Abstract
Tool health monitoring and maintenance has become a more challenge issue for big data recently and is extremely essential in today's highly competitive environment in industry. Good monitoring approach and right maintenance strategy will be a great benefit to the company for reducing the cost and prolong the useful life of machines. Related researches have been studied based on different methods and approaches for investigating the health status of machines in many fields. In this study, we provide a data mining framework for monitoring the tool heal status under the partial least squares approach as we as the control chart construction. A real data from panel industry is applied to demonstrate our proposed research.