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Ensemble Data Mining Methods
Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better... -
Pseudo-Label Generation for Multi-Label Text Classification
With the advent and expansion of social networking, the amount of generated text data has seen a sharp increase. In order to handle such a huge volume of text data, new and... -
Qualitative Event-based Diagnosis with Possible Conflicts Applied to...
Model-based diagnosis enables efficient and safe operation of engineered systems. In this paper, we describe two algorithms based on a qualitative event-based fault isolation... -
Anomaly Detection in Sequences
We present a set of novel algorithms which we call sequenceMiner, that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise from... -
DXC'10 Industrial Track Sample Data
Sample data, including nominal and faulty scenarios, for Diagnostic Problems I and II of the Second International Diagnostic Competition. Three file formats are provided, tab-... -
sequenceMiner algorithm
Detecting and describing anomalies in large repositories of discrete symbol sequences. sequenceMiner has been open-sourced! Download the file below to try it out. sequenceMiner... -
Model-Based Fault Detection and Diagnosis System for NASA Mars Subsurface...
The Drilling Automation for Mars Environment (DAME) project, led by NASA Ames Research Center, is aimed at developing a lightweight, low-power drill prototype that can be... -
MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING
MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING MOHAMMAD SALIM AHMED, LATIFUR KHAN, NIKUNJ OZA, AND MANDAVA RAJESWARI Abstract. There has been... -
A Comparison of Three Data-driven Techniques for Prognostics
In situations where the cost/benefit analysis of using physics-based damage propagation algorithms is not favorable and when sufficient test data are available that map out the... -
A Local Scalable Distributed Expectation Maximization Algorithm for Large...
This paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P)... -
Fault Tolerance, Diagnostics, and Prognostics in Aircraft Flight
Abstract In modern fighter aircraft with statically unstable airframe designs, the flight control system is considered flight critical, i.e. the aircraft will encounter a... -
Orca
Orca is a data-driven, unsupervised anomaly detection algorithm that uses a distance-based approach. It uses a novel pruning rule that allows it to run in nearly linear time.... -
Flight Data For Tail 668
The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following: -sensor... -
Aviation Safety 2009 IVHM Presentations
IVHM Presentations -
Prognostic Techniques for Capacitor Degradation and Health Monitoring
This paper discusses our initial efforts in constructing physics of failure models for electrolytic capacitors subjected to electrical stressors in DC-DC power converters.... -
Discovery of Recurring Anomalies in Text Reports
This paper describes the results of a significant research and development effort conducted at NASA Ames Research Center to develop new text mining algorithms to discover... -
Prognostication of Residual Life and Latent Damage Assessment in Lead-free...
Requirements for system availability for ultra-high reliability electronic systems such as airborne and space electronic systems are driving the need for advanced heath... -
A Reasoning Architecture for Expert Troubleshooting of Complex Processes
This paper introduces a novel reasoning methodology, in combination with appropriate models and measurements (data) to perform accurately and expeditiously expert... -
A Model-based Prognostics Approach Applied to Pneumatic Valves
Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the... -
Mining Distance-Based Outliers in Near Linear Time
Full title: Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule Abstract: Defining outliers by their distance to neighboring examples...