PMSMs are widely used in applications on electric vehicles, robotics and mechatronic systems of industrial machinery. Thus it becomes increasingly interesting to prevent their fault or malfunctioning with Predictive Maintenance (PdM). However, reaching this outcome could be difficult, especially if the stationary condition is not achieved and without additional sensors. This paper examines the use of a load torque observer based on Extended Kalman Filter for the diagnosis of electric drives working under non-stationary conditions. The proposed Motor Torque Analysis (MTA) is compared with the Motor Current Signature Analysis by evaluating their diagnostic capabilities under the assumed conditions. Finally, the results of bearing failure detection under non-stationary conditions are presented, highlighting the superior diagnostic capabilities of the MTA under such conditions.

Comparison of PMSMS Motor Current Signature Analysis and Motor Torque Analysis under Transient Conditions

Bonci A.
Primo
Membro del Collaboration Group
;
Kermenov R.
Secondo
Membro del Collaboration Group
;
Longhi S.
Penultimo
Membro del Collaboration Group
;
Nabissi G.
Ultimo
Membro del Collaboration Group
2021

Abstract

PMSMs are widely used in applications on electric vehicles, robotics and mechatronic systems of industrial machinery. Thus it becomes increasingly interesting to prevent their fault or malfunctioning with Predictive Maintenance (PdM). However, reaching this outcome could be difficult, especially if the stationary condition is not achieved and without additional sensors. This paper examines the use of a load torque observer based on Extended Kalman Filter for the diagnosis of electric drives working under non-stationary conditions. The proposed Motor Torque Analysis (MTA) is compared with the Motor Current Signature Analysis by evaluating their diagnostic capabilities under the assumed conditions. Finally, the results of bearing failure detection under non-stationary conditions are presented, highlighting the superior diagnostic capabilities of the MTA under such conditions.
INDIN 2021 - IEEE 19th International Conference on Industrial Informatics
978-1-7281-4395-8
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11566/300789
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