Online Fault Diagnosis and Condition Monitoring of Electric Machines
Sponsored by: Enging
- Fault Diagnosis
Date: 26 September
Time: 3PM London/10AM New York
The importance of condition monitoring and fault diagnosis for electrical machines.
The development of fault diagnosis and condition monitoring techniques applied to electric machines (electric motors and power transformers) has a paramount importance for the implementation of proper predictive maintenance techniques to these industrial assets. Recently, predictive maintenance as attracted lot of interest since firstly it allows to predict when a failure might occur, and secondly, to prevent the occurrence of the failure by performing maintenance.
Monitoring for future failure allows maintenance to be planned before the failure occurs. Ideally, predictive maintenance allows the maintenance frequency to be as low as possible to prevent unplanned reactive maintenance, without incurring costs associated with doing too much preventive maintenance.
Naturally, the effectiveness of the predictive maintenance approach will strongly depend on the techniques used since the chosen approaches must be effective at predicting failure and also provide sufficient warning time for upcoming maintenance. According to this, this webinar focuses the most relevant online techniques (no need to stop the equipment to perform measurements) related to fault diagnosis and condition monitoring of electric motors and power transformers. The general webinar content is:
• Introduction to predictive maintenance techniques
• Online fault diagnosis and condition monitoring of electric motors
o Acoustic, thermal, vibration and electrical analysis
o Case studies/examples
• Online fault diagnosis and condition monitoring of power transformers
o Acoustic, thermal, partial discharges, dissolved gas and electrical analysis
o Case studies/examples
The knowledge of the most advanced fault diagnosis and condition monitoring techniques applied to electric motors and power transformers is extremely important to decide which predictive maintenance approaches can be applied to these specific assets. When predictive maintenance is working effectively as a maintenance strategy, maintenance is only performed on machines when it is really required, that is, just before a failure is likely to occur.
This brings several cost savings such as minimizing the time the equipment is being maintained, minimizing the production hours lost due to maintenance, and minimizing the cost of spare parts and supplies.
Jorge Oliveira Estima,
Jorge O. Estima was born in Aveiro, Portugal in 1984. He received the Dipl. Eng. And the Dr. Eng. degree from University of Coimbra, Portugal, in 2007 and 2012, respectively. Since 2016 he has been with University of Beira Interior where he is Invited Assistant Professor at the Department of Electromechanical Engineering and member of CISE - Electro mechatronic Systems Research Centre.
Since 2016 he has also been with the company Enging where he is R&D Manager. He has lot of experience regarding the development of new condition monitoring techniques for electric machines and applied to the industry.
Key Learning Objectives
- Online fault diagnosis and condition monitoring applied to electric motors
- Online fault diagnosis and condition monitoring applied to power transformers
- Advantages/disadvantages of the different techniques
- Typical fault modes in electric motors and power transformers
- Head of Maintenance Department
- Maintenance Responsible
- Head of Assets Management