Yes / Safety and reliability are two important aspects of dependability that are needed to be rigorously evaluated throughout the development life-cycle of a system. Over the years, several methodologies have been developed for the analysis of failure behavior of systems. Fault tree analysis (FTA) is one of the well-established and widely used methods for safety and reliability engineering of systems. Fault tree, in its classical static form, is inadequate for modeling dynamic interactions between components and is unable to include temporal and statistical dependencies in the model. Several attempts have been made to alleviate the aforementioned limitations of static fault trees (SFT). Dynamic fault trees (DFT) were introduced to enhance the modeling power of its static counterpart. In DFT, the expressiveness of fault tree was improved by introducing new dynamic gates. While the introduction of the dynamic gates helps to overcome many limitations of SFT and allows to analyze a wide range of complex systems, it brings some overhead with it. One such overhead is that the existing combinatorial approaches used for qualitative and quantitative analysis of SFTs are no longer applicable to DFTs. This leads to several successful attempts for developing new approaches for DFT analysis. The methodologies used so far for DFT analysis include, but not limited to, algebraic solution, Markov models, Petri Nets, Bayesian Networks, and Monte Carlo simulation. To illustrate the usefulness of modeling capability of DFTs, many benchmark studies have been performed in different industries. Moreover, software tools are developed to aid in the DFT analysis process. Firstly, in this chapter, we provided a brief description of the DFT methodology. Secondly, this chapter reviews a number of prominent DFT analysis techniques such as Markov chains, Petri Nets, Bayesian networks, algebraic approach; and provides insight into their working mechanism, applicability, strengths, and challenges. These reviewed techniques covered both qualitative and quantitative analysis of DFTs. Thirdly, we discussed the emerging trends in machine learning based approaches to DFT analysis. Fourthly, the research performed for sensitivity analysis in DFTs has been reviewed. Finally, we provided some potential future research directions for DFT-based safety and reliability analysis.

This research investigates an important class of hardware attacks against embedded software, which uses fault injection as a hacking tool. Fault attacks use well-chosen, targeted fault injection combined with clever system response analysis to break the security of a system. In case of a fault attack on embedded software, faults are injected into the underlying processor hardware and their effects are observed in the executed software's output. This introduces an additional difficulty in mitigation of fault attack risk. Designing efficient countermeasures requires first understanding software, instruction-set, and hardware level components of fault attacks, and then, systematically addressing the vulnerabilities at each level. This research first proposes an instruction fault sensitivity model to capture effects of fault injection on embedded software. Based on the instruction fault sensitivity model, a novel fault attack method called MAFIA (Micro-architecture Aware Fault Injection Attack) is also introduced. MAFIA exploits the vulnerabilities in multiple abstraction layers. This enables an adversary to determine best points to attack during the execution as well as pinpoint the desired fault effects. It has been shown that MAFIA breaks the existing countermeasures with significantly fewer fault injections than the traditional fault attacks. Another contribution of the research is a fault attack simulator, MESS (Micro-architectural Embedded System Simulator). MESS enables a user to model hardware, instruction-set, and software level components of fault attacks in a simulation environment. Thus, software designers can use MESS to evaluate their programs against several real-world fault attack scenarios. The final contribution of this research is the fault-attack-resistant FAME (Fault-attack Aware Microprocessor Extensions) processor, which is suited for embedded, constrained systems. FAME combines fault detection in hardware and fault response in software. This allows low-cost, performance-efficient, flexible, and backward-compatible integration of hardware and software techniques to mitigate fault attack risk. FAME has been designed as an architectural concept as well as implemented as a chip prototype. In addition to protection mechanisms, the chip prototype also includes fault injection and analysis features to ease fault attack research. The findings of this research indicate that considering multiple abstraction layers together is essential for efficient fault attacks, countermeasures, and evaluation techniques. / Ph. D.

published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

The North Anatolian Fault Zone (NAFZ) in Turkey is a major continental strike-slip fault, 1200 km long and with a current slip rate of 25 mm/yr. Historical records show that the NAFZ is capable of producing high-magnitude earthquakes, activating different segments of the fault in a westward progression. Currently, the NAFZ poses a major seismic hazard for the city of Istanbul, which is situated close to one of the two strands into which the fault splays in northwestern Turkey. Understanding of fault zone structure and properties at depth is essential to constrain where deformation occurs within the lithosphere and how strain localises with depth. In fact, geodynamic models explaining surface deformation require knowledge of the width and depth extent of the fault zone in both the crust and upper mantle. In this framework, this thesis aims to provide better constraints on fault zone geometry within the lithosphere. To achieve this objective P and S wave teleseismic tomography have been applied to the data recorded by a dense array of broadband seismic stations (DANA, Dense Array for Northern Anatolia); through teleseismic tomography it was possible to image the NAFZ structure in both the crust and uppermost mantle. In addition, joint inversion i of P-wave teleseismic data and local earthquake data collected using the same array provided a greatly improved resolution within the upper 20 km of the crust. Results from this work highlighted the presence of a shear zone associated to the northern branch of the NAFZ in the study area. The fault zone appears to be 15 km wide within the upper crust and narrows to < 10 km within the lower crust and to Moho depth. In the uppermost mantle its width is constrained to be 30 to 50 km.

In this dissertation, we study two new approaches to fault detection for autonomous robots. The first approach involves the synthesis of software components that give a robot the capacity to detect faults which occur in itself. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data in three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots both while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of the number of false positives and the time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot's sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task. Finally, we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot. The second approach involves the use of firefly-inspired synchronization to allow the presence of faulty robots to be determined by other non-faulty robots in a swarm robotic system. We take inspiration from the synchronized flashing behavior observed in some species of fireflies. Each robot flashes by lighting up its on-board red LEDs and neighboring robots are driven to flash in synchrony. The robots always interpret the absence of flashing by a particular robot as an indication that the robot has a fault. A faulty robot can stop flashing periodically for one of two reasons. The fault itself can render the robot unable to flash periodically. Alternatively, the faulty robot might be able to detect the fault itself using endogenous fault detection and decide to stop flashing. Thus, catastrophic faults in a robot can be directly detected by its peers, while the presence of less serious faults can be detected by the faulty robot itself, and actively communicated to neighboring robots. We explore the performance of the proposed algorithm both on a real world swarm robotic system and in simulation. We show that failed robots are detected correctly and in a timely manner, and we show that a system composed of robots with simulated self-repair capabilities can survive relatively high failure rates. We conclude that i) fault injection and learning can give robots the capacity to detect faults that occur in themselves, and that ii) firefly-inspired synchronization can enable robots in a swarm robotic system to detect and communicate faults.

The development of condition monitoring and fault diagnosis systems for wind turbines has received considerable attention in recent years. With wind playing an increasing part in Canada’s electricity demand from renewable resources, installations of new wind turbines are experiencing significant growth in the region. Hence, there is a need for efficient condition monitoring and fault diagnosis systems for wind turbines. Gearbox, as one of the highest risk elements in wind turbines, is responsible for smooth operation of wind turbines. Moreover, the availability of the whole system depends on the serviceability of the gearbox. This work presents signal processing and soft computing techniques to increase the detection and diagnosis capabilities of wind turbine gearbox monitoring systems based on vibration signal analysis. Although various vibration based fault detection and diagnosis techniques for gearboxes exist in the literature, it is still a difficult task especially because of huge background noise and a large solution search space in real world applications. The objective of this work is to develop a novel, intelligent system for reliable and real time monitoring of wind turbine gearboxes. The developed system incorporates three major processes that include detecting the faults, extracting the features, and making the decisions. The fault detection process uses intelligent filtering techniques to extract faulty information buried in huge background noise. The feature extraction process extracts fault-sensitive and vibration based transient features that best describe the health of the gearboxes. The decision making module implements probabilistic decision theory based on Bayesian inference. This module also devises an intelligent decision theory based on fuzzy logic and fault semantic network. Experimental data from a gearbox test rig and real world data from wind turbines are used to verify the viability, reliability, and robustness of the methods developed in this thesis. The experimental test rig operates at various speeds and allows the implementation of different faults in gearboxes such as gear tooth crack, tooth breakage, bearing faults, iv and shaft misalignment. The application of hybrid conventional and evolutionary optimization techniques to enhance the performance of the existing filtering and fault detection methods in this domain is demonstrated. Efforts have been made to decrease the processing time in the fault detection process and to make it suitable for the real world applications. As compared to classic evolutionary optimization framework, considerable improvement in speed has been achieved with no degradation in the quality of results. The novel features extraction methods developed in this thesis recognize the different faulty signatures in the vibration signals and estimate their severity under different operating conditions. Finally, this work also demonstrates the application of intelligent decision support methods for fault diagnosis in gearboxes. / UOIT

Condition monitoring of hydraulic systems is an area that has grown substantially in the last few decades. This thesis presents a scheme that automatically generates the fault symptoms by on-line processing of raw sensor data from a real test rig. The main purposes of implementing condition monitoring in hydraulic systems are to increase productivity, decrease maintenance costs and increase safety. Since such systems are widely used in industry and becoming more complex in function, reliability of the systems must be supported by an efficient monitoring and maintenance scheme. This work proposes an accurate state space model together with a novel model-based fault diagnosis methodology. The test rig has been fabricated in the Process Automation and Robotics Laboratory at UBC. First, a state space model of the system is derived. The parameters of the model are obtained through either experiments or direct measurements and manufacturer specifications. To validate the model, the simulated and measured states are compared. The results show that under normal operating conditions the simulation program and real system produce similar state trajectories. For the validated model, a condition monitoring scheme based on the Unscented Kalman Filter (UKF) is developed. In simulations, both measurement and process noises are considered. The results show that the algorithm estimates the iii system states with acceptable residual errors. Therefore, the structure is verified to be employed as the fault diagnosis scheme. Five types of faults are investigated in this thesis: loss of load, dynamic friction load, the internal leakage between the two hydraulic cylinder chambers, and the external leakage at either side of the actuator. Also, for each leakage scenario, three levels of leakage are investigated in the tests. The developed UKF-based fault monitoring scheme is tested on the practical system while different fault scenarios are singly introduced to the system. A sinusoidal reference signal is used for the actuator displacement. To diagnose the occurred fault in real time, three criteria, namely residual moving average of the errors, chamber pressures, and actuator characteristics, are considered. Based on the presented experimental results and discussions, the proposed scheme can accurately diagnose the occurred faults.

Commercial building owners spent $167 billion for energy in 2006. Building commissioning services have proven to be successful in saving building energy consumption. However, the optimal energy performance obtained by commissioning may subsequently degrade. The persistence of savings is of significant interest. For commissioning persistence, two statistical approaches, Days Exceeding Threshold-Date (DET-Date) method and Days Exceeding Threshold-Outside Air Temperature (DET-Toa) method, are developed to detect abnormal whole building energy consumption, and two approaches called Cosine Similarity method and Euclidean Distance Similarity method are developed to isolate the possible fault reasons. The effectiveness of these approaches is demonstrated and compared through tests in simulation and real buildings. The impacts of the factors including calibrated simulation model accuracy, fault severity, the time of fault occurrence, reference control change magnitude setting, and fault period length are addressed in the sensitivity study. The study shows that the DET-Toa method and the Cosine Similarity method are superior and more useful for the whole building fault detection and diagnosis.

No description available.

Recovered core samples from the San Andreas Fault Observatory at Depth (SAFOD), located near Parkfield, CA, offer a unique opportunity to study the products of faulting and to learn about the mechanisms of slip at 3 km depth. Casing deformation reflects active creep along two strands of the San Andreas Fault (SAF) at SAFOD. The two fault strands are referred to as the Southwest Deforming Zone (SDZ) at 3194 m measured depth (MD) and the Central Deforming Zone (CDZ) at 3301 m MD. The SDZ and CDZ contain remarkably similar gouge layers, both of which consist of a clay-bearing, ultrafine grain matrix containing survivor clasts of sandstone and serpentinite. The two gouges have sharp boundary contacts with the adjacent rocks. We have used X-ray Computed Tomography (XCT) imaging, at two different sampling resolutions, to investigate the mesoscale and microscale structure of the fault zone, specifically to characterize the shape, preferred orientation, and size distribution of the survivor clasts. Using various image processing techniques, survivor clast shape and size are characterized in 3D by best-fit ellipsoids. Renderings of survivor clasts illustrate that survivor clasts have fine tips reminiscent of sigma type tails of porphyroclasts observed in myolonites. The resolution of the XCT imaging permits characterization of survivor clasts with equivalent spherical diameters greater than 0.63 mm. The survivor clast population in both the SDZ and CDZ gouge layers have similar particle size distributions (PSD) which fit a power law with a slope of approximately -3; aspect ratio (major to minor axis ratios) distributions also are similar throughout ranging between 1.5 and 4, with the majority occurring between 2-2.5. The volume- and shape- distributions vary little with position across the gouge zones. A strong shape preferred orientation (SPO) exists in both creeping zones. In both the SDZ and CDZ the minor axes form a SPO approximately normal to the plane of the San Andreas Fault (SAF), and the major axes define a lineation in the plane of the SAF. The observation that the size-, shape- and orientation-distributions of mesoscale, matrix-supported clasts are similar in the SDZ and CDZ gouge layers, and vary little with position in each gouge layer, is consistent with the hypothesis that aseismic creep in the SDZ and CDZ is achieved by distributed, shearing. The consistency between the SPO and simple-shear, strike-slip kinematics, and the marked difference of PSD, fabric, cohesion and clast lithology of the gouge with that of the adjacent rock, is consistent with the hypothesis that the vast majority of the shear displacement on the SAF at SAFOD is accommodated within the gouge layers and the gouge displays a mature, nearly steady-state structure.