Data-driven Methodology for State Detection of Gearbox in PHM Context

With the development of artificial intelligence technology, data-driven PHM technology has been widely used for life cycle health management of equipment. Equipment will generate a lot of data in the process of operation and production. Analyzing the data and establishing machine learning model can accurately evaluate the operation status of equipment. Increasingly, extracting knowledge from data has become an important task in organizations for performance improvements. Data is the resource for equipment health assessment, so it is of great significance to focus on the research of data quality. Based on this, the main work of this paper is as follows. (1) The data quality issues are discussed in the context of PHM. (2) The PHM framework is proposed for improving the reliability of equipment. (3) Several machine learning algorithms are introduced for state detection. (4) The proposed technology is applied to real cases, and the results are analyzed and visualized in detail.