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Miguel Delgado Prieto

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Miguel Delgado Prieto

Ph.D. Thesis title:
Contributions to electro-mechanical systems diagnosis by means data fusion techniques

Miguel Delgado Prieto

Juan Antonio Ortega Redondo i Antonio García Espinosa

Reading date:
26 de Octubre de 2012

Electromechanical drives have traditionally found their field of application in the industrial sector. However, the use of such systems is spreading to other sectors within the field of transport, such as the automotive sector, or to the aircraft sector with the development of the concept of More Electric Aircraft (MEA). One of the major improvements of the MEA concept is related to the actuators of the primary flight controls, where so far only have been considered electrohydraulic actuators, although the current trend is to replace them with electromechanical actuators (EMA).
Widespread use, in the future, of EMA in transport systems, is only possible with research and advances in algorithms for
detection and diagnosis of faults that may occur both, in the electrical or mechanical parts, in order to ensure the reliability of the drive and the safety of users.
During the last years, the study of electro-mechanical systems and the fault diagnosis under varying conditions of torque and speed has been mandatory. Although these requirements have been studied deeply by different authors, most of the works are focused on single fault detection. Therefore, there is a lack of diagnosis methods able to detect different kinds of faults in an electro-mechanical actuator. There are very few studies related with diagnosis schemes capable of identifying various faults under different operating conditions, and even less analyzing deeply all the diagnosis chain to face the challenge from all possible perspectives.
In this research work, it is proposed the investigation towards integral health monitoring schemes for electro-mechanical systems based on pattern recognition. In order to identify various faults under different operating conditions, the health monitoring scheme is developed from a data fusion point of view. The processing of great deals of information enhances the pattern recognition capabilities but, in turn, requires the implementation of advanced techniques and methodologies.
Therefore, first, it is proposed in this research work a review of the whole diagnosis chain, including the different stages (feature calculation, features reduction and classification), the methodologies and techniques. The review finishes by presenting the proposed strategies to take a step further in each diagnosis stage, proposing methodologies to be investigated which would allow a significant advance towards the integral diagnosis systems.
In this sense, investigation towards a novel feature calculation methodology able to deal with non-stationary conditions is presented. Next, the feature reduction stage is covered by the proposal of collaborative methodologies by different techniques to improve the significance of the reduced feature set. Also, a more concrete approach is developed by non-lineal techniques, which are not commonly used. Finally, different classification structures are analyzed and novel classification architecture is proposed to be applied in multi-fault diagnosis problems.
Experimental analyses are presented resulting from the application of the proposed strategies to different electro-mechanical arrangements. The obtained results achieve high performance levels, and the proposed methodologies can be adapted to the necessary diagnostic requirements. It should be noticed that the proposed contributions increase the information obtained from the system to a better understanding of its behavior and this, has a direct effect over the reliability of the system operation.