Non-intrusive load monitoring (NILM) technology, crucial for intelligent electricity management, has gained considerable attention in residential electricity usage studies. NILM enables monitoring of total electrical current and voltage in homes, offering insights vital for enhancing safety and preventing domestic electrical accidents. Despite its importance, accurately discerning the operational status of appliances using non-intrusive methods remains a challenging area within this field. This paper presents a novel methodology that integrates an advanced clustering algorithm with a Bayesian network for the identification of appliance operational states. The approach involves capturing the electrical current signals during appliance operation via NILM, followed by their decomposition into odd harmonics. An enhanced clustering algorithm is then employed to ascertain the central coordinates of the signal clusters. Building upon this, a three-layer Bayesian network inference model, incorporating leak nodes, is developed. Within this model, harmonic signals are used as conditions for node activation. The operational states of the appliances are subsequently determined through probabilistic reasoning. The proposed method’s effectiveness is validated through a series of simulation experiments conducted in a laboratory environment. The results of these experiments (low mode 89.1%, medium mode 94.4%, high mode 92.0%, and 98.4% for combination) provide strong evidence of the method’s accuracy in inferring the operational status of household electrical appliances based on NILM technology.

Electric bicycles offer convenient short-distance travel, but improper battery charging poses a fire risk, especially indoors, potentially causing significant accidents, property damage, and even threats to life. Recognizing the charging state of electric bicycle batteries is crucial for safety. This paper proposes a novel method to identify the charging process of lithium batteries in electric bicycles. Methods that do not require physical alterations to the equipment are used to acquire users’ electricity consumption data, with current signals preprocessed and input into a combined model integrating convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks. The proposed model captures complex patterns and features in the charging data, effectively identifying the charging characteristics of lithium batteries. Validation using NASA’s lithium battery dataset and real experimental data shows that the combined model achieves recognition accuracy of 96% and 97% on training data and 93% and 94% on validation data. Further validation under multiple device loads and comparison with other models indicate that the proposed method is highly accurate, outperforming traditional CNN and LSTM models by 4–9%. This research enhances the safety and regulation of electric bicycle battery charging and provides a reliable method for non-intrusive load identification in smart monitoring systems, contributing to improved safety measures and energy management in residential environments.

Reactor pressure vessel (RPV) steels are highly susceptible to irradiation embrittlement due to prolonged exposure to high temperature, high pressure, and intense neutron irradiation. This leads to the shift in nil-ductility transition reference temperature—∆RT_NDT. The change in ∆RT_NDT follows a certain distribution pattern and is impacted by factors including chemical composition, neutron fluence, and irradiation temperature. Existing empirical procedures can estimate ∆RT_NDT based on fitting extensive irradiation embrittlement data, but their reliability has not been thoroughly investigated. Probability statistical distributions and the Gamma stochastic process were performed to model material property degradation in RPV steels from a pressurized water reactor due to irradiation embrittlement, with the probability models considered being normal, Weibull, and lognormal distributions. Comparisons with existing empirical procedures showed that the Weibull distribution model and the Gamma stochastic model demonstrate good reliability in predicting ∆RT_NDT for RPV steels. This provides a valuable reference for studying irradiation embrittlement in RPV materials.

This paper addresses the problem of fault diagnosis in rotating systems by monitoring and analysis of their vibration signals. To tackle the prevalent challenges posed by the scarcity of high-quality labelled data and the complexity of models in the context of intelligent fault diagnosis for edge computing deployments, this research introduces a lightweight unsupervised deep representation method for edge-side fault detection. By leveraging manifold learning, this approach places a strong emphasis on unsupervised learning and model compactness. Firstly, the approach utilises the non-linear fitting capabilities of deep learning to implement manifold learning's dimensionality reduction, which can efficiently extract structured relationships from high-dimensional data, enabling rapid mapping of unlabelled samples to a lower-dimensional space. Then, the proposed method includes a clustering algorithm to refine the model's accuracy by correcting pseudo-labels near category centres, reducing intra-class distances and enhancing low-dimensional clustering. Finally, the model employs the isolation forest (iForest) algorithm for the rapid identification of both known and novel fault types at the edge, an innovative step in fault detection technology. Experiments on an open-source dataset and a real-world fault dataset collected by a customised test-rig indicate that the model proves effective in complex scenarios involving unlabelled data, sample imbalances and emerging fault categories. Its success underscores its significant potential for real-time online fault diagnosis in industrial environments, marking a substantial advancement in the field of intelligent fault identification.