Trends in condition monitoring of pitch bearings

The value of wind power generation for energy sustainability in the future is undeniable. Since operation and maintenance activities take a sizeable portion of the cost associated with offshore wind turbines operation, strategies are needed to decrease this cost. One strategy, condition monitoring (CM) of wind turbines, allows the extension of useful life for several parts, which has generated great interest in the industry. One critical part are the pitch bearings, by virtue of the time and logistics involved in their maintenance tasks. As the complex working conditions of pitch bearings entail the need for diverse and innovative monitoring techniques, the classical bearing analysis techniques are notsuitable. This paper provides a literature review of several condition monitoring techniques, organized as follows: first, arranged according to the nature of the signal such as vibration, acoustic emission and others; second, arranged by relevant authors in compliance with signal nature. While little research has been found, an outline is significant for further contributions to the literature.Postprint (published version

Performance de la modélisation hybride sur un processus de défaillance dans les systèmes industriels

Une approche hybride est proposée pour établir le diagnostic d'un moteur électrique. L'approche se caractérise par un assemblage entre la modélisation physique du système réel et la mise en place d'un algorithme d'apprentissage automatique, en vue d'améliorer les performances de diagnostic. Mots-clefs-Modèle hybride, apprentissage automatique, modèle basé sur la connaissance, processus de défaillance, calcul haute performanc

Performance de la modélisation hybride sur un processus de défaillance dans les systèmes industriels

A non-localised failure on a component can cause irreparable damage but it can also lead to the complete shutdown of the industrial system if it is not detected in time. Indeed, the first step in a failure process is the detection of the fault. Locating the fault is the second step in the process to know at which level of the system to intervene. Numerous methods for diagnosing industrial systems have already proved their worth. They are mainly based on physics-based behaviour laws. However, these behavioural models are generic and present difficulties of adaptation when applied to particular job profiles. Moreover, when dealing with complex systems, the implementation of behavioural laws the coupling of multiple components, interacting with each other with each other, is a laborious and time-consuming task. time-consuming task. The development of industrial systems instrumentation also encourages the use of the potential of real-time data collected on the systems. The problem in studying the data is the transparency of the models created solely from this data. The weight of the interactions present between the system's variables is not always identifiable. This means that the models developed from the data will not be easily transposable from one system to another, guaranteeing the same performance. To improve this adaptability, the idea is to draw on the knowledge of the system in question and to integrated into the modelling. For this purpose, models based on and those based on data learning will be coupled in order to data learning will be coupled in order to study the overall performance of this type of modelling. In the literature, this In the literature, this coupling is called hybrid modelling. To understand the construction process of such a model, the To understand the construction process of such a model, the study proposes to focus on the modelling of a DC electric motor. This application, which is widely studied in the literature, allows us to to exploit existing physical models of the system. The objective of this paper is therefore to study the The objective of this paper is therefore to study the performance of hybrid modelling to diagnose The objective of this paper is therefore to study the performance of hybrid modelling to diagnose the failures of a DC electric motor. To this end, the paper will describe the construction of the data-based model and the theoretical model. model and the theoretical model by discussing the capabilities and the capabilities and limitations of each model. The implementation of a The implementation of a hybrid approach will then be detailed. Finally, the performance of the implemented models will be presented