A Fast Monte Carlo Method for Model-Based Prognostics Based on Stochastic Calculus

This work proposes a fast Monte Carlo method to solve differential equations utilized in model-based prognostics. The methodology is derived from the theory of stochastic calculus, and the goal of such a method is to speed up the estimation of the probability density functions describing the independent variable evolution over time. In the prognostic scenarios presented in this paper, the stochastic differential equations describe variables directly or indirectly related to the degradation of a monitored system. The method allows the estimation of the probability density functions by solving the deterministic equation and approximating the stochastic integrals using samples of the model noise. By so doing, the prognostic problem is solved without the Monte Carlo simulation based on Euler's forward method, which is typically the most time consuming task of the prediction stage. Three different prognostic scenarios are presented as proof of concept: (i) life prediction of electrolytic capacitors, (ii) remaining time to discharge of Lithium-ion batteries, and (iii) prognostic of cracked structures under fatigue loading. The paper shows how the method produces probability density functions that are statistically indistinguishable from the distributions estimated with Euler's forward Monte Carlo simulation. However, the proposed solution is orders of magnitude faster when computing the time-to-failure distribution of the monitored system. The approach may enable complex real-time prognostics and health management solutions with limited computing power

Design and Development of a Gyro Machine for Industrial Applications

Gyroscopes have generated considerable research interest leading to number of commercial applications. In the present investigation, a new approach to build the mechanical gyroscope with a hollow spherical container as a spinning mass is proposed for industrial applications such as washing machine, mixing ceramic powders or different granular materials, in mixing a variety of chemicals or paints. The main objective of this research is to rotate the spherical container about all the three axes using gyroscopic principles. In this study, a hollow sphere was mounted on the inner gimbal, and this sub-assembly was further mounted on the outer gimbal. Based on the degrees of freedom concept, the dynamic mechanism was developed using Pro-Engineering software. Based on the results obtained from the dynamic mechanism the micro-controller was developed to automate the gyro machine. Magnetic field was introduced to operate the system (to spin the spherical container as well as to rotate the outer gimbal with just single input, using DC motor) based on gimbal lock concept. Springs were used to energize the sphere after equal intervals by changing the rotational direction of outer gimbal in the interval of 15 to 20 seconds. The experimentation was carried out to analyze the relative motion between sphere, inner gimbal, and outer gimbal using high-speed photography technique. It is shown that the relative motions due to gyroscopic effect leads to rotation and oscillations of sphere about all the three axes. Based on these relative motions different cycles were developed.Mechanical & Aerospace Engineerin

End-of-Discharge and End-of-Life Prediction in Lithium-Ion Batteries with Electrochemistry-Based Aging Models

As batteries become increasingly prevalent in complex systems such as aircraft and electric cars, monitoring and predicting battery state of charge and state of health becomes critical. In order to accurately predict the remaining battery power to support system operations for informed operational decision-making, age-dependent changes in dynamics must be accounted for. Using an electrochemistry-based model, we investigate how key parameters of the battery change as aging occurs, and develop models to describe aging through these key parameters. Using these models, we demonstrate how we can (i) accurately predict end-of-discharge for aged batteries, and (ii) predict the end-of-life of a battery as a function of anticipated usage. The approach is validated through an experimental set of randomized discharge profiles

A Battery Health Monitoring Framework for Planetary Rovers

Batteries have seen an increased use in electric ground and air vehicles for commercial, military, and space applications as the primary energy source. An important aspect of using batteries in such contexts is battery health monitoring. Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. For planetary rovers, battery health estimation and prediction is critical to mission planning and decision-making. We develop a model-based approach utilizing computaitonally efficient and accurate electrochemistry models of batteries. An unscented Kalman filter yields state estimates, which are then used to predict the future behavior of the batteries and, specifically, end of discharge. The prediction algorithm accounts for possible future power demands on the rover batteries in order to provide meaningful results and an accurate representation of prediction uncertainty. The framework is demonstrated on a set of lithium-ion batteries powering a rover at NASA

Diagnostic Reasoning using Prognostic Information for Unmanned Aerial Systems

With increasing popularity of unmanned aircraft, continuous monitoring of their systems, software, and health status is becoming more and more important to ensure safe, correct, and efficient operation and fulfillment of missions. The paper presents integration of prognosis models and prognostic information with the R2U2 (REALIZABLE, RESPONSIVE, and UNOBTRUSIVE Unit) monitoring and diagnosis framework. This integration makes available statistically reliable health information predictions of the future at a much earlier time to enable autonomous decision making. The prognostic information can be used in the R2U2 model to improve diagnostic accuracy and enable decisions to be made at the present time to deal with events in the future. This will be an advancement over the current state of the art, where temporal logic observers can only do such valuation at the end of the time interval. Usefulness and effectiveness of this integrated diagnostics and prognostics framework was demonstrated using simulation experiments with the NASA Dragon Eye electric unmanned aircraft

Effects of Aircraft Health on Airspace Safety

This manuscript investigates the effects of aircraft health on the surrounding airspace, and proposes a methodology to understand how different aircraft-level faults (system faults, communication faults, etc.) can adversely affect the safety of the airspace, and qualitatively assess the impact of such faults on airspace safety metrics (such as congestion, controller/pilot workload, etc.). The topic of systems health management deals with continuously monitoring the performance of an engineering system, identifying and detecting the presence of faults, predicting the growth/progression of faults, computing the remaining useful life, and aiding online decision-making for the robust, continued operation of such engineering systems. The topic of real-time airspace modeling and safety analysis deals with defining and computing safety metrics for airspace operations in order to support risk-informed decision-making activities for various airspace entities including pilots, air traffic controllers, airlines, etc. This report presents recent research efforts that focus on combining multiple aspects of the aforementioned topics, and investigates the impact of aircraft-level faults on the airspace safet

Model Based Diagnostics and Prognostics Framework for Systems Health Management

In order to tackle and solve the system health prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. This presentation will cover a physics based-modeling approach implemented for case-studies in battery and composite structures for prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making

Application of Model-based Prognostics to a Pneumatic Valves Testbed

Pneumatic-actuated valves play an important role in many applications, including cryogenic propellant loading for space operations. Model-based prognostics emphasizes the importance of a model that describes the nominal and faulty behavior of a system, and how faulty behavior progresses in time, causing the end of useful life of the system. We describe the construction of a testbed consisting of a pneumatic valve that allows the injection of faulty behavior and controllable fault progression. The valve opens discretely, and is controlled through a solenoid valve. Controllable leaks of pneumatic gas in the testbed are introduced through proportional valves, allowing the testing and validation of prognostics algorithms for pneumatic valves. A new valve prognostics approach is developed that estimates fault progression and predicts remaining life based only on valve timing measurements. Simulation experiments demonstrate and validate the approach

A Study of the Degradation of Electronic Speed Controllers for Brushless DC Motors

Brushless DC motors are frequently used in electric aircraft and other direct drive applications. As these motors are notactually direct current machines but synchronous alternating current machines; they are electronically commutated by a power inverter. The power inverter for brushless DC motors typically used in small scale UAVs is a semiconductor base delectronic commutator that is external to the motor and is referred to as an electronic speed control (ESC). This paper examines the performance changes of a UAV electric propulsion system resulting from ESC degradation. ESC performance is evaluated in simulation and on a new developed test bed featuring propulsion components from a reference UAV. An increase in the rise fall times of the switched voltages is expected to cause timing issues at high motor speeds. This study paves the way for further development of diagnostic and prognostic methods for inverter circuits which are part of the overall electric UAV system

Remaining Flying Time Prediction Implementing Battery Prognostics Framework for Electric UAV's

In this paper the problem of building trust in the online safety prediction of an fixed wing small electric unmanned aerial vehicles (e-UAV) for remaining flying time is addressed. A series of flight tests are described to verify the performance of the remaining flying time prediction algorithm. The estimate of remaining flying time is used to activate an alarm when the predicted remaining time falls below a threshold of two minutes. This updates the pilot to transition to the landing sequence of the flight profile. A second alarm is activated when the battery state of charge (SOC) falls below a specified safety limit threshold. This SOC threshold is the point at which the battery energy reserve would no longer safely support enough aborted landing attempts. During the test flights, the motor system is operated with the same predefined timed airspeed profile for each test. To test the robustness of the developed prediction algorithm, partial tests were performed with and remaining were performed without a simulated power train fault. To simulate a partial power train fault in the e-UAV the pilot engages a resistor bank at a specified time during the test flight. The flying time prediction system is agnostic of the pilot's activation of the fault and must adapt to the vehicle's state. The time at which the limit threshold on battery SOC is reached, it is then used to measure the accuracy of the remaining flying time predictions. This is demonstrated through comparing results from two battery models being developed. Accuracy requirements for the alarms are considered and the results discussed