Jason R. Kolodziej

Trajectory determination for energy efficient autonomous soaring

Unmanned Aerial Gliders (UAG) use atmospheric energy in its different forms to remain aloft for extended flight durations. This UAGs aim is to extract atmospheric thermal energy and use it to supplement its battery energy usage and increase the mission period. Given an identified atmospheric thermal of known strength and location; current wind speed and direction; battery level; altitude and location of the UAG; and estimating the expected altitude gain from the thermal, is it possible to make an energy-efficient based motivation to fly to an atmospheric thermal so as to achieve UAG extended flight time? For this work it is assumed that candidate atmospheric thermal locations are of known longitude/latitude location, size, and strength. An algorithm, based on a fuzzy logic approach, is then developed to incorporate all available information with the current UAG status to provide an energy-based recommendation to modify the flight path from the nominal mission trajectory. Research, development, and simulation of the decision-making algorithm is the primary focus of this work. Three models are developed: Battery Usage Model (BUM), Altitude Gain Model (AGM), and Intelligent Decision Model (IDM).

A novel approach to model determination using the minimum model error estimation

The purpose of this paper is to present an algorithm for the combination of a proven nonlinear system identification technique, the minimum model error estimation algorithm (MME) with an analysis of variance (ANOVA) correlation routine where a forward stepwise procedure is implemented. The analysis of variance approach to model identification is well documented primarily in social science literature but has been sparsely written about for engineering applications. This paper shows a significant improvement in nonlinear model identification when used in conjunction with the MME algorithm.

Bearing Fault Detection in Electromechanical Actuators from Empirically Extracted Features

This paper proposes an approach to detecting bearing faults in electromechanical actuators (EMAs) using features extracted from experimental data. The method of feature extraction proposed uses established parameter estimation techniques based on system identification followed by an orthogonal transformation of estimated parameters to derive the required features. A Bayesian classifier is then used to create health classes from the extracted features. The performance of the approach is tested using both data obtained from simulations of bearing faults in a permanent magnet DC motor system as well as data recorded from a Moog MaxForce EMA. The approach shows a misclassification performance of 10% when tested with 50 different data sets generated via simulations. Marginally inferior performance is observed when using 40 different data sets collected from the Moog MaxForce EMA. The conclusion is that bearing fault detection in EMAs is possible via the proposed approach, although further refinements are required.

An Aerospace Themed Design Experience for Middle School Girls Interested in STEM Careers

In today’s society, women are drastically underrepresented in engineering. In an attempt to remedy this situation, the Rochester Institute of Technology’s Women in Engineering (WE@RIT) program has developed a week long summer program that is intended to spark the interest of adolescent girls in engineering. For the past three years an aerospace focused project has taken place at this program. In 2012 the program was offered the week of July 9th with 40 girls who participated in WE@RIT’s Everyday Engineering day camp that focused on the design, fabrication, and test of various forms of model rockets. Through in-flight measurement, post-flight group analysis, and subsequent redesign, an engineering design objective was successfully demonstrated. An exit survey was given to all the participants and according to the data collected, more than a third of the participants said they were now considering aerospace engineering as a possible career.

A Data Driven Frequency Based Feature Extraction and Classification Method for EMA Fault Detection and Isolation

This research investigates a novel data driven approach to condition monitoring of Electro-Mechanical Actuators (EMAs) consisting of feature extraction and fault classification. The approach is able to accommodate time-varying loads and speeds since EMA’s typically operate under non-steady conditions. The feature extraction process exposes fault frequencies in signal data that are synchronous with motor position through a series of signal processing techniques. A resulting reduced dimension feature is then used to determine the condition of the EMA with a trained Bayesian Classifier. Signal data collected from EMAs in known health configurations is used to train the algorithms so that the condition of EMA’s with unknown health may be predicted. Although the process was developed for EMAs, it can be used generically on other rotating machine applications as a Health and Usage Management System (HUMS) tool.Copyright

Scale Aircraft Fabrication Laboratory to Increase Undergraduate Student Interest in Aerospace Engineering

According to the U.S. Bureau of Labor Statistics the job outlook for aerospace engineers is expected to be strong in the near term. Additionally, the employment forecast is likely to increase significantly in the future to cover the large retirement expected from a generation of Cold War aerospace scientist and engineers. While much of aerospace engineering requires graduate level study, it is important to excite engineering students, especially in the early part of their education, in the principles of aerospace engineering in a fun, creative, and engaging way. An elective course developed at RIT targets this early engineering demographic by seeking to give the students a hands-on laboratory in the fundamentals of scale-sized aircraft fabrication and test. The goal is to inspire students who either have not decided upon an engineering concentration or are looking to increase their awareness and understanding in a much needed field as they plan their future careers.

An image-based pattern recognition approach to condition monitoring of reciprocating compressor valves:

This work presents the development of a vibration-based condition monitoring method for early detection and classification of valve wear within industrial reciprocating compressors through the combined use of time-frequency analysis with image-based pattern recognition techniques. Two common valve related fault conditions are spring fatigue and valve seat wear and are seeded on the crank-side discharge valves of a Dresser-Rand ESH-1 industrial compressor. Operational data including vibration, cylinder pressure, and crank shaft position are collected and processed using a transformed time-frequency domain approach. The results are processed as images with features extracted using 1st and 2nd order image texture statistics and binary shape properties. Feature reduction is accomplished by principal component analysis and a Bayesian classification strategy is employed with accuracy rates greater than 90%.

Seeded Fault Testing and Classification of Dynamically Loaded Floating Ring Compressor Bearings

This paper investigates a variety of signal-monitoring and data-driven processing techniques to classify seed faults imposed on floating ring main crankshaft compressor bearings. Simulated main bea...

Design and Analysis of a Scale-Sized Electromechanical Actuator for Unsteady Condition Monitoring Applications

Growing interest in using Electromechanical Actuators to replace current hydraulic actuation methods on aircraft control surfaces has driven significant research in the area of prognostics and health management. Non-stationary speeds and loads in the course of controlling an aircraft surface make fault identification in EMAs difficult. This work presents a time-frequency analysis of EMA thrust bearing vibration signals using wavelet transforms. A lab sized EMA system is designed and fabricated to allow for quick and repeatable component replacement. Indentation faults from moderate and heavy loads are seeded in the thrust bearings and are then tested to generate data. An artificial neural network achieves 95% classification accuracy in a two class scenario using healthy and moderately spalled thrust bearings.Copyright

A Validated System-Level Thermodynamic Model of a Reciprocating Compressor With Application to Valve Condition Monitoring

Condition-based health monitoring systems are a very important addition to machinery to monitor the system and assure it is running at the peak efficiency, to schedule maintenance, and prevent catastrophic failure. Recently, these systems have become more common on industrial compression technology. Reciprocating compressor health monitoring systems typically use only indirect measurements, P-V diagrams, to monitor the system’s health.Specifically, this research focuses on three different valve failure modes that are common in reciprocating compressors: liquid slugging; valve spring fatigue; and valve seat wear. First, a system-level model of a Dresser-Rand industrial reciprocating compressor is derived and validated, experimentally, to better understand how different subsystem dynamics are related through the compressor. Also, a preliminary instrument investigation is conducted to determine what sensor types are the most effective at detecting these faults.© 2013 ASME