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Methods for Probabilistic Fault Diagnosis: An EPS 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. **Reference:** B. W. Ricks and O. J. Mengshoel, "Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study." In Proc. of the First Annual Conference of the Prognostics and Health Management Society (PHM-09), San Diego, CA, September 27 – October 1, 2009. **BibTex Reference:** @inproceedings{ricks09methods, author = {Ricks, B. W. and Mengshoel, O. J.}, title = {Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study}, booktitle = {Proc. of the Annual Conference of the Prognostics and Health Management Society (PHM-09)}, address = {San Diego, CA}, month = sep, year = {2009} }

Greener Aviation with Virtual Sensors: A Case Study

The environmental impact of aviation is enormous given the fact that in the US alone there are nearly 6 million flights per year of commercial aircraft. This situation has driven numerous policy and procedural measures to help develop environmentally friendly technologies which are safe and affordable and reduce the environmental impact of aviation. However, many of these technologies require significant initial investment in newer aircraft fleets and modifications to existing regulations which are both long and costly enterprises. We propose to use an anomaly detection method based on Virtual Sensors to help detect overconsumption of fuel in aircraft which relies only on the data recorded during flight of most existing commercial aircraft, thus significantly reducing the cost and complexity of implementing this method. The Virtual Sensors developed here are ensemble-learning regression models for detecting the overconsumption of fuel based on instantaneous measurements of the aircraft state. This approach requires no additional information about standard operating procedures or other encoded domain knowledge. We present experimental results on three data sets and compare five different Virtual Sensors algorithms. The first two data sets are publicly available and consist of a simulated data set from a flight simulator and a real-world turbine disk.We show the ability to detect anomalies with high accuracy on these data sets. These sets contain seeded faults, meaning that they have been deliberately injected into the system. The second data set is from realworld fleet of 84 jet aircraft where we show the ability to detect fuel overconsumption which can have a significant environmental and economic impact. To the best of our knowledge, this is the first study of its kind in the aviation domain.

Towards A Model-based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging

A remaining useful life prediction methodology for elec- trolytic capacitors is presented. This methodology adopts a Kalman filter approach in conjunction with an empirical state-based degradation model to predict the degradation of capacitor parameters through the life of the capacitor. Elec- trolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are good candi- dates for component level prognostics and health manage- ment. Prognostics provides a way to assess remaining use- ful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. This paper discusses the empirical degradation models and experi- mental results of an accelerated aging test performed on a set of identical capacitors subjected to electrical stress. The data form the basis for developing the Kalman-filter based remain- ing life prediction algorithm.

PADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY

PADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY TUSHAR MAHULE*, KIRK BORNE**, SANDIPAN DEY*, SUGANDHA ARORA*, AND HILLOL KARGUPTA*** Abstract. Peer-to-Peer (P2P) networks are appealing for astronomy data mining from virtual observatories because of the large volume of the data, compute-intensive tasks, potentially large number of users, and distributed nature of the data analysis process. This paper offers a brief overview of PADMINI—a Peer-to-Peer Astronomy Data MINIng system. It also presents a case study on PADMINI for distributed outlier detection using astronomy data. PADMINI is a webbased system powered by Google Sky and distributed data mining algorithms that run on a collection of computing nodes. This paper offers a case study of the PADMINI evaluating the architecture and the performance of the overall system. Detailed experimental results are presented in order to document the utility and scalability of the system.

Towards A Model-Based Prognostics Methodology For Electrolytic Capacitors: A Case Study Based On Electrical Overstress Accelerated Aging

This paper presents a model-driven methodology for predict- ing the remaining useful life of electrolytic capacitors. This methodology adopts a Kalman filter approach in conjunction with an empirical state-based degradation model to predict the degradation of capacitor parameters through the life of the capacitor. Electrolytic capacitors are important components of systems that range from power supplies on critical avion- ics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their critical role in the system, they are good candidates for component level prognostics and health management. Prognostics provides a way to assess remain- ing useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. This paper proposes and empirical degradation model and discusses experimental results for an accelerated aging test performed on a set of identical capacitors subjected to electrical stress. The data forms the basis for developing the Kalman-filter based remaining life prediction algorithm.

restrictednotspecifiedApr 2025View details →

Combining Model-Based and Feature-Driven Diagnosis Approaches – A Case Study on Electromechanical Actuators

Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.

Prognostics for Ground Support Systems: Case Study on Pneumatic Valves

Prognostics technologies determine the health (or damage) state of a component or sub- system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such infor- mation enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics method- ology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the ow of propellant, so failures may have a signi cant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle ltering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.

restrictednotspecifiedApr 2025View details →

Qualitative Event-based Diagnosis with Possible Conflicts: Case Study on the Third International Diagnostic Competition

We describe two model-based diagnosis algo- rithms entered into the Third International Diag- nostic Competition. We focus on the first diag- nostic problem of the industrial track of the com- petition in which a diagnosis algorithm must de- tect, isolate, and identify faults in an electrical power distribution testbed in order to provide cor- rect abort recommendations. Both diagnosis al- gorithms are based on a qualitative event-based fault isolation framework augmented with model- based fault identification. Although based on a common framework, the fundamental difference between the two algorithms is that one is based on a global model for residual generation, fault iso- lation, and fault identification, whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe, compare, and contrast the two algorithms in terms of practical implementation and their diagnosis results.

Space Shuttle Main Propulsion System Anomaly Detection: A Case Study

The space shuttle main engine (SSME) is part of the Main Propnlsion System (MPS) which is an extremely complex system containing several sub-systems and components, each of which must work precisely in order to achieve a successful mission. A critical component under study is the flow control valve (FCV) which controls the pressure of the gaseous hydrogen between the SSME and the external fuel tank. The FCV has received added attention since a Space Shuttle Mission in November 2008, where it was discovered during the mission that an anomaly had occurred in one of the three FCV's. Subsequent inspection revealed that one FCV cracked during ascent. This type of fault is of high criticality because it can lead to potentially catastrophic gaseous hydrogen leakage. A supervised learning method known as Virtual Sensors (VS), and an unsupervised learning method known as the Inductive Monitoring System (IMS) were used to detect anomalies related to the FCV in the MPS. Both algorithms identify the time of the anomaly in a multi-dimensional time series of temperatures, pressures, and control signals related to the FCV. This discovery corroborates the results of the inspection and also reveals the time at which the anomaly likely occurred. The methods were applied to data obtained from the March 2009 launch of Space Shuttle Discovery to determine whether an anomaly occurred in the same sub-system. According to our models, the FCV SUb-system showed nominal behavior during ascent.

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequences of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods.

openGEO-OpenDec 2021View details →

The validation case study of read-across based on IATA for hepatotoxicity of Naphthalene and its structurally similar analogs

GEO Series GSE156442. Rattus norvegicus. 15 samples. Type: Expression profiling by array.

openGEO-OpenMay 2016View details →

Prior Knowledge-based Approach for Associating Emerging Contaminants with Effects in Fish Exposed In Situ: A Case Study in the St. Croix River Basin, MN, WI, USA.

GEO Series GSE81263. Pimephales promelas. 40 samples. Type: Expression profiling by array.

openGEO-OpenDec 2014View details →

Determinants of excess genetic risk of acute myocardial infarction – a matched case-control study

GEO Series GSE34198. Homo sapiens. 97 samples. Type: Expression profiling by array.

openGEO-OpenNov 2022View details →

Comparison of toxicological effects and exposure levels between triclosan and its structurally similar chemicals using in vitro tests for read-across case study

GEO Series GSE186659. Rattus norvegicus. 12 samples. Type: Expression profiling by array.

openGEO-OpenJul 2018View details →

The value of circulating microRNAs for early diagnosis of B-cell lymphoma: A case-control study on historical samples

GEO Series GSE117063. Homo sapiens. 31 samples. Type: Expression profiling by RT-PCR.

restrictedFeb 2020View details →

Introduction to curating and managing research data for re-use: natural gas infrastructure case study data set

This data set underpins the report and data provided between 2004-2006 for registration in the restrictions application of Estonian Land Board.

restrictedOct 2020View details →

Drug-interaction annotations over full text articles reporting on clinical studies reports or case reports

Drug interaction annotations over full text reports. These annotations were done as part of the National Library of Medicine funded research project "Addressing gaps in clinically useful evidence on drug-drug interactions" (R01LM011838)

restrictedcc-by-4.0Nov 2023View details →

Inside the origin of a Floor fractured crater: case study in Gorgonum Chaos Basins, Mars

The file contain the map of an unnamed floor-fractured crater located in Gorgonum Basin, Terra Sirenum, Mars.