Intelligent Condition Monitoring using Machine Learning,Part 2.2

Offentlig prosjektsammendrag

Our company founded in 1989 and has over 30 years of experience from various rotational machinery activities - on ships, offshore and land-based industries. The core business is condition monitoring and troubleshooting services through analysis and diagnostics to customers both on- and offshore, mostly based on vibration analysis. Condition monitoring can benefit resulting in shorter downtimes, higher operation reliability, reduced maintenance cost and more effective planning. However, machines today are increasingly complex and data processing requires deep knowledge and experience. Therefore, there is a need for research on intelligent condition monitoring techniques that can be applied to complex systems in variable operating conditions and be a part of IAS (Integrated Automation System). Our goal is to create a condition monitoring system based on machine learning techniques (SVM, Artificial Intelligence, etc.) that can be a part of IAS and could be used in "digital twin" technology in the future. The new condition monitoring system must have an auto-diagnostics modules that can recognize most of the known machinery faults in early stages and notify other systems (such as IAS) in advance.