The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions

Diagnosing intermittent and persistent faults using static Bayesian networks

ABSTRACT Both intermittent and persistent faults may occur in a wide range of systems. We present in this paper the introduction of intermittent fault handling techniques into ProDiagnose, an algorithm that previously only handled persistent faults. We discuss novel algorithmic techniques as well as how our static Bayesian networks help diagnose, in an integrated manner, a range of intermittent and persistent faults. Through experiments with data from the ADAPT electrical power system test bed, generated as part of the Second International Diagnostic Competition (DXC-10), we show that this novel variant of ProDiagnose diagnoses intermittent faults accurately and quickly, while maintaining strong performance on persistent faults

The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and < 1 second mean diagnostic time

Anodic Passivation of Tin by Alkanethiol Self-Assembled Monolayers Examined by Cyclic Voltammetry and Coulometry

The self-assembly of medium chain length alkanethiol monolayers on polycrystalline Sn electrodes has been investigated by cyclic voltammetry and coulometry. These studies have been performed in order to ascertain the conditions under which their oxidative deposition can be achieved directly on the oxide-free Sn surface, and the extent to which these electrochemically prepared self-assembled monolayers (SAMs) act as barriers to surface oxide growth. This work has shown that the potentials for their oxidative deposition are more cathodic (by 100–200 mV) than those for Sn surface oxidation and that the passivating abilities of these SAMs improve with increasing film thickness (or chain length). Oxidative desorption potentials for these films have been observed to shift more positively, and in a highly linear fashion, with increasing film thickness (∼75 mV/CH₂). Although reductive desorption potentials for the SAMs are in close proximity to those for reduction of the surface oxide (SnO<i>x</i>), little or no SnO<i>x</i> formation occurs unless the potential is made sufficiently anodic that the monolayers start to be removed oxidatively. Our coulometric data indicate that the charge involved with alkanethiol reductive desorption or oxidative deposition is consistent with the formation of a close-packed monolayer, given uncertainties attributable to surface roughness and heterogeneity phenomena. These experiments also reveal that the quantity of charge passed during oxidative desorption is significantly larger than what would be predicted for simple alkylsulfinate or alkylsulfonate formation, suggesting that oxidative removal involves a more complex oxidation mechanism. Analogous chronocoulometric experiments for short-chain alkanethiols on polycrystalline Au electrodes have evidenced similar oxidative charge densities. This implies that the mechanism for oxidative desorption on both surfaces may be very similar, despite the significant differences in the inherent dissolution characteristics of the two materials at the anodic potentials employed

Exposure Route Influences Disease Severity in the COVID-19 Cynomolgus Macaque Model

The emergence of SARS-CoV-2 and the subsequent pandemic has highlighted the need for animal models that faithfully replicate the salient features of COVID-19 disease in humans. These models are necessary for the rapid selection, testing, and evaluation of potential medical countermeasures. Here, we performed a direct comparison of two distinct routes of SARS-CoV-2 exposure—combined intratracheal/intranasal and small particle aerosol—in two nonhuman primate species, rhesus and cynomolgus macaques. While all four experimental groups displayed very few outward clinical signs, evidence of mild to moderate respiratory disease was present on radiographs and at necropsy. Cynomolgus macaques exposed via the aerosol route also developed the most consistent fever responses and had the most severe respiratory disease and pathology. This study demonstrates that while all four models produced suitable representations of mild COVID-like illness, aerosol exposure of cynomolgus macaques to SARS-CoV-2 produced the most severe disease, which may provide additional clinical endpoints for evaluating therapeutics and vaccines

Development of a coronavirus disease 2019 nonhuman primate model using airborne exposure.

Airborne transmission is predicted to be a prevalent route of human exposure with SARS-CoV-2. Aside from African green monkeys, nonhuman primate models that replicate airborne transmission of SARS-CoV-2 have not been investigated. A comparative evaluation of COVID-19 in African green monkeys, rhesus macaques, and cynomolgus macaques following airborne exposure to SARS-CoV-2 was performed to determine critical disease parameters associated with disease progression, and establish correlations between primate and human COVID-19. Respiratory abnormalities and viral shedding were noted for all animals, indicating successful infection. Cynomolgus macaques developed fever, and thrombocytopenia was measured for African green monkeys and rhesus macaques. Type II pneumocyte hyperplasia and alveolar fibrosis were more frequently observed in lung tissue from cynomolgus macaques and African green monkeys. The data indicate that, in addition to African green monkeys, macaques can be successfully infected by airborne SARS-CoV-2, providing viable macaque natural transmission models for medical countermeasure evaluation