After the Prophets of Allah Almighty, the most sacred class of mankind is the class of Prophet Muhammad's Companions. Those are the people who had seen the prophet of Islam with their naked eyes, remained in his companionship and got the heights of knowledge and actions and proved to be a great example of character by attaining the image of Prophet Muhammad's model of excellence. Another class which, like the companions of Prophet (pbuh), deserves such honor is the class of Tabe’en (The followers of the Companions). Tabe'en had contributed a matchless role in the history of Islam regarding religious knowledge and literature. They had also examplary performed in social, economical, political and military services. Due to these great services they are considered the most sacred class of the Ummah after the prophet’companions. There is a list of academic and literary services ahead of the name of each person in this class. And these services are the great testimony to the greatness of these people. The prophet's companions received the religious knowledge directly from the Prophet (peace and blessings of Allah be upon him), while Tabe'en got it from the companions and then published it in the whole world. The sincere efforts performed by Tabe'en regarding Quran, Hadith, Tafseer and Islamic litrature are of so high level that no one had reached such level of sincerity in the entire history of Islam. The steps that occur after that period, regarding the development of Islamic culture are only the effects of their services. Actually ‘‘Tabe’en’’ is the only class that has spread the social, moral and spritual blessings of Islam throughout the world. That is why, it is not only the Quran that witnesses their greatness but the Prophet (peace be upon him) also praises them.
This study aims for the development of suitable fault detection and isolation technique for industrial machines. The study focuses on the development of a unified platform that considers the traits of reliable information from models, multidomain features and optimally incorporates the capabilities of intelligent processing (i.e. handling uncertainties and data unavailability). Considering the effectiveness of significant, well-established statistical and spectral features towards fault diagnosis, in this study a hybrid fault detection approach is presented that uses signal processing methods to extract multi-perspective feature knowledge of the system and make use of this low-dimensional, significant feature knowledge for intelligent classification based on artificial immune system. Furthermore, a generalized system identification approach is investigated which incorporates adaptive thresholding based fault detection and fault severity indices based fault isolation. Finally, a comprehensive hybrid fault detection and isolation method is proposed which is inspired by multilayer biological immune system. The developed hybrid fault diagnosis technique opts for the parallel operation of model-based fault diagnostics and intelligent fault detection. The proposed hybrid method carefully analyze the data-parallel operation of both methods and further incorporates a comprehensive information resolution mechanism to ensure improved and reliable fault diagnostics. In contrast to the conventional methods, the developed hybrid fault diagnosis scheme offers an improved reliability, safety and added efficiency in industrial machines.