Enhancing Engine Reliability Testing with IoT, AI and ML: Real-Time ML-based Limit Monitoring and Predictive Maintenance with IoT sensor

The integration of Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) has transformed various industries, offering substantial benefits. The application of these technologies in engine reliability testing has immense potential as they offer real-time monitoring and analysis of engine performance parameters. Engine reliability testing is vital for ensuring the safety, efficiency, and longevity of engines. Traditional methods are time consuming, expensive, and rely heavily on manual inspection and data analysis. This paper shows how IoT, AI and ML technologies can greatly enhance the efficiency of engine reliability testing. The paper includes the following case studies: 1. ML-based Limit Monitoring for test data: With the help of machine learning models, we monitor the Engine health by comparing predicted and measured values during real-time testing. Intelligent algorithms predict and compare values, detect anomalies, and identify root causes in real-time, enhancing diagnostics and performance without requiring expert skills. 2. IoT Infrastructure for Predictive Maintenance: Implementing IoT infrastructure allows for continuous monitoring of utilities, deriving crucial insights for predictive maintenance. This approach reduces unplanned downtime, lowers maintenance costs, and improves reliability, further enhancing engine reliability testing. Additionally, the paper discusses the limitations and challenges of implementing these solutions, such as data security, privacy, and system integration.