AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The proposed IoT-MDS-EDFIM-IGANN framework is efficient, accurate, and cost-effective solution for induction motor fault diagnosis and combines advanced preprocessing, class balancing, feature extraction, and optimization to achieve reliable predictive maintenance and promote operational reliability of industrial induction motor systems.
The Predictive Maintenance Fault Network (PdM-FaultNet) is the combination of the Enhanced Wombat Optimization Algorithm (EWOA) for the feature selection and Dual Quantum-inspired Denoising Autoencoder Transformer (DQDAT) for the predictive modeling.
An end-to-end predictive maintenance system is proposed for high stress mechanical drivetrain and rotating machinery and the architecture proposed combines an industrial Internet of Things edge sensory network and hybrid machine learning and deep learning pipelines.
Motors are crucial elements in the industry, where unexpected failures can interrupt production cycles, reduce profits, and raise safety concerns; and therefore an early anomaly detection in motor behavior is highly appreciated. As an extension of the known internet of things (IoT), industrial IoT or IIoT allows connection of motors and their drive units through distributed sensing platforms capable of acquiring operational data related to power, temperature, vibration, and rotational speed. Once these data are transferred through the available IIoT infrastructure and stored appropriately for later off-line processing, the limited availability of labeled fault data remains a major obstacle in practical industrial applications. As a remedy, this study proposes a semi-supervised anomaly detection framework that relies exclusively on non-intrusive three-phase electrical telemetry. Focusing on a commercial offset printing press, high-frequency power measurements were collected from failure-sensitive dryer motors. Time-domain statistical features, including mean, clearance factor, and shape factor extracted from active power, power factor, and current signals, were employed to train an unsupervised One-Class Support Vector Machine (OC-SVM). Experimental results obtained from 84 hours of real industrial telemetry demonstrated the effectiveness of the proposed approach in modeling normal operating behavior, achieving a Recall of 93.79%, a False Positive Rate of 5.12%, and an F1-Score of 94.59%. The developed framework enables early anomaly detection, lowers maintenance expenses, reduces operational downtime, and enhances overall system reliability, supporting the advancement of smart Industry 4.0 environments.
— Fault Detection and Diagnosis (FDD) are vital for maintaining the energy efficiency and reliability of centrifugal chillers. This study proposes a hybrid data-driven approach that combines Radial Basis Function (RBF) regression and Gaussian Process Regression (GPR) to develop an accurate residual-based reference model. Deviations in thermodynamic parameters are continuously tracked using an Exponentially Weighted Moving Average (EWMA) control chart to detect condenser fouling and refrigerant leakage at multiple severity levels. The proposed RBF_GPR_EWMA framework was validated using the ASHRAE RP-1043 dataset and real chiller data obtained from a hospital in Ho Chi Minh City. The results demonstrate high prediction accuracy (R 2 > 0.99) and reliable detection of early-stage performance degradation. The proposed framework does not require fault labels or complex feature design, offering robustness and interpretability. The simplicity and adaptability of the framework make it suitable for integration into building management systems to support condition-based maintenance and energy-efficient operation of heating, ventilation, air conditioning, and refrigeration equipment.
Temperature sensor systems are critical for nuclear power plant (NPP) condition monitoring, whose reliability underpins unit safety and stability. Fault localization and diagnosis are essential to sustain their stable service. Conventional Principal Component Analysis (PCA) and Graph Neural Network (GNN) methods suffer clear drawbacks: PCA is vulnerable to noise and cannot classify fault types accurately, while GNNs struggle to quantify correlations among temperature data. This paper fuses PCA’s anomaly representation capability and GNN’s structural feature extraction capacity to propose an Abnormal Feature-GCN method for joint fault localization and diagnosis. First, an Adaptive PCA (APCA) model fed with multi-dimensional sensor features computes abnormal features. These features are then transformed into edge weights to construct a weighted graph. A dual-branch GCN is finally trained via a joint loss function for parallel multi-task learning to simultaneously locate faulty sensors and identify fault types. Validated on a nuclear primary circuit temperature sensor system under constant-, rising-, and falling-temperature working conditions, the proposed method realizes accurate fault localization and classification. The mean overall accuracy of the proposed method surpasses mainstream baselines by 2.23%, 1.62%, and 1.89% for the three typical working conditions, respectively.
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