Transformer is extensively employed in natural language processing, and computer vision (CV), with the self-attention structure. Due to its outstanding long-range dependency modeling and parallel computing capability, some leading researchers have recently attempted to apply Transformer to intelligent fault diagnosis tasks for mechanical equipment, and have achieved remarkable results. Physical phenomena such as changes in vibration, sound, and heat play a crucial role in the research of mechanical equipment fault diagnosis, which directly reflects the operational status and potential faults of mechanical equipment. Currently, intelligent fault diagnosis of mechanical equipment based on monitoring signals such as vibration, sound, and temperature using Transformer-based models remains a popular research topic. While some review literature has explored the related principles and application scenarios of Transformer, there is still a lack of research on its application in intelligent fault diagnosis tasks for mechanical equipment. Therefore, this work begins by examining the current research status of fault diagnosis methods for mechanical equipment. This study first provides a brief overview of the development history of Transformer, outlines its basic structure and principles, and analyzes the characteristics and advantages of its model structure. Next it focuses on three model variants of Transformer that have generated a significant impact in the field of CV. Following that, the research progress and current challenges of Transformer-based intelligent fault diagnosis methods for mechanical equipment are discussed. Finally, the future development direction of Transformer in the field of mechanical equipment fault diagnosis is proposed.

At present, rotating machinery is widely used in all walks of life and has become the key equipment in many production processes. It is of great significance to strengthen the condition monitoring of rotating machinery, timely diagnose and eliminate faults to ensure the safe and efficient operation of rotating machinery and improve the economic benefits of enterprises. When the state of a rotating machine deteriorates, the thermal energy that is much more than its normal operation will be generated due to the increase in the friction between the components or other factors. Therefore, using the infrared thermal camera to collect the infrared thermal images of rotating machinery and judge the health status of rotating machinery by observing the temperature distribution in the thermal images is often more rapid and effective than other technologies. Nevertheless, after decades of development, the research achievements of infrared thermography (IRT) and its application in various industrial fields are numerous and complex, and there is a lack of systematic sorting and summary of the achievements in this field. Accordingly, this paper summarizes the development and application of IRT as a non-contact and non-invasive tool for equipment condition monitoring and fault diagnosis, and introduces the basic theory of IRT, image processing technology and fault diagnosis methods of rotating machinery in detail. Finally, the review is summarized and some future potential topics are proposed, which will make the subject easier for beginners and non-experts to understand.

This paper tries to introduce a new intelligent method for the early fault diagnosis of diesel engines. Firstly, infrared thermography (IRT) is introduced into diesel engine condition monitoring, then infrared images of diesel engines in four health states, such as normal condition,...

Diesel engines find extensive application in various production sectors, including industry and agriculture. Strengthening the condition monitoring and fault diagnosis of diesel engines is of paramount importance in ensuring the smooth operation of production systems. Timely detection and elimination of defects play a crucial role in maintaining the normal functioning of these systems. Significant temperature fluctuations during the operation of diesel engines are often associated with malfunctions, including ignition failure, abnormal intake, and exhaust processes. Hence, the application of infrared thermography (IRT) for collecting infrared images of diesel engines and conducting quantitative analysis of the temperature distribution in these images has proven to be a faster and more efficient method for recognizing the health status of diesel engines, compared to other fault diagnosis methods. In recent years, there has been a growing interest in deep learning (DL) for fault diagnosis in various industries. This emerging trend has attracted significant attention from researchers. Convolutional neural network (CNN) has garnered significant attention owing to the exceptional capability in extracting image features. Therefore, the article presents a new fault diagnosis method for diesel engines using IRT and CNN. The proposed method involves conducting adaptive histogram equalization for image enhancement, followed by employing Softmax regression for pattern recognition. Finally, two sets of self-made experimental data are used to investigate the impact of temperature variations on fault diagnosis performance and to validate the efficacy of the proposed method in comparison with three DL methods. The findings indicate that this method exhibits superior performance in the realm of diesel engine fault diagnosis.