A facile and effective strategy for fabricating a three-dimensionally (3D) structured nanocomposite catalyst based on nonprecious metals water splitting in alkaline electrolyzers is reported this paper. This consists of the CdS quantum dots (QDs) decorated Ni3S2 nanosheet flowers deposited plasma-treated nickel foam (PNF). The NiO formed during plasma treatment shown to play an important role pushing hydrogen oxygen evolution reactions (HER OER) media. enhanced exposure active sites...

Accurate prediction of wind power generation is great significance for the efficient operation farms. However, traditional deep learning-based methods predict without simultaneously considering temporal features and spatial between variables, which leads to low accuracy. This article proposes a novel forecasting approach based on graph convolution network (GCN) multiresolution neural (CNN), combining features. In this approach, GCN merged with maximum information coefficient (MIC) proposed...

The telemetry data obtained from an on-orbit spacecraft contain important information to indicate anomaly of the spacecraft. However, large number monitoring variables and amount points, as well lack prior knowledge about due complicated structure its working conditions, pose great challenge detection. This article proposes detection algorithm based on a spatial–temporal generative adversarial network (GAN) for in data. establishes GAN-based model combining convolutional neural (CNN) long...

Voltage fault diagnosis is critical for detecting and identifying the Lithium-ion battery failure. This paper proposes a voltage algorithm based on an Equivalent Circuit Model-Informed Neural Network (ECMINN) method batteries, which aims to learn observer by embedding Model (ECM) into neural network structures. directly embeds deterministic mechanism part in ECM designs uncertain networks, takes advantage of high precision physical model strong nonlinear processing ability improve effect. In...

Detecting anomalies for multivariate time series is of great importance in modern industrial applications. However, due to the complex temporal dynamics systems, finding a distinguishable judge criterion hard, which makes accurate anomaly detection still challenging task. In order better capture anomalous features and design more informative criterion, this article presents an unsupervised generative adversarial network (GAN) detection, highlights novel active distortion transformer (ADT)...

A novel RUL prediction approach for lithium-ion batteries using quantum particle swarm optimization (QPSO)-based filter (PF) is proposed. Compared to (PSO)-based PF, QPSO-based PF proved have a better performance in global searching and has fewer parameters control, which makes QPSO-PF easier applications. Moreover, particles are required by accurately track the battery's health status, leading reduction of computation complexity. results real data provided NASA compared with benchmark...

For critical engineering systems such as aircraft and aerospace vehicles, accurate Remaining Useful Life (RUL) prediction not only means cost saving, but more importantly, is of great significance in ensuring system reliability preventing disaster. RUL affected by a system's intrinsic deterioration, also the operational conditions under which operating. This paper proposes an approach to estimate mean continuously degrading dynamic subjected condition monitoring at short equi-distant...

Remaining useful life (RUL) prediction of rolling bearings brings benefits for maintenance spacecrafts. Vibration signals are widely used RUL prediction. However, under some situations such as high-speed rotation bearings, vibration quite easily disturbed by noise and might be tough to collect due inappropriate installation accelerometers. Therefore, in this paper, stator current considered health indicator bearing Based on signals, feature extraction trajectory tracking suffer two...

Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for system’s proper operation. A direct way estimate SOH through measurement of battery’s capacity; however, this during operation not that easy practice. Moreover, battery always running under randomized loading conditions, which makes estimation even more difficult. Therefore, paper proposes an indirect method relies on health...

State-of-health (SOH) plays a vital role in battery health management and power system stability. This process can be achieved by capacity estimation. However, practice, the of is difficult to obtain online given that it cannot determined with general sensors. means only known for limited cycles batteries. To address this issue, we propose novel semi-supervised learning framework estimate unlabeled data achieve better SOH prediction. First, four indirect features are extracted from charging...

Tool wear estimation and prediction are keys of maintenance decision-making for milling machine. Various discrete-state degradation models have been developed tool prediction. However, previous research assume that the number discrete states is fixed based on prior understanding process. To break this limitation, a data-driven approach Hierarchical Dirichlet process-Hidden Markov model (HDP-HMM) proposed. The states, transition probability matrix omission distribution hidden (HMM) can be...

Efficient anomaly detection in telemetry time series is of great importance to ensure the safety and reliability spacecraft. However, traditional methods are complicated train, have a limited ability maintain details, do not consider temporal-spatial patterns. These problems make it still challenge effectively identify anomalies for multivariate series. In this paper, we propose Denoising Diffusion Time Series Anomaly Detection (DDTAD), an unsupervised reconstruction-based method using...

This paper presented the fabrication and calibration of a clad-modified evanescent based plastic optical fiber (POF) sensor for detection ammonia in both stagnant dynamic aqueous media. optochemical was on Oxazine 170 perchlorate (sensing material) polydimethylsiloxane (PDMS) (protective thin layers. A special chemical solution developed etching removal cladding methodology trapping moisture exercised. Experimental results dissolved exhibited short response time (≤10 s), low limit (minimum...

Ensuring the safety and reliability of unmanned aerial vehicles (UAVs) has become a critical issue as they continue to advance. Analyzing anomalous events using event-based explanations is an effective approach identifying key behaviors mitigating potential risks. However, this task challenging because in flight data lack time-step labels real-world UAV scenarios. To address challenge, we propose dual attention-based multi-instance learning (DA-DI-MIL) for pinpointing anomaly instances...