ENHANCED AND OPTIMIZED INDUSTRIAL PROCESSES MANAGEMENT USING MACHINE LEARNING (ML) AND BLOCKCHAIN

Abstract

The development of the fourth Industrial Revolution 4.0 and inventive smart manufacturing gave rise to a predictive maintenance system for monitoring or tracking industrial equipment. At present, Industry 5.0 has emerged, smudging a significant milestone. Smart factories are on the rise in business productivity, thereby showcasing the limitations of Industry 4.0. The latest industrial revolution is represented by Industry 5.0, amalgamating technologies such as IoT and artificial intelligence. It encompasses an extensive network of interconnected devices, that ensures prompt data transport, especially within a 5G-capable setting. However going forward, the protocols that are based on centralization and conventional access control techniques are unlikely to remain relevant. Hence, for device-to-device interaction and communication, a robust and efficient decentralized access control system is required. Privacy, confidence and reliability stand out as the most significant issues Privacy, security and reliability stand out as primary concerns in the context of industrial process management. The melding of Blockchain (BC), Industrial IOT, MQTT communications protocol, and ML techniques are emphasized in this research study. Through industrial machinery, real-time data from sensors measure characteristics like current, vibration and temperature. Machine learning(ML) models are utilized for examining this data in order to detect any abnormalities or deviations and failures forecast. With the help of the MQTT communication protocol the consistent interaction among the cloud server, gateway devices and sensors. The system underwent testing using an up-running machine data set, employing the ML model linear regression (LR) algorithms for the processing in the proposed framework and analysis of collected/gathered data to forecast machine failures and provide enhanced and secure maintenance level. This method minimizes costs and operational interruptions through optimized maintenance decisions and schedules, illustrating an Industry 5.0 approach for advanced and smart manufacturing.