Compound faults, which arise from the interaction of multiple simultaneous failures, pose significant risks to the maintenance of industrial machinery in the Industrial Internet of Things (IIoT), leading to complex and unpredictable system failures. Traditional models tend to misclassify emerging compound faults as known categories due to a bias toward seen data. Additionally, the delayed emergence of compound faults relative to single faults hampers prompt sample collection. Furthermore, compound fault datasets typically exhibit a long-tailed distribution. Driven by these challenges, we propose an incremental contrastive learning model based on cross-modal contrastive embedding (ICLCFD) to achieve an incremental fault diagnosis from single faults to compound faults.Firstly, we employ Symmetrized Dot Pattern (SDP) image transformation to convert vibration signals into visual representations. We extract high-dimensional visual features from these SDP images using a Multi-Scale Residual Convolutional Neural Network (MS-ResCNN) and generate low-dimensional semantic features based on vibration signals to obtain richer feature information. Subsequently, a novelty detection mechanism is developed using the predicted number of fault sources as a count-based indicator to identify newly emerging faults. Furthermore, we incorporate a difficulty-aware class rebalancing sampling strategy to prioritize hard-to-diagnose samples during incremental updates, mitigating the excessive impact of head-class samples on the diagnosis results. Experiments on three open-source bearing datasets (CWRU, PU, and XJTU-SY) demonstrate that ICLCFD achieves a state-of-the-art accuracy of 96.58 ± 0.28%, outperforming existing methods by approximately 2%. The results confirm its effectiveness in handling unknown and compound faults in dynamic IoT maintenance pipelines.

Software-Defined Networks (SDN) bring unprecedented agility and programmability to traditional networks by decoupling the control plane and data plane. However, this separation enables adversaries to manipulate data plane forwarding behaviors or modify packet payloads, thereby violating the network security policies set by the control plane and leading to information leakage, network congestion, or even network collapse. In this article, we propose an Enhanced Lightweight Path Validation Scheme (EL-PVS) for the SDN environment. Firstly, we propose a packet forwarding path validation scheme that verifies the paths traversed by packets, alongside a theoretical analysis of this validation process. Then, we extend the scheme with a network flow-level path validation to improve the validation efficiency, and present a storage optimization method to reduce the storage overhead in the validation process. To support large-scale deployment, we design a path partition scheme and present a Greedy-based KeySwitch Node Selection Algorithm (GKSS) to pinpoint optimal switches for path partition, significantly reducing overall data plane storage usage and the total number of paths requiring validation. In addition, we extend our path validation scheme to detect packet payload modification, where a multi-phase packet modification detection approach is designed, and then the detection results are integrated with path validation information to minimize switch-to-controller bandwidth usage. Finally, we present an anomaly switch identification technique to identify abnormal switches when the controller encounters validation failure. The evaluation results verify that EL-PVS enables flow-level path validation and packet modification detection with small validation header, minimizing processing delay and switch storage overhead.

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multisource domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a dual-reweighted Siamese contrastive learning network (DRSC) for cross-domain fault diagnosis with multisource domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis.

With lithium-ion batteries prevalent in safety-critical industries, accurate and reliable estimation of battery state-of-health, termed trustworthy prognosis, has become crucial. However, neglecting model uncertainty during representative correlation learning may lead to inappropriate aggregation of ambiguous segments. Moreover, shifts in dominant spatial channels and sparsity in useful temporal features further hinder the trustworthy estimation. Furthermore, the misalignment between the predicted and actual distributions leads to a confidence biases issue and degrades performance in distinguishing out-of-distribution samples. To address these challenges, we propose a hierarchical stochastic spatial–temporal Transformer (HSSTT). First, HSSTT implements stochastic self-attention utilizing Gumbel–Softmax reparameterization for uncertainty quantification. Then, a hierarchical spatial–temporal Transformer is designed to leverage uncertainty-aware timestep-wise dilated convolution and clustered stochastic self-attention. Finally, we theoretically analyse the confidence bias issue through bias-variance decomposition and develop a principled calibration strategy. Experimental results on four datasets demonstrate the superiority of HSSTT in trustworthy prognosis against seven State-of-the-Art models.

Software-Defined Networks (SDN) revolutionize traditional networks by separating control and data planes for enhanced agility and programmability. This separation, however, also opens up vulnerabilities, allowing adversaries to manipulate data plane forwarding and breach security policies. To counter this, we propose a Lightweight Path Validation Scheme (L-PVS) specifically designed for SDN environments. Our approach uses a simple validation scheme for packet forwarding paths that verifies the paths traversed by packets. Then, we further amplify the scheme with a network flow path validation to boost the validation efficiency. To reduce storage demands on switches during flow path validation, we develop a storage optimization method that aligns switch storage overhead with network flows rather than individual packets. Furthermore, we formulate a path partition scheme and present a Greedy-based KeySwitch Node Selection Algorithm (GKSS) to pinpoint optimal switches for path partition, significantly reducing overall data plane storage usage. Lastly, we design a technique using temporary KeySwitch nodes to identify anomaly switches when the controller encounters path validation failure. Evaluation results verify that L-PVS facilitates path validation with a reduced validation header size while minimizing the impact on processing delay and switch storage overhead.

Empowered by the large amounts of sensor data in the Industrial Internet of Things, data-driven fault diagnosis has a pivotal role in improving equipment reliability in harsh industrial environments. To enhance diagnostic performance under unknown operating conditions, transfer learning-based cross-domain fault diagnosis has been emerging. However, diagnostic models are prone to overfit to the source domain due to the lack of sample diversity when only a single-source domain is available. Moreover, significant domain shifts between the single-source domain and multiple unknown target domains may degrade the generalization performance on the unknown domains. To address these challenges, we propose a multipseudo domains augmented adversarial domain-invariant learning (MDA-AD) for cross-domain fault diagnosis. First, we design a multipseudo domain generator, where interdomain diversity constraints and manifold-semantic consistency constraints are implemented to avoid overfitting on the source domain by generating diverse and representative pseudo samples. Subsequently, to alleviate the domain shift, we design an adversarial domain-aware classifier that extracts domain-invariant features by introducing an adversarial paradigm between a feature extractor and a domain discriminator. Finally, to further enhance the diversity of the pseudo domains, we implement a diversity-consistency constrained domain-invariant training strategy. The experimental results, obtained through comparative studies, hyperparameter influence analysis, and visualization on two bearing data sets, affirm the superior diagnostic performance of MDA-AD in a single-source domain.

Due to the poisonousness, explosiveness, and diffuseness of some continuous objects (e.g., toxic gas, nuclear radiation, and industrial dust), continuous object tracking has a pivotal role in protecting the safety of the people, especially in hazardous industries. To improve production safety, the Industrial Internet of Things (IIoT) has become a promising technology for continuous object tracking. However, IIoT can hardly satisfy the requirements of both energy efficiency and tracking accuracy due to diffusion characteristics, redundant packets, unnecessary awakened nodes, etc. To address these challenges, we propose a two-stage continuous object predictive tracking scheme based on a state transition model (TCOT-STM). First, the predictive tracking process of TCOT-STM is partitioned into two stages to determine wake-up regions where the future continuous objects are located. Considering the high diffusion speed in the tracking process, stage I tracking is designed by communication range calibration and global wake-up region establishing. To eliminate the redundant boundary nodes in the tracking process, stage II tracking is designed by intercluster gap eliminating, virtual node generating, and local wake-up region establishing. Then, a state transition model (STM) based on finite state machines is designed to awaken nodes selectively. Finally, with the STM and the wake-up regions determined by two-stage tracking, the potential boundary nodes are proactively awakened for predictive tracking. Simulation results demonstrate that the proposed TCOT-STM can reduce energy consumption and communication cost while improving tracking accuracy.

To improve the reliability of autonomous vehicles, open-set fault diagnosis is indispensable to jointly detect known and unknown faults, in which unknown faults only appear in the testing set. However, in learning the representations for open-set diagnosis, the extracted representations lack hierarchy to preserve high-level and genuine representations, and the final representations utilized for diagnosing lack distinctiveness to separate unknowns from knowns. In addition, in the stage of testing, the open-set diagnosis models are error-prone when unknowns are similar to knowns. Motivated by these challenges, we propose a Multi-hop Attentive Graph Variational Autoencoder (MAGVA) model for open-set fault diagnosis in this paper. First, a multi-hop attentive graph convolutional network is developed to adaptively extract hierarchical representations and eliminate unknown fault misidentification. Then, to avoid unknown faults occupying the same region as known faults and identify known faults, structural representation constraints are designed by jointly conducting reconstruction with an intra-class constraint and classification with an inter-class constraint. Finally, combining the distinguishable representations learned by MAGVA, a generative distance-based open-set diagnosis algorithm is proposed, in which the procedures of estimating class-conditional distributions are designed, and a relative generative distance is then presented to derive diagnosis results under the class-conditional distributions. Experiments on three commonly used bearing datasets for vehicles demonstrate that the proposed MAGVA consistently outperforms the compared models in open-set, closed-set, and unknown fault diagnosis.