Deep Learning-Enabled Industrial IoT Framework for Autonomous Fault Forecasting in Connected Diesel Engine Systems
DOI:
https://doi.org/10.64137/31079458/IJCSEI-V1I2P106Keywords:
Predictive Maintenance Systems, Diesel Engine Analytics, Industrial IoT Monitoring, Condition-Based Maintenance, Autonomous Fault Detection, Sensor Data Intelligence, Telemetry-Based Diagnostics, AI-Driven Maintenance, Failure Prediction Models, Intelligent Engine MonitoringAbstract
Diesel engines power 80% of commercial traffic and 80% of the world's merchant fleet. Almost all ship propulsion engines are diesel. Because even unnoticed faults can lead to considerable losses, Predictive Maintenance (PdM) applications are needed to guarantee Availability, Reliability, and Safety. The proliferation of sensors surrounding these machines and their connectivity in the Industrial IoT (IIoT) creates a new ecosystem for Condition-Based Maintenance (CBM). Autonomously monitoring the data generated within newly connected diesel engine ecosystems would guarantee a faster response to detected symptoms. Data from analogue sensors, digital binary sensors, and telemetry lines would act as predictors to feed a fault classification engine. This would allow faster decision-making by maintenance teams and ultimately reduce repair costs and machine downtime. Connectivity enhances intelligent systems equipped with Artificial Intelligence (AI) algorithms capable of autonomously processing and analyzing the generated data. A variety of Data Challenge tasks, offered in competitions on crowdsourcing platforms like Kaggle, could be solved using the same principles. In these competitions, databases often link telemetric information of diesel engine vehicles with driving qualities like Comfort or Safety, or with the Failure classification of electric components. Such competitions generally seek to predict the Failure code.
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