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A modular electric power system test bed for small spacecraft

In the new climate of smaller, faster, and cheaper space science satellites, a new power system topology has been developed at the NASA Lewis Research Center. This new topology is based on a series connected boost converter (SCBC) and can greatly affect the size, weight, fault tolerance, and cost of any small spacecraft using photovoltaic solar arrays. The paper presents electric power system design factors and requirements as background information. The series connected boost converter topology is discussed and several advantages over existing technologies are illustrated. Besides being small, lightweight, and efficient, this topology has the added benefit of inherent fault tolerance. A positive ground power system test bed has been developed for the TROPIX spacecraft program. Performance of the SCBC in the test bed is described in detail. SCBC efficiencies of 95 percent to 98 percent have been measured. Finally, a modular, photovoltaic regulator 'kit' concept is presented. Two SCBC's are used to regulate solar array charging of batteries and to provide 'utilitytype' power to the user loads. The kit's modularity will allow a spacecraft electric power system to be built from off-the-shelf hardware; resulting in smaller, faster, and cheaper spacecraft.

Button, Robert M.↗

Diagnostic Analyzer for Gearboxes (DAG): User's Guide

This documentation describes the Diagnostic Analyzer for Gearboxes (DAG) software for performing fault diagnosis of gearboxes. First, the user would construct a graphical representation of the gearbox using the gear, bearing, shaft, and sensor tools contained in the DAG software. Next, a set of vibration features obtained by processing the vibration signals recorded from the gearbox using a signal analyzer is required. Given this information, the DAG software uses an unsupervised neural network referred to as the Fault Detection Network (FDN) to identify the occurrence of faults, and a pattern classifier called Single Category-Based Classifier (SCBC) for abnormality scaling of individual vibration features. The abnormality-scaled vibration features are then used as inputs to a Structure-Based Connectionist Network (SBCN) for identifying faults in gearbox subsystems and components. The weights of the SBCN represent its diagnostic knowledge and are derived from the structure of the gearbox graphically presented in DAG. The outputs of SBCN are fault possibility values between 0 and 1 for individual subsystems and components in the gearbox with a 1 representing a definite fault and a 0 representing normality. This manual describes the steps involved in creating the diagnostic gearbox model, along with the options and analysis tools of the DAG software.

Jammu, Vinay B.↗