| The ability to accurately diagnose and recover from faults in complex systems such as the copiers of Océ Technologies constitutes a crucial element in achieving higher system dependability. As effective recovery (or repair) fully depends on the accuracy of the fault diagnostic process to determine the root cause of failure, fault diagnosis (FD) is the key determining factor. Apart from the operational phase, FD is also beneficial in the development phase where many system faults occur as a result of improper design and/or integration. The increasing complexity of multidisciplinary systems that integrate mechanical, electronics, and embedded software components, such as paper handling systems, poses an increasing tension between the effort of developing (correct) embedded FD software, and the FD accuracy that is required to improve system dependability. For these types of complex systems, the effort to realize FD mechanisms that have sufficient diagnostic accuracy to be practically useful in dependability enhancement, is increasingly becoming prohibitive. In the Finesse project, we develop and investigate an improved FD strategy, based on a novel FD method within a model-based approach. The method provides the required diagnostic accuracy to meet the challenges posed by the complex application carrier. The model-based approach reduces the embedded FD software development effort since it is used to generate the code from. As the model-based approach is relatively wellestablished, the FD method is the central theme in Finesse. Due to many reasons, explained later on, diagnostic models of complex systems usually allow for many diagnostic solutions, ordered in terms of probability, while only one of the solutions reflects actual system health (e.g., the combination of HW component X and SW component Y is unambiguously at fault). In order to radically improve FD accuracy compared to the current state-of-the-art, we propose to (1) improve the quality of the probabilistic diagnosis ranking process, and (2) to significantly decrease the number of diagnosis solutions. To address the former, we develop an improved fault probability modelling method to estimate the a priori probability of faults occurring in software components, which is much more complex than hardware component fault probability modeling. To address the latter, we develop an improved FD algorithm which includes the ability to reason over time at low-cost as well as to automatically generate test vectors as part of the diagnostic reasoning process. The combination of enhanced (SW) fault probability analysis, as well as concepts known from the automatic test pattern generation and sequential diagnosis disciplines within a model-based, FD algorithm has not been proposed before. The above FD approach will be implemented in terms of an existing, model-based tool set called UpTime, owned by Science & Technology, and validated on a paper handling system (PHS) of Océ Technologies in terms of a demonstrator. The issues that will be investigated include the adequacy of the new FD approach to improve system dependability during operations, the effort spent in modeling, the computational costs of the FD approach, all compared to traditional techniques, as well as architectural development topics such as the added (dependability) value of improved sensor placement, and improved testability features. Utilization The results of the research (fault probability modeling and FD algorithm), will be applied in two ways. The FD approach will be applied within the PHS technology of Océ Technologies, which will allow Océ Technologies to improve the dependability of their products in terms of both design and operation. Better FD during design leads to better designs, and also shortens the development cycle which enables Océ Technologies to introduce products with less time to market and with less development cost. Better FD during operation leads to better (more dependable) performing products (greater mean time between failure and less downtime through better preventive maintenance and more focused and quick repair), with less service cost. The FD algorithm will be integrated in the UpTime model-based system health management environment, that is used by Science & Technology to develop high-dependability solutions for industrial appliances (e.g., develop better designs, generate embedded FD code). Apart from Océ Technologies and LogicaCMG, the impact of the research is very high. Many manufacturers that produce complex hardware-software artifacts performing functions with a high economic added value and/or which are life-critical, are facing tremendous problems with respect to systems dependability, and have traditionally spent a huge effort on devising FD mechanisms. On a national scale examples can be found at industries such as ASML, Philips (Medical Systems, Consumer Electronics, Semiconductors), and Astron, apart from Océ Technologies. The FD solution provided by the Finesse project is directly applicable to the above domains. In general, the range of potential applications is virtually unlimited, potentially leading to autonomic dependability in many so-called intelligent products, ranging from PDAs to TVs, satellites, medical devices, wafer scanners, and process plants. |
