Abstract: As the main sources of equipment failure, lubrication and wear faults are the serious threats to the safe, healthy, and reliable operation of industrial equipment. Lubrication and wear fault diagnosis, which have the history of more than sixty years, are the important aspect of tribology research and industrial application. As the reason of huge data sources involved in the diagnosis work, the data used for lubrication and wear fault diagnosis has the characteristic of high data dimension, diversified forms, complex structure and relationship, and unclear mapping relationship between data and fault, which seriously affects the efficiency, accuracy and pertinence of the diagnosis. On the other hand, with the development of intelligent, integrated, and large-scale equipment, lubrication wear fault diagnosis will enter the era of big data and intelligence, which will have a higher requirement for the application and analysis level of diagnostic data. As the basic work of lubrication wear fault diagnosis and the premise of data efficient application, feature extraction can reduce the dimension of original data, establish the data relationship, and obtain fault sensitivity information. Hence, a comprehensive overview on lubrication and wear fault diagnosis feature extraction is necessary. Through the analysis of the process and technology of lubrication and wear fault diagnosis, the composition of the information collected from the equipment lubrication and wear fault diagnosis was studied from three aspects of diagnostic laboratory testing, industrial field monitoring, and online real-time monitoring, and the research direction and content of feature extraction were clarified. Research achievements of lubrication and wear fault diagnosis feature extraction in 40 years were summarized from four aspects, which were the feature of wear particle image identification, wear quantitative data of wear element and particle, lubricating oil performance degradation, and lubricating oil pollution of external medium and particles, and the technology and algorithm of the four aspects feature extraction were also analyzed. In the future, lubrication and wear fault diagnosis will be extended to the equipment whole life lubrication health status recognition, evaluation, and prediction. The challenging problems of its feature extraction are that the mapping relationship between lubrication and wear fault symptoms and characterization information was not clear, the research of life cycle feature extraction was insufficient, feature extraction studies could not meet the needs of lubrication wear fault diagnosis in industrial practice, and the portability and generalization ability of the algorithm used to feature extraction were weak. According to the above challenging problems, combined with the development of equipment, the research trend and direction of feature extraction of lubrication and wear fault were pointed out. In the future, the feature extraction of lubrication wear fault will be carried out with multi-method fusion from fault mechanism analysis, bench simulation test, industrial verification evaluation, fault case and its rule reasoning. The multi-directional feature extraction research based on laboratory detection, industrial field monitoring, and online real-time monitoring should be carried out. The new theory and method of feature extraction should also be studied. According to the requirements of green and intelligent development of equipment, the feature extraction of green lubrication and the diagnosis feature extraction in big data environment will also be the focus of research.