Current OBD-II vehicle systems detect misfires by monitoring slight variances of crankshaft acceleration throughout power-strokes of each of the engine’s cylinders. If the PCM determines that the acceleration of the engine’s crankshaft is inappropriate, it concludes a misfire is detected. However, after this misfire is detected, the technician still needs to diagnose (isolate) the root-cause. Diagnosis is no easy task, especially with several potential subsystems that could be at fault: fuel injection, air-intake, sparkignition, and engine-mechanical. With this being said, it is difficult for many technicians to isolate the fault causing a misfire because of the wide range of root-cause possibilities within each of the subsystems. The proposed On-Board Diagnostics III contributes to the computer-aided detection and diagnosis of future-production vehicle faults. Several data-mining algorithms were investigated and applied to data parameters collected from misfire and misfire-free fault instances. Rules were then used to accurately classify future engine misfire fault instances.

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FPGAs are attractive devices as they enable the designer to make changes to the system during its lifetime. This is important in the early stages of development when all the details of the final system might not be known yet. In a research environment like at CERN there are many FPGAs used for this very reason and also because they enable high speed communication and processing. The biggest problem at CERN is that the systems might have to operate in a radioactive envi- ronment which is very harsh on electronics. ASICs can be designed to withstand high levels of radiation and are used in many places but they are expensive in terms of cost and time and they are not very flexible. There is therefore a need to understand if it is possible to use FPGAs in these places or what needs to be done to make it possible. Mitigation techniques can be used to avoid that a fault caused by radiation is disrupting the system. How this can be done and the importance of under- standing the underlying architecture of the FPGA is discussed in this thesis. A simulation tool used for injecting faults into the design is proposed in order to verify that the techniques used are working as expected which might not always be the case. The methods used during simulation which provided the best protec- tion against faults is added to a system design which is implemented on a flash based FPGA mounted on a board. This board was installed in the CERN Proton Synchrotron for 99 days during which the system was continuously monitored. During this time 11 faults were detected and the system was still functional at the end of the test. The result from the simulation and hardware test shows that with reasonable effort it is possible to use commercially available FPGAs in a radioactive environment.

There are increasing safety concerns in chemical and petrochemical process industry. The huge explosion of Nowruz oil Field platform that happened in Persian gulf-IRAN at 1983, along with other disastrous events have effected chemical industrial renaissance and led to high demand to enhance safety. Oil and chemical Industries involve complex processes and handle hazardous materials that may potentially cause catastrophic consequences in terms of human losses, injuries, asset lost and environmental stresses. One main reason of such catastrophic events is the lack of effective control and monitoring approaches that are required to achieve successful fault diagnosis and accurate hazard identification. Currently, there are aggressive worldwide efforts to propose an effective, robust, and high accuracy fault propagation analysis and monitoring techniques to prevent undesired events at early stages prior to their occurrence. Among these requirements is the development of an intelligent and automated control and monitoring system to first diagnose faulty equipment and process variable deviations, and then identify hazards associated with faults and deviations. Research into safety and control issues become high priority in all aspects. To support these needs, predictive control and intelligent monitoring system is under study and development at the Energy Safety and Control Laboratory (ESCL) – University of Ontario Institute of Technology (UOIT). The purpose of this research is to present a real time fault propagation analysis method for chemical / petrochemical process industry through fault semantic network (FSN) using accurate process variable interactions (PV-PV interactions). The effectiveness, feasibility, and robustness of the proposed method are demonstrated on simulated data emanating from a well-known Tennessee Eastman (TE) chemical process. Unlike most existing probabilistic approaches, fault propagation analysis module classifies faults and identifies faulty equipment and deviations according to obtained data from the underlying processes. It is an expert system that identifies corresponding causes and consequences and links them together. FSN is an integrated framework that is used to link fault propagation scenarios qualitatively and quantitatively. Probability and fuzzy rules are used for reasoning causes and consequences and tuning FSN. / UOIT

Transverse zones cross strike of thrust-belt structures as large-scale alignments of cross-strike structures. The Transylvania fault zone is a set of discontinuous right-lateral transverse faults striking at about 270º across Appalachian thrust-belt structures along 40º N latitude in Pennsylvania. Near Everett, Pennsylvania, the Breezewood fault terminates with the Ashcom thrust fault. The Everett Gap fault terminates westward with the Hartley thrust fault. Farther west, the Bedford fault extends westward to terminate against the Wills Mountain thrust fault. The rocks, deformed during the Alleghanian orogeny, are semi-independently deformed on opposite sides of the transverse fault, indicating fault movement during folding and thrusting. Palinspastic restorations of cross sections on either side of the fault zone are used to compare transverse fault displacement. The difference in shortening corresponds to the amount of displacement on either side of the transverse fault. The palinspastic restoration indicates a difference in the amount of shortening that will balance farther to the west in the Appalachian Plateau province.

With the advancement of automated control systems in the past few years, the focus has also been moved to safer, more reliable systems with less harmful effects on the environment. With increased job mobility, less experienced operators could cause more damage by incorrect identification and handling of plant faults, often causing faults to progress to failures. The development of an automated fault detection and diagnostic system can reduce the number of failures by assisting the operator in making correct decisions. By providing information such as fault type, fault severity, fault location and cause of the fault, it is possible to do scheduled maintenance of small faults rather than unscheduled maintenance of large faults. Different fault detection and diagnostic systems have been researched and the best system chosen for implementation as a distributed fault detection and diagnostic architecture. The aim of the research is to develop a distributed fault detection and diagnostic system. Smaller building blocks are used instead of a single system that attempts to detect and diagnose all the faults in the plant. The phases that the research follows includes an in-depth literature study followed by the creation of a simplified fault detection and diagnostic system. When all the aspects concerning the simple model are identified and addressed, an advanced fault detection and diagnostic system is created followed by an implementation of the fault detection and diagnostic system on a physical system. / Thesis (M.Ing. (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2009.

With the advancement of automated control systems in the past few years, the focus has also been moved to safer, more reliable systems with less harmful effects on the environment. With increased job mobility, less experienced operators could cause more damage by incorrect identification and handling of plant faults, often causing faults to progress to failures. The development of an automated fault detection and diagnostic system can reduce the number of failures by assisting the operator in making correct decisions. By providing information such as fault type, fault severity, fault location and cause of the fault, it is possible to do scheduled maintenance of small faults rather than unscheduled maintenance of large faults. Different fault detection and diagnostic systems have been researched and the best system chosen for implementation as a distributed fault detection and diagnostic architecture. The aim of the research is to develop a distributed fault detection and diagnostic system. Smaller building blocks are used instead of a single system that attempts to detect and diagnose all the faults in the plant. The phases that the research follows includes an in-depth literature study followed by the creation of a simplified fault detection and diagnostic system. When all the aspects concerning the simple model are identified and addressed, an advanced fault detection and diagnostic system is created followed by an implementation of the fault detection and diagnostic system on a physical system. / Thesis (M.Ing. (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2009.

Integrated Circuits (ICs) are an essential part of nearly every electronic device. From toys to appliances, spacecraft to power plants, modern society truly depends on the reliable operation of billions of ICs around the world. The steady shrinking of IC transistors over past decades has enabled drastic improvements in IC performance while reducing area and power consumption. However, with continued scaling of semiconductor fabrication processes, failure sources of many types are becoming more pronounced and are increasingly affecting system operation. Additionally, increasing variation during fabrication also increases the difficulty of yielding chips in a cost-effective manner. Finally, phenomena such as early-life and wear-out failures pose new challenges to ensuring robustness. One approach for ensuring robustness centers on performing test during run-time, identifying the location of any defects, and repairing, replacing, or avoiding the affected portion of the system. Leveraging the existing design-for-testability (DFT) structures, thorough tests that target these delay defects are applied using the scan logic. Testing is performed periodically to minimize user-perceived performance loss, and if testing detects any failures, on-chip diagnosis is performed to localize the defect to the level of repair, replacement, or avoidance. In this dissertation, an on-chip diagnosis solution using a fault dictionary is described and validated through a large variety of experiments. Conventional fault dictionary approaches can be used to locate failures but are limited to simplistic fail behaviors due to the significant computational resources required for dictionary generation and memory storage. To capture the misbehaviors expected from scaled technologies, including early-life and wear-out failures, the Transition-X (TRAX) fault model is introduced. Similar to a transition fault, a TRAX fault is activated by a signal level transition or glitch, and produces the unknown value X when activated. Recognizing that the limited options for runtime recovery of defective hardware relax the conventional requirements for defect localization, a new fault dictionary is developed to provide diagnosis localization only to the required level of the design hierarchy. On-chip diagnosis using such a hierarchical dictionary is performed using a new scalable hardware architecture. To reduce the computation time required to generate the TRAX hierarchical dictionary for large designs, the incredible parallelism of graphics processing units (GPUs) is harnessed to provide an efficient fault simulation engine for dictionary construction. Finally, the on-chip diagnosis process is evaluated for suitability in providing accurate diagnosis results even when multiple concurrent defects are affecting a circuit.

An electric power system consists of the generating stations, the transmission lines, and the distribution systems. Transmission lines are the connecting links between the generating stations and the distribution systems. With the rapid growth of economy and technology, the demand for large blocks of power, power quality and increased reliability suggested the interconnection of neighboring systems. Transmission lines are elements of a network which connects the generating plants to the distribution systems, and could extend hundreds of miles . Because of the long distances traversed by transmission lines over open area, they tend to fade by natural and artificial calamity imposed on the power system. It maybe easy to discover the fault with sufficient information in the populous region. When fault occurs in the remote region, it is difficult to identify the outage location. An efficient and reliable technique is thus desirable to resolve the problem. This dissertation presents the fault location for high voltage lines with Artificial Neural Network( ANN ) method. Beside the fault location, this research also improve the problem further by considering the fault resistance. The fault resistance may not remain the same due to the variation of environmental factors. The fault location may involve errors owing to the fault resistance. An algorithms has been developed in this dissertation to calculate fault resistance and revise the ANN training data for three-phase fault, double line-to-ground fault, single line-to-ground fault, and line-to-line fault. To verify the effectiveness of the method, practical transmission lines were used for tests. The results proved that the method could be used to identify the fault location effectively and help dispatchers determine a reference distance.