COURSE OVERVIEW

 This practical and highly-interactive course includes various practical sessions and exercises. Theory learnt will be applied using our state-of-the-art simulators. 

 

This course is designed to provide participants with a detailed and up-to-date overview of AI-Enhanced Predictive Maintenance Professional. It covers the traditional versus predictive maintenance and the artificial intelligence (AI) and machine learning (ML); the types of machine learning covering supervised, unsupervised and reinforcement learning; the data science for predictive maintenance, basic data storage concepts and data pipeline concepts; the sensor technologies and data acquisition in industrial settings including data quality and data integrity; the data cleaning and transformation techniques and extracting relevant features from sensor data; and the data visualization. 

 

Further, the course will also discuss the regression models for predicting remaining useful life (RUL), classification models for failure prediction, time-series analysis for anomaly detection and model selection and evaluation metrics; the model persistence and model deployment basics, real-time data streaming and processing; the anomaly detection and alerting systems; and integrating with CMMS and other maintenance systems. 

 

 

 

During this interactive course, participants will learn the deployment strategies for predictive maintenance models, cloud based predictive maintenance and calculating ROI and other key performance indicators (KPIs); the economic impact assessment of predictive maintenance and ethical considerations in AI-powered predictive maintenance; the security challenges and best practices; and the future trends in AI-enhanced predictive maintenance.