Efficient diagnosis of multiprocessor systems under probabilistic models

The problem of fault diagnosis in multiprocessor systems is considered under a probabilistic fault model. The focus is on minimizing the number of tests that must be conducted in order to correctly diagnose the state of every processor in the system with high probability. A diagnosis algorithm that can correctly diagnose the state of every processor with probability approaching one in a class of systems performing slightly greater than a linear number of tests is presented. A nearly matching lower bound on the number of tests required to achieve correct diagnosis in arbitrary systems is also proven. Lower and upper bounds on the number of tests required for regular systems are also presented. A class of regular systems which includes hypercubes is shown to be correctly diagnosable with high probability. In all cases, the number of tests required under this probabilistic model is shown to be significantly less than under a bounded-size fault set model. Because the number of tests that must be conducted is a measure of the diagnosis overhead, these results represent a dramatic improvement in the performance of system-level diagnosis techniques

The dizzy patient: A review of etiology, differential diagnosis and management

Introduction: Dizziness is a lay term used to describe a variety of sensations. Unfortunately, the term dizziness does not have a precise medical definition, so additional information is typically required to further define the patient\u27s problem. Classifications: When dizziness is a presenting complaint, distinctions must be made between vertigo (a sense of false movement), near-syncope (a feeling of impending faint), disequilibrium (loss of balance), and ill-defined lightheadedness (an inability to concentrate or focus the mind, e.g. , a dazed feeling). Etiologies: Possible causes of dizziness include conflicts between visual and vestibular information, vascular problems, medication adverse reactions, psychological difficulties, systemic disease, and the effects of aging. Management: Dizziness is a symptom of a physiological or psychological illness, therefore management is typically directed toward treatment of the underlying illness. However, in some cases the cause of the dizziness cannot be found or is untreatable. In these cases, management is directed toward symptom reduction. Summary: Dizziness is a relatively common problem that can arise from a variety of causes. In many cases, optometrists can participate in the diagnosis and management patients with complaints of dizziness

Replay-based Recovery for Autonomous Robotic Vehicles from Sensor Deception Attacks

Sensors are crucial for autonomous operation in robotic vehicles (RV). Physical attacks on sensors such as sensor tampering or spoofing can feed erroneous values to RVs through physical channels, which results in mission failures. In this paper, we present DeLorean, a comprehensive diagnosis and recovery framework for securing autonomous RVs from physical attacks. We consider a strong form of physical attack called sensor deception attacks (SDAs), in which the adversary targets multiple sensors of different types simultaneously (even including all sensors). Under SDAs, DeLorean inspects the attack induced errors, identifies the targeted sensors, and prevents the erroneous sensor inputs from being used in RV's feedback control loop. DeLorean replays historic state information in the feedback control loop and recovers the RV from attacks. Our evaluation on four real and two simulated RVs shows that DeLorean can recover RVs from different attacks, and ensure mission success in 94% of the cases (on average), without any crashes. DeLorean incurs low performance, memory and battery overheads

Electrical and magnetic faults diagnosis in permanent magnet synchronous motors

Permanent magnet synchronous motors (PMSMs) are an alternative in critical applications where high-speed operation, compactness and high efficiency are required. In these applications it is highly desired to dispose of an on-line, reliable and cost-effective fault diagnosis method. Fault prediction and diagnosis allows increasing electric machines performance and raising their lifespan, thus reducing maintenance costs, while ensuring optimum reliability, safe operation and timely maintenance. Consequently this thesis is dedicated to the diagnosis of magnetic and electrical faults in PMSMs. As a first step, the behavior of a healthy machine is studied, and with this aim a new 2D finite element method (FEM) modelbased system for analyzing surface-mounted PSMSs with skewed rotor magnets is proposed. It is based on generating a geometric equivalent non-skewed permanent magnet distribution which accounts for the skewed distribution of the practical rotor, thus avoiding 3D geometries and greatly reducing the computational burden of the problem. To diagnose demagnetization faults, this thesis proposes an on-line methodology based on monitoring the zero-sequence voltage component (ZSVC). Attributes of the proposed method include simplicity, very low computational burden and high sensibility when compared with the well known stator currents analysis method. A simple expression of the ZSVC is deduced, which can be used as a fault indicator parameter. Furthermore, mechanical effects arising from demagnetization faults are studied. These effects are analyzed by means of FEM simulations and experimental tests based on direct measurements of the shaft trajectory through self-mixing interferometry. For that purpose two perpendicular laser diodes are used to measure displacements in both X and Y axes. Laser measurements proved that demagnetization faults may induce a quantifiable deviation of the rotor trajectory. In the case of electrical faults, this thesis studies the effects of resistive unbalance and stator winding inter-turn short-circuits in PMSMs and compares two methods for detecting and discriminating both faults. These methods are based on monitoring and analyzing the third harmonic component of the stator currents and the first harmonic of the ZSVC. Finally, the Vold-Kalman filtering order tracking algorithm is introduced and applied to extract selected harmonics related to magnetic and electrical faults when the machine operates under variable speed and different load levels. Furthermore, different fault indicators are proposed and their behavior is validated by means of experimental data. Both simulation and experimental results show the potential of the proposed methods to provide helpful and reliable data to carry out a simultaneous diagnosis of resistive unbalance and stator winding inter-turn faults