An Innovative Decentralized and Distributed Deep Learning Framework for Predictive Maintenance in the Industrial Internet of Things

The integration of predictive maintenance (PdM) with the Industrial Internet of Things (IIoT) represents a pivotal shift in equipment management, particularly with the incorporation of deep learning (DL) for processing time series data from IIoT devices. This combination offers a sophisticated approach to predictive analysis, harnessing DL's prowess in analyzing complex patterns in large data sets. However, it also presents notable challenges, including significant security risks associated with centralized organizations and the immense volume of time series data generated by IIoT. To address these issues, our study introduces an innovative decentralized framework thoughtfully segmented into device and edge levels. This framework leverages the strengths of blockchain technology and the interplanetary file system (IPFS). IPFS effectively manages the large-scale storage needs of time series data for DL applications in a decentralized manner, while blockchain provides a robust foundation for ensuring data security and maintaining consistent transactions. Furthermore, we conducted thorough performance analyses, examining aspects, such as accuracy, execution time, and computational cost, which validated the efficacy of our approach. Security considerations were also rigorously evaluated, focusing on potential attacker scenarios, the strengths of a decentralized architecture, and the immutable nature of smart contracts. The results highlight our framework's exceptional ability to ensure the highest level of security in DL, maintain data integrity, and preserve model accuracy. In conclusion, the seamless integration of DL, PdM, blockchain, and IPFS in our framework marks a significant advancement in contemporary industrial maintenance strategies. It successfully bridges the gap between advanced security needs and the handling of vast quantities of data, positioning our approach at the forefront of modern industrial maintenance solutions.