Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Have fundamental knowledge on data driven machine learning methods and models
• Have fundamental knowledge of the use of databases and common data formats to manage, store and process data
• Have comprehension of the benefits of preventive and predictive maintenance and condition monitoring in the industrial sector
• Have comprehension on the machine learning models and methods used in condition monitoring

Skills

• Be able to analyse data for vibrational machinery condition monitoring and the fundamental effects of vibration Measuring equipment includes sensors, signal conditioners, and video cameras
• Be able to analyse typical case studies, and identifications of malfunction
• Be able to implement data driven modelling and machine learning based systems for condition monitoring and anomaly detection
• Be able to assess the performance of data driven models for condition monitoring

Competences

• Be able to monitor the condition and develop measurable preventive maintenance for given service industry problems
• Be able to monitor machinery condition with vibration effects and computer vision using various electronic equipment and video cameras
• Be able to evaluate and compare modern data driven models and algorithms for condition monitoring
• Be able to apply a data driven approach to analyse, and implement condition monitoring methods
• Be able to use frameworks to develop machine learning based condition monitoring systems

Type of instruction

The course module will be targeted with a mixture of lectures, self-preparatory presentation by students, and discussion on various real time case studies supplemented with e-learning activities.

Extent and expected workload

Since it is a 5 ECTS course module, the work load is expected to be 150 hours for the student.

Exam

Exams