Apport de l'intelligence artificielle dand la detection et le diagnostic des defauts d'une machine electrique

Abstract

Fault detection and identification has the advantage of reducing the reliance on experienced personnel with expert knowledge. Various diagnostics methods have been proposed for different types of rotating machinery. However, little research has been conducted on synthesizing and analyzing these techniques, resulting in apprehension when technicians need to choose a technique suitable for application. This paper presents a review of a variety of diagnosis that have had demonstrated success when applied to rotating machinery and highlights fault detection and identification techniques based mainly on artificial intelligence approaches especially deep learning. The literature is categorized in the following order; chapter 1 introduces the reader to the machines fault diagnosis field This chapter gives the vocabulary and terminology of this specific domain , chapter 2 discusses the machines faults diagnosis using artificial intelligence techniques, chapter 3 introduces the deep learning technique as well giving a state of the art of the use of deep learning method in machines faults diagnosis and Finally, the conclusion section concludes this document by giving the mains contribution as well as the future perspectives of this work. Fault diagnosis of rotating machinery plays a significant role in the industrial production and engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as heavily dependence on human knowledge and professional experience, intelligent fault diagnosis based on deep learning (DL) has aroused the interest of researchers. DL achieves the desirable automatic feature learning and fault classification. Therefore, in this review, DL and DL-based intelligent fault diagnosis techniques are overviewed. DLbased fault diagnosis approaches for rotating machinery are summarized and discussed, primarily including bearing, gear/gearbox and pumps. Finally, with respect to modern intelligent fault diagnosis, the existing challenges and possible future research orientations are prospected and analyzed. This topic has attracted researchers to work in during the past few years because of its great influence on the operational continuation of many industrial processes. Correct diagnosis and early detection of incipient faults result in fast unscheduled maintenance and short down time for the machine under consideration. It also avoids harmful, sometimes devastative, consequences and helps reduce financial loss. Reduction of the human expert’s involvement in the diagnosis process has gradually taken place upon the recent developments in the modern artificial intelligence (AI) tools.