Intelligent Fault Diagnosis Framework for Industrial Internet of Things (IIoT) Systems Using Hybrid Deep Learning

Abstract

The rapid adoption of Industrial Internet of Things (IIoT) technologies in smart manufacturing and industrial automation has significantly increased the complexity and interconnectivity of industrial systems. Although IIoT environments improve operational efficiency and real-time monitoring capabilities, they are highly vulnerable to equipment faults, sensor failures, communication disruptions, and abnormal operational conditions that can negatively affect productivity and system reliability. This research presents an intelligent fault diagnosis framework for Industrial Internet of Things systems using hybrid deep learning techniques to enhance fault detection accuracy, predictive maintenance capability, and operational stability. The proposed framework integrates hybrid deep learning models combining convolutional neural networks, recurrent neural networks, and feature extraction mechanisms to analyze multidimensional industrial data collected from sensors, machines, and communication devices within IIoT environments. Real-time data acquisition and preprocessing techniques are employed to manage noise, missing information, and heterogeneous industrial datasets. The framework utilizes intelligent pattern recognition and anomaly detection algorithms to identify fault conditions, classify operational abnormalities, and predict potential equipment failures before critical breakdown occurs. Performance evaluation is conducted using parameters such as fault classification accuracy, response time, prediction reliability, computational efficiency, and maintenance optimization capability. Experimental analysis demonstrates that the proposed hybrid deep learning framework significantly improves fault diagnosis precision and reduces false alarm rates compared with conventional machine learning and rule-based diagnostic systems. The findings further indicate enhanced adaptability in handling complex industrial datasets and dynamic operating conditions.