Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specifically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. (See CASI ID 20100021910 for supplemental data disk.

A Survey of Health Management User Objectives Related to Diagnostic and Prognostic Metrics

One of the most prominent technical challenges to effective deployment of health management systems is the vast difference in user objectives with respect to engineering development. In this paper, a detailed survey on the objectives of different users of health management systems is presented. These user objectives are then mapped to the metrics typically encountered in the development and testing of two main systems health management functions: diagnosis and prognosis. Using this mapping, the gaps between user goals and the metrics associated with diagnostics and prognostics are identified and presented with a collection of lessons learned from previous studies that include both industrial and military aerospace applications

Benchmarking Diagnostic Algorithms on an Electrical Power System Testbed

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model-based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies. In this paper, we present our efforts to benchmark a set of DAs on a common platform using a framework that was developed to evaluate and compare various performance metrics for diagnostic technologies. The diagnosed system is an electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The paper presents the fundamentals of the benchmarking framework, the ADAPT system, description of faults and data sets, the metrics used for evaluation, and an in-depth analysis of benchmarking results obtained from testing ten diagnostic algorithms on the ADAPT electrical power system testbed

A 16-Year Retrospective Study Examining Socio-Demographic Factors among Suicide Decedents in Bolu Province, Northwestern Turkey

This study aimed to examine the socio-demographic features of completed suicides in Bolu province, northwestern Turkey, between 2003 and 2019, using corpse examinations and autopsy reports to provide epidemiological data with a view to helping prevent suicidal behavior. Of the 108 suicides examined, males accounted for 84.3%, and females accounted for 15.7%. The suicide rate was the highest in the 25–34 age groups and among those of an unknown marital status. Suicide in an unknown location in the group aged 24 years and below was significantly higher than among age groups, while the workplace was significantly higher in those aged 35–64 years. In both genders, the home and its surroundings comprised the leading location of the suicide event, with an unknown location found to be significantly higher in males. The most common suicide method was identified as hanging, with no statistically significant difference between demographic groups with respect to method, and the highest suicide rate was observed in 2016–2019. With regard to time of year, winter was found to be significantly higher among those aged 24–34 years, whereas those aged 65 years and above were likelier to die in spring or summer (p=0.014). Several risk factors were identified, most of which could be detected and resolved before the suicidal act. Studies such as this are extremely valuable for their contribution to preventing suicide by identifying such risk factors

First International Diagnosis Competition - DXC'09

A framework to compare and evaluate diagnosis algorithms (DAs) has been created jointly by NASA Ames Research Center and PARC. In this paper, we present the first concrete implementation of this framework as a competition called DXC 09. The goal of this competition was to evaluate and compare DAs in a common platform and to determine a winner based on diagnosis results. 12 DAs (model-based and otherwise) competed in this first year of the competition in 3 tracks that included industrial and synthetic systems. Specifically, the participants provided algorithms that communicated with the run-time architecture to receive scenario data and return diagnostic results. These algorithms were run on extended scenario data sets (different from sample set) to compute a set of pre-defined metrics. A ranking scheme based on weighted metrics was used to declare winners. This paper presents the systems used in DXC 09, description of faults and data sets, a listing of participating DAs, the metrics and results computed from running the DAs, and a superficial analysis of the results

Towards a Framework for Evaluating and Comparing Diagnosis Algorithms

Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics

Assessment of the State of the Art of Integrated Vehicle Health Management Technologies as Applicable to Damage Conditions

A survey of literature from academia, industry, and other Government agencies assessed the state of the art in current integrated vehicle health management (IVHM) aircraft technologies. These are the technologies that are used for assessing vehicle health at the system and subsystem level. This study reports on how these technologies are employed by major military and commercial platforms for detection, diagnosis, prognosis, and mitigation. Over 200 papers from five conferences from the time period of 2004 to 2009 were reviewed. Over 30 of these IVHM technologies are then mapped into the 17 different adverse event damage conditions identified in a previous study. This study illustrates existing gaps and opportunities for additional research by the NASA IVHM Project

Impairment of the left ventricular systolic and diastolic function in patients with non-alcoholic fatty liver disease

Background: Non-alcoholic fatty liver disease (NAFLD) is considered the liver component of the metabolic syndrome. We investigated the diastolic and systolic functional parameters of patients with NAFLD and the impact of metabolic syndrome on these parameters. Methods: Thirty-five non-diabetic, normotensive NAFLD patients, and 30 controls, were included in this study. Each patient underwent transthoracic conventional and tissue Doppler echocardiography (TDI) for the assessment of left ventricular (LV) diastolic and systolic function. Study patients were also evaluated with 24-hour ambulatory blood pressure monitoring. Results: NAFLD patients had higher blood pressures, increased body mass indices, and more insulin resistance than controls. TDI early diastolic velocity (E&#8217; on TDI) values were lower in NAFLD patients than the controls (11.1 &#177; 2.1 vs 15.3 &#177; 2.7; p < 0.001). TDI systolic velocity (S&#8217; on TDI) values were lower in NAFLD patients than the controls (9.34 &#177; 1.79 vs 10.6 &#177; 1.52; p = 0.004). E&#8217; on TDI and S&#8217; on TDI values were moderately correlated with night-systolic blood pressure, night-diastolic blood pressure, and night-mean blood pressure in NAFLD patients. Conclusions: Patients with NAFLD have impaired LV systolic and diastolic function even in the absence of morbid obesity, hypertension, or diabetes. (Cardiol J 2010; 17, 5: 457-463

Causal Factors and Adverse Events of Aviation Accidents and Incidents Related to Integrated Vehicle Health Management

Causal factors in aviation accidents and incidents related to system/component failure/malfunction (SCFM) were examined for Federal Aviation Regulation Parts 121 and 135 operations to establish future requirements for the NASA Aviation Safety Program s Integrated Vehicle Health Management (IVHM) Project. Data analyzed includes National Transportation Safety Board (NSTB) accident data (1988 to 2003), Federal Aviation Administration (FAA) incident data (1988 to 2003), and Aviation Safety Reporting System (ASRS) incident data (1993 to 2008). Failure modes and effects analyses were examined to identify possible modes of SCFM. A table of potential adverse conditions was developed to help evaluate IVHM research technologies. Tables present details of specific SCFM for the incidents and accidents. Of the 370 NTSB accidents affected by SCFM, 48 percent involved the engine or fuel system, and 31 percent involved landing gear or hydraulic failure and malfunctions. A total of 35 percent of all SCFM accidents were caused by improper maintenance. Of the 7732 FAA database incidents affected by SCFM, 33 percent involved landing gear or hydraulics, and 33 percent involved the engine and fuel system. The most frequent SCFM found in ASRS were turbine engine, pressurization system, hydraulic main system, flight management system/flight management computer, and engine. Because the IVHM Project does not address maintenance issues, and landing gear and hydraulic systems accidents are usually not fatal, the focus of research should be those SCFMs that occur in the engine/fuel and flight control/structures systems as well as power systems

Commercial Aircraft Integrated Vehicle Health Management Study

Statistical data and literature from academia, industry, and other government agencies were reviewed and analyzed to establish requirements for fixture work in detection, diagnosis, prognosis, and mitigation for IVHM related hardware and software. Around 15 to 20 percent of commercial aircraft accidents between 1988 and 2003 involved inalftfnctions or failures of some aircraft system or component. Engine and landing gear failures/malfunctions dominate both accidents and incidents. The IVI vl Project research technologies were found to map to the Joint Planning and Development Office's National Research and Development Plan (RDP) as well as the Safety Working Group's National Aviation Safety Strategic. Plan (NASSP). Future directions in Aviation Technology as related to IVHlvl were identified by reviewing papers from three conferences across a five year time span. A total of twenty-one trend groups in propulsion, aeronautics and aircraft categories were compiled. Current and ftiture directions of IVHM related technologies were gathered and classified according to eight categories: measurement and inspection, sensors, sensor management, detection, component and subsystem monitoring, diagnosis, prognosis, and mitigation