Autonomous Recovery Of Reconfigurable Logic Devices Using Priority Escalation Of Slack

Field Programmable Gate Array (FPGA) devices offer a suitable platform for survivable hardware architectures in mission-critical systems. In this dissertation, active dynamic redundancy-based fault-handling techniques are proposed which exploit the dynamic partial reconfiguration capability of SRAM-based FPGAs. Self-adaptation is realized by employing reconfiguration in detection, diagnosis, and recovery phases. To extend these concepts to semiconductor aging and process variation in the deep submicron era, resilient adaptable processing systems are sought to maintain quality and throughput requirements despite the vulnerabilities of the underlying computational devices. A new approach to autonomous fault-handling which addresses these goals is developed using only a uniplex hardware arrangement. It operates by observing a health metric to achieve Fault Demotion using Recon- figurable Slack (FaDReS). Here an autonomous fault isolation scheme is employed which neither requires test vectors nor suspends the computational throughput, but instead observes the value of a health metric based on runtime input. The deterministic flow of the fault isolation scheme guarantees success in a bounded number of reconfigurations of the FPGA fabric. FaDReS is then extended to the Priority Using Resource Escalation (PURE) online redundancy scheme which considers fault-isolation latency and throughput trade-offs under a dynamic spare arrangement. While deep-submicron designs introduce new challenges, use of adaptive techniques are seen to provide several promising avenues for improving resilience. The scheme developed is demonstrated by hardware design of various signal processing circuits and their implementation on a Xilinx Virtex-4 FPGA device. These include a Discrete Cosine Transform (DCT) core, Motion Estimation (ME) engine, Finite Impulse Response (FIR) Filter, Support Vector Machine (SVM), and Advanced Encryption Standard (AES) blocks in addition to MCNC benchmark circuits. A iii significant reduction in power consumption is achieved ranging from 83% for low motion-activity scenes to 12.5% for high motion activity video scenes in a novel ME engine configuration. For a typical benchmark video sequence, PURE is shown to maintain a PSNR baseline near 32dB. The diagnosability, reconfiguration latency, and resource overhead of each approach is analyzed. Compared to previous alternatives, PURE maintains a PSNR within a difference of 4.02dB to 6.67dB from the fault-free baseline by escalating healthy resources to higher-priority signal processing functions. The results indicate the benefits of priority-aware resiliency over conventional redundancy approaches in terms of fault-recovery, power consumption, and resource-area requirements. Together, these provide a broad range of strategies to achieve autonomous recovery of reconfigurable logic devices under a variety of constraints, operating conditions, and optimization criteria

GA-Based fault diagnosis algorithms for distributed systems

Distributed Systems are becoming very popular day-by-day due to their applications in various fields such as electronic automotives, remote environment control like underwater sensor network, K-connected networks. Faults may aect the nodes of the system at any time. So diagnosing the faulty nodes in the distributed system is an worst necessity to make the system more reliable and ecient. This thesis describes about dierent types of faults, system and fault model, those are already in literature. As the evolutionary approaches give optimum outcome than probabilistic approaches, we have developed Genetic algorithm based fault diagnosis algorithm which provides better result than other fault diagnosis algorithms. The GA-based fault diagnosis algorithm has worked upon dierent types of faults like permanent as well as intermittent faults in a K-connected system. Simulation results demonstrate that the proposed Genetic Algorithm Based Permanent Fault Diagnosis Algorithm(GAPFDA) and Genetic Algorithm Based Intermittent Fault Diagnosis Algorithm (GAIFDA) decreases the number of messages transferred and the time needed to diagnose the faulty nodes in a K-connected distributed system. The decrease in CPU time and number of steps are due to the application of supervised mutation in the fault diagnosis algorithms. The time complexity and message complexity of GAPFDA are analyzed as O(n*P*K*ng) and O(n*K) respectively. The time complexity and message complexity of GAIFDA are O(r*n*P*K*ng) and O(r*n*K) respectively, where ’n’ is the number of nodes, ’P’ is the population size, ’K’ is the connectivity of the network, ’ng’ is the number of generations (steps), ’r’ is the number of rounds. Along with the design of fault diagnosis algorithm of O(r*k) for diagnosing the transient-leading-to-permanent faults in the actuators of a k-fault tolerant Fly-by-wire(FBW) system, an ecient scheduling algorithm has been developed to schedule dierent tasks of a FBW system, here ’r’ denotes the number of rounds. The proposed algorithm for scheduling the task graphs of a multi-rate FBW system demonstrates that, maximization in microcontroller’s execution period reduces the number of microcontrollers needed for performing diagnosis

Fault propagation timing analysis to aid in the selection of sensors fro health management systems

Sensor data is processed to assess performance and health of complex systems. Proper sensor selection, placement, and implementation are critical to build an effective health management system. For complex systems in which the timely assessment of the health is desired to avoid expensive consequences of failure, sensor placement is vital. The ability to identify a critical failure early is completely dependent on sensor location within the fault propagation path. A strategy for assessing a sensor suite with respect to timely critical failure detection is presented in this thesis. To illustrate the strategy, Fault Propagation Timing Analysis (FPTA) will be performed on the Rocketdyne RS-68 rocket engine --Abstract, page iii

Methods and Systems for Fault Diagnosis in Nuclear Power Plants

This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed. A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model. A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system. A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF. For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system. To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP. The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research. In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified

Distributed Self Fault Diagnosis in Wireless Sensor Networks using Statistical Methods

Wireless sensor networks (WSNs) are widely used in various real life applications where the sensor nodes are randomly deployed in hostile, human inaccessible and adversarial environments. One major research focus in wireless sensor networks in the past decades has been to diagnose the sensor nodes to identify their fault status. This helps to provide continuous service of the network despite the occurrence of failure due to environmental conditions. Some of the burning issues related to fault diagnosis in wireless sensor networks have been addressed in this thesis mainly focusing on improvement of diagnostic accuracy, reduction of communication overhead and latency, and robustness to erroneous data by using statistical methods. All the proposed algorithms are evaluated analytically and implemented in standard network simulator NS3 (version 3.19). A distributed self fault diagnosis algorithm using neighbor coordination (DSFDNC) is proposed to identify both hard and soft faulty sensor nodes in wireless sensor networks. The algorithm is distributed (runs in each sensor node), self diagnosable (each node identifies its fault status) and can diagnose the most common faults like stuck at zero, stuck at one, random data and hard faults. In this algorithm, each sensor node gathered the observed data from the neighbors and computes the mean to check the presence of faulty sensor node. If a node diagnoses a faulty sensor node in the neighbors, then it compares observed data with the data of the neighbors and predicts its probable fault status. The final fault status is determined by diffusing the fault information obtained from the neighbors. The accuracy and completeness of the algorithm are verified based on the statistical analysis over sensors data. The performance parameters such as diagnosis accuracy, false alarm rate, false positive rate, total number of message exchanges, energy consumption, network life time, and diagnosis latency of the DSFDNC algorithm are determined for different fault probabilities and average degrees and compared with existing distributed fault diagnosis algorithms. To enhance the diagnosis accuracy, another self fault diagnosis algorithm is proposed based on hypothesis testing (DSFDHT) using the neighbor coordination approach. The Newman-Pearson hypothesis test is used to diagnose the soft fault status of each sensor node along with the neighbors. The algorithm can diagnose the faulty sensor node when the average degree of the network is less. The diagnosis accuracy, false alarm rate and false positive rate performance of the DSFDHT algorithm are improved over DSFDNC for sparse wireless sensor networks by keeping other performance parameters nearly same. The classical methods for fault finding using mean, median, majority voting and hypothesis testing are not suitable for large scale wireless sensor networks due to large devi- ation in transmitted data by faulty sensor nodes. Therefore, a modified three sigma edit test based self fault diagnosis algorithm (DSFD3SET) is proposed which diagnoses in an efficient manner over a large scale wireless sensor networks. The diagnosis accuracy, false alarm rate, and false positive rate of the proposed algorithm improve as compared to that of the DSFDNC and DSFDHT algorithms. The algorithm enhances the total number of message exchanges, energy consumption, network life time, and diagnosis latency, because the proposed algorithm needs less number of message exchanges over the algorithms such as DSFDNC and DSFDHT. In the DSFDNC, DSFDHT and DSFD3SET algorithms, the faulty sensor nodes are considered as soft faulty nodes which behave permanently. However in wireless sensor networks, the sensor nodes behave either fault free or faulty during different periods of time and are considered as intermittent faulty sensor nodes. Diagnosing intermittent faulty sensor nodes in wireless sensor networks is a challenging problem, because of inconsistent result patterns generated by the sensor nodes. The traditional distributed fault diagnosis (DIFD) algorithms consume more message exchanges to obtain the global fault status of the network. To optimize the number of message exchanges over the network, a self fault diagnosis algorithm is proposed here, which repeatedly conducts the self fault diagnosis procedure based on the modified three sigma edit test over a duration to identify the intermittent faulty sensor nodes. The algorithm needs less number of iterations to identify the intermittent faulty sensor nodes. The simulation results show that, the performance of the HISFD3SET algorithm improves in diagnosis accuracy, false alarm rate and false positive rate over the DIFD algorith

Fault diagnosis of hybrid systems with applications to gas turbine engines

Stringent reliability and maintainability requirements for modern complex systems demand the development of systematic methods for fault detection and isolation. Many of such complex systems can be modeled as hybrid automata. In this thesis, a novel framework for fault diagnosis of hybrid automata is presented. Generally, in a hybrid system, two types of sensors may be available, namely: continuous sensors supplying continuous-time readings (i.e., real numbers) and threshold sensitive (discrete) sensors supplying discrete outputs (e.g., level high and pressure low). It is assumed that a bank of residual generators (detection filters) designed based on the continuous model of the plant is available. In the proposed framework, each residual generator is modeled by a Discrete-Event System (DES). Then, these DES models are integrated with the DES model of the hybrid system to build an Extended DES model. A "hybrid" diagnoser is then constructed based on the extended DES model. The "hybrid" diagnoser effectively combines the readings of discrete sensors and the information supplied by residual generators (which is based on continuous sensors) to determine the health status of the hybrid system. The problem of diagnosability of failure modes in hybrid automata is also studied here. A notion of failure diagnosability in hybrid automata is introduced and it is shown that for the diagnosability of a failure mode in a hybrid automaton, it is sufficient that the failure mode be diagnosable in the extended DES model developed for representing the hybrid automaton and residual generators. The diagnosability of failure modes in the case that some residual generators produce unreliable outputs in the form of false alarm or false silence signals is also investigated. Moreover, the problem of isolator (residual generator) selection is examined and approaches are developed for computing a minimal set of isolators to ensure the diagnosability of failure modes. The proposed hybrid diagnosis approach is employed for investigating faults in the fuel supply system and the nozzle actuator of a single-spool turbojet engine with an afterburner. A hybrid automaton model is obtained for the engine. A bank of residual generators is also designed, and an extended DES is constructed for the engine. Based on the extended DES model, a hybrid diagnoser is constructed and developed. The faults diagnosable by a purely DES diagnoser or by methods based on residual generators alone are also diagnosable by the hybrid diagnoser. Moreover, we have shown that there are faults (or groups of faults) in the fuel supply system and the nozzle actuator that can be isolated neither by a purely DES diagnoser nor by methods based on residual generators alone. However, these faults (or groups of faults) can be isolated if the hybrid diagnoser is used

Space station automation of common module power management and distribution, volume 2

The new Space Station Module Power Management and Distribution System (SSM/PMAD) testbed automation system is described. The subjects discussed include testbed 120 volt dc star bus configuration and operation, SSM/PMAD automation system architecture, fault recovery and management expert system (FRAMES) rules english representation, the SSM/PMAD user interface, and the SSM/PMAD future direction. Several appendices are presented and include the following: SSM/PMAD interface user manual version 1.0, SSM/PMAD lowest level processor (LLP) reference, SSM/PMAD technical reference version 1.0, SSM/PMAD LLP visual control logic representation's (VCLR's), SSM/PMAD LLP/FRAMES interface control document (ICD) , and SSM/PMAD LLP switchgear interface controller (SIC) ICD

Riverscape properties contribute to the origin and structure of a hybrid zone in a Neotropical freshwater fish

Understanding the structure of hybrid zones provides valuable insights about species boundaries and speciation, such as the evolution of barriers to gene flow and the strength of selection. In river networks, studying evolutionary processes in hybrid zones can be especially challenging, given the influence of past and current river properties along with biological species- specific traits. Here, we suggest that a natural hybrid zone between two divergent lineages of the sexually dimorphic Neotropical fish Nematocharax venustus was probably established by secondary contact as a result of a river capture event between the Contas and Pardo river basins. This putative river capture is supported by hydrogeological evidence of elbows of capture, wind gaps and geological faults. The morphological (colour pattern) and genetic (mtDNA and RADseq) variation reveal a clinal transition between parental lineages along the main river, with predominance of F2 hybrids at the centre of the hybrid zone, absence of early generation backcrosses and different levels of hybridization in the tributaries. We highlight that different sources of information are crucial for understanding how the riverscape spatial history influences the connectivity between and within river systems and, consequently, the dynamics of gene flow between freshwater lineages/species.River networks are spatially and temporally dynamic environments that impose challenges to the study of hybrid zones. Here, we elucidate the morphological and genetic structure of a hybrid zone generated by secondary contact between lineages of a Neotropical fish genus and discuss the possible role of past and current river properties (including river captures) in its origin and structure.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163541/3/jeb13689_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163541/2/jeb13689-sup-0001-AppendixS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163541/1/jeb13689.pd