Early diagnosis of plant faults / deviations is a critical factor for optimized and safe plant operation and maintenance. Although smart controllers and diagnosis systems are available and widely used in chemical plants, however, some faults couldn't be detected. Major reason is the lack of learning techniques that can learn from operational running data and previous abnormal cases. In addition, operator and maintenance engineer opinions and observations are not well used, while useful diagnosis knowledge is ignored. This research paper presents the framework of the proposed learning mechanisms in different stages of integrated fault diagnostic system, which is called FDS. The proposed idea will support plant operation and maintenance planning as well as overall plant safety.