Experimental Evaluation of Deep Learning Enabled IoT Technology for Industrial Machinery Fault Detection and Performance Improvement

Abstract

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.