Based on the theory of PHM and predictive maintenance, this course explores how to leverage data, algorithmic models, and vibration knowledge to achieve health assessment, anomaly detection, fault diagnosis, and specifically fault diagnosis for rotating machinery. Participants will gain an understanding of what PHM and predictive maintenance are, and what value they ultimately help us realize, enabling a correct conceptual understanding of these fields. The training demonstrates knowledge of PHM, predictive maintenance, and vibration analysis, as well as methods to implement equipment monitoring and evaluation, anomaly detection, fault diagnosis, and predictive monitoring. It aims to equip learners with practical skills to implement PHM and predictive maintenance. By integrating algorithmic models and methodologies from PHM and predictive maintenance, the course enables participants to conceptualize actionable predictive maintenance scenarios tailored to their actual conditions, using data to achieve smart equipment operations and more rational maintenance strategies.
Type
Face-to-face training
Duration
2 days
Language
zh
Target Group
Technical Development and Operations Personnel engaged in Equipment Maintenance, Fault Diagnosis, Prognostics and Health Management (PHM), and Predictive Maintenance
Content
Fundamental Concepts of Vibration
Fundamentals of Signal Processing
Time Domain and Frequency Domain of Vibration
Characteristic Values of Vibration
Eccentric Rotor, Bent Shaft and Looseness
Rolling Bearings and Gears
PHM Concept and Scope
Data Science and PHM
Predictive Maintenance Systems
Concepts of Health Assessment and Anomaly Detection
Data-Driven Fault Diagnosis
Prerequisites
Familiar with lean production management systems and with experience in the manufacturing industry
