize parameters support vector regression (SVR) model. A high precision prediction carried out, a reasonable threshold set up alarm fault. condition wind turbine realized....

With the continuous elevation of demand for large-scale wind turbines and operation & maintenance cost an increasing interest has been rapidly generated on CM (Condition Monitoring) system. The main components are focus all as they overall lead to high repair costs equipment downtime. Thus, it is difficult make comprehensive assessment in assessment. In present study, intelligent machine learning algorithms adopted mine SCADA (Supervisory Control Data Acquisition) system data WTs (wind...

Although traditional fault diagnosis methods are proficient in extracting signal features, their diagnostic interpretability remains challenging. Consequently, this article proposes a conditionally interpretable generative adversarial network (C-InGAN) model for the feature of bearings. Initially, vibration is denoised and transformed into frequency domain signal. The consists two primary networks, each employing convolutional layer an attention module, generator (G) discriminator (D),...

With the construction of large-scale wind turbines, how to reduce operation and maintenance costs has become an urgent problem be solved. In this paper, by extracting actual data turbine in Supervisory Control Data Acquisition (SCADA) system, Bidirectional R ecurrent Neural Networks (BRNN) is used establish prediction model. By eliminating abnormal points caused accidental factors through box diagram, fault risk threshold components optimized. Then, based on residual between value measured...

Abstract Within the context of rapidly progressing industrial sectors, rolling bearings have become a fundamental component across an array mechanical systems. Their fault detection and remaining useful life (RUL) estimations are vital for ensuring production safety. Yet, understated characteristics early-stage, minor faults in bearing degradation often escape detection. Additionally, numerous existing networks overlook critical information embedded multi-scale features, consequently...

Real-time production information is important to enterprise for improving management. Therefore, the RFID technology can be used in obtain real-time information, which integrated into manufacturing execution system, and decision of management transferred floor. The process textile industry analyzed by this paper. Considering technical advantages RFID, model system based on perfection solution presented

From the supply chain, now RFID technology is gradually applied to core of manufacturing process. By adopting in workshop layer, exact real-time information that obtained from RFID, can be seamlessly integrated into execution system. Therefore it create additional value, and increase productivity for enterprise. The technical advantages applying system are analyzed detailedly by this paper. Taking textile industry as object, design based on presented And provide perfection solution...

With the increasing complexity of wind turbines, current situation high failure rates and maintenance costs has attracted attention power operators. The research on health status monitoring turbines is great significance to development industry. In this study, a novel method for evaluating proposed. fully considers characteristics turbine with high-dimensional nonlinearity. Firstly, Gaussian kernel density estimation Local Outlier Factor (GLOF) used clean data. Secondly, feature parameters...

It is important but difficult to analyze transient signal of Radio Frequency Fingerprinting (RFF), especially extract the features RFF signal. Method analyzing based on Empirical Mode Decompositions (EMD) introduced in this paper. EMD can dynamically achieve signal's Time-Frequency Distribution (TFD) and amplify feature's difference between RFF's signals from different radio transmitters. efficient process system. The results simulation show that method has advantages processing speed fast...

To improve the predicting accuracy of PM10 concentration prediction, this paper presents a combined prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE) and long short-term memory (LSTM). The time series is decomposed into restructured subsequences obvious complexity differences by CEEMDAN-SE firstly. Then, adding meteorological parameters to each different restrictured subsequence, LSTM built. By results, final results...

In order to compensate time-delay in the networked control system (NCS), paper presents a state prediction method which adopted time-stamp. At first, we studied time-stamp based measuring algorithm of NCS and implemented compensation system. Then, proposed approach establishing an approximate model object states controller actuator time-driven. With that, designed make achieve deduced necessary sufficient conditions for stabilizing last, simulation results numerical example prove validity approach.