IEEE Transactions on Industrial Informatics

An effective coordination strategy between charging stations (CSs) and the distribution system operator (DSO) can fully exploit the charging flexibility of electric vehicles to improve the flexibility of power systems. Multiagent deep reinforcement learning (MADRL) has proven its effectiveness in coordination problems. However, existing MADRL-based approaches face the problems of incentive inequi…

Detection of high-impedance faults (HIFs) is significant for protecting active distribution networks and human safety. However, existing HIF detection methods show low accuracy and reliability, and protection remains a great challenge in active distribution networks. In this article, a novel HIF detection method is proposed by considering nonlinear arc distortion features in active distribution n…

Memristive chaotic oscillator has significant application value in multiple industrial fields. In this study, based on three piecewise linear functions and a new nonlinear memristor, we first develop a new extremely multistable grid-scroll memristive chaotic oscillator, and applied it to acoustic signal detection. The model can produce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:…

Aviation electrical connectors are essential components in the aircraft electrical wiring interconnection system (EWIS), responsible for information and energy transmission. Even a minor fault in a connector pin can critically affect the reliability and stability of the EWIS. However, traditional faulty pin detection methods rely heavily on manual visual inspection, which is inefficient and susce…

In today's industrial Internet of Things systems, functional safety communication protocols are widely adopted to transmit safety protocol data unit (SPDU). While the guessing random additive noise decoding (GRAND) algorithm can improve the reliability of cyclic redundancy check (CRC)-coded SPDU, the decoding complexity of long SPDU remains too high for practical deployment. To address this, we e…

High-frequency dynamic targets introduce substantial appearance variations in LiDAR scans of the same location over time, posing a major challenge for place recognition. To tackle this, we propose DyLPR, a cascaded PR framework that integrates a LiDAR depth inpainting network and a place recognition network (PTN-Net), leveraging the complementary strengths of convolutional neural networks (CNNs) …

An effective strategy for online safety assessment is the guarantee and fundamental to ensuring the safe and stable operation of industrial systems. However, in increasingly dynamic and complex industrial systems, operational condition transitions and production state changes may cause the probability density function (PDF) to exhibit notable irregularities or even severe collapse. This renders t…

In industrial wireless networks with resource-constrained and densely deployed devices, link scheduling is a challenging task. Traditional optimization methods have high computational complexity and low scalability. Graph learning offers a promising approach, yet it also comes with limitations of capturing multivariate relationships from interference, leading to ineffective link scheduling. In th…

Wheeled robot fault diagnosis is indispensable for ensuring its reliable and safe operations. However, two challenges impede the application of prevalent intelligent diagnosis methods. 1) Multisensor fusion: The complexity of robot movements necessitates multisensor for comprehensive monitoring, generating strong-coupled, and high-dimensional data that complicate both intrinsic relationship minin…

Compressive strength is the critical quality metric in industrial cement production, yet conventional assessment relies on destructive 28-day tests or inaccurate accelerated methods, hindering timely quality control. These methods inherently cause <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sample scarcity</i> (only 52–195 batches/year/plant) …

Reconstruction-based anomaly detection is appealing for industrial inspection because it reconstructs anomaly-free references and produces interpretable pixel-level residual maps. However, in multiclass settings, it often suffers from, first, identity mapping, where abnormal regions are overreconstructed and residuals vanish, and second, cross-category feature entanglement, which introduces artif…

This work investigates the oriented-covert attacks and corresponding defense strategies on an unmanned aerial vehicle (UAV) equipped with a GPS sensor and an Ultra-WideBand sensor in single and double base-station scenarios. The attacker drives the UAV away from its nominal path and causes a high-velocity collision while remaining stealthy to base station detection. The attack is formulated as a …

Cross-domain fault diagnosis leverages knowledge from multiple source domains to improve diagnostic accuracy in target domain. However, existing methods align source and target domains either jointly or independently, often neglecting the distributional discrepancies among source domains, which hinders effective knowledge transfer. To address this issue, we proposes a hierarchical task-building-b…

Predicting the response parameters of ice-covered overhead transmission lines (OHTLs) is critical for assessing the reliability of power systems. However, current methodologies face significant challenges: physics-based simulations are computationally demanding and thus infeasible for real-time applications, while conventional machine learning (ML) models often lack generalization capability due …

In modern industrial scenarios, federated learning (FL) has been widely adopted due to its advantages in privacy preservation and distributed modeling. However, existing FL approaches rely on a single update paradigm, which significantly hampers communication efficiency. Moreover, the server preserves historical aggregation states to model causal propagation across training rounds, instead of per…

Electricity price forecasting remains a critical research focus in modern power systems, where future electricity prices are influenced not only by historical electricity price patterns but also by exogenous factors. Traditional approaches often employ single-stream recurrent neural networks to integrate exogenous variables with historical electricity price sequences. However, since historical el…

The development of control methods in vehicle platoon with irregular constraints has emerged as a research topic of considerable interest. This article investigates the adaptive tracking control for vehicle platoon systems with irregular constraints and input saturation. First, a sufficiently differentiable shift function is designed to distinguish clearly between constrained and unconstrained st…

The transition to Industry 5.0, supported by the high-bandwidth and low-latency capabilities of 6G networks, accelerates the demand for personalized products, making efficient and accurate product style recognition a critical task. However, traditional recognition models, which rely heavily on large-labeled datasets, struggle with generalization and efficiency in such data-rich environments. In c…

Accurate recognition of coal-rock properties (CRPs) while drilling is a critical prerequisite for ensuring the intelligent control and stable operation of antipunching drilling robot. The primary challenges in industrial applications are potential safety hazards and single drilling mode in coal mine roadways, resulting in samples with insufficient number, unbalanced distribution, and background i…