Industrial machinery fault detection is a cornerstone of predictive maintenance, directly influencing operational reliability, safety, and production efficiency. Conventional rule-based and machine learning approaches often struggle to handle non-stationary sensor signals, cross-machine variability, and early-stage fault manifestations. This paper proposes a Quantum-Inspired Neuro-Federated Spatio-Temporal Autoencoding Transformer (QNF-STAEFormer) to smartly, scalably and privately diagnose faults in industries. The model incorporates self-adaptive multi-modal sensing, neuromorphic event-driven signal conditioning, physics-directed multi-resolution decomposition, graph-wavelet spatio-temporal encoding, hyperdimensional latent representation learning, and self-supervised predictive modeling. A quantum-inspired reasoning layer facilitates the parallel consideration of various hypotheses on faults and a neuro-federated learning policy supports decentralized collaborative learning at industrial locations. The experimental analysis reveals that the proposed framework has a general fault classification error of 98.4%, F1-score, 98.1%, and AUC, 99.0% which is better than standard CNN, LSTM and transformer-based baselines. The model also has a high early-fault detection ability, where it has a 98.5% detected rate at a 60-minute prediction horizon. These findings prove that the proposed architecture can be used as a strong, precise, and future-proof solution to intelligent monitoring of industrial conditions.
M. Jayaprakash, P. Sundaram· International Conference Com...· 0 citations
The residential market has seen the rapid take-up of Internet of Things (IoT) devices, connected IIoT smart systems due to it. This situation gives rise to new issues and challenges for the energy management implemented in real time. The traditional centralized energy management mechanisms face great challenges like excessive latency, confidentiality vulnerability, scalability issues across heterogeneous smart home networks. The paper proposes a Federated Deep Reinforcement Learning (D-RLR) framework based on DQN agents and federated learning method. The framework allows for the decentralized training of DQN models in smart home nodes with the restriction that raw energy consumption data remain local. Each agent carries out the processing of grid price signal, usage data of appliances, observed amount and availability of local renewable energy to develop optimal load scheduling policy. The overall demand response policy is obtained by periodic federated aggregation of local models, helping to reduce communication latency. Experiments conducted on a simulated IIoT smart home environment, constructed using appliance consumption profiles derived from publicly available residential energy datasets and realistic solar irradiance-based renewable generation profiles, demonstrate that the proposed framework reduces peak energy consumption by 23% and improves demand response efficiency by 18% relative to centralized DRL baselines. Communication overhead generates roughly 65% lesser and user privacy is preserved throughout by design. The mechanism scales across nodes and supports real-time scheduling decisions under dynamically changing grid conditions. Therefore, it can be deployed practically for next-generation residential energy management.
Sundaram P, M. Jayaprakash· 2026 4th International Confe...· 0 citations
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