Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “prognostic algorithms”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Distilling the Verification Process for Prognostics Algorithms

The goal of prognostics and health management (PHM) systems is to ensure system safety, and reduce downtime and maintenance costs. It is important that a PHM system is verified and validated before it can be successfully deployed. Prognostics algorithms are integral parts of PHM systems. This paper investigates a systematic process of verification of such prognostics algorithms. To this end, first, this paper distinguishes between technology maturation and product development. Then, the paper describes the verification process for a prognostics algorithm as it moves up to higher maturity levels. This process is shown to be an iterative process where verification activities are interleaved with validation activities at each maturation level. In this work, we adopt the concept of technology readiness levels (TRLs) to represent the different maturity levels of a prognostics algorithm. It is shown that at each TRL, the verification of a prognostics algorithm depends on verifying the different components of the algorithm according to the requirements laid out by the PHM system that adopts this prognostics algorithm. Finally, using simplified examples, the systematic process for verifying a prognostics algorithm is demonstrated as the prognostics algorithm moves up TRLs.

verification↗
NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

The field of prognostics and health management provides quantitative methods for monitoring and predicting the health of physical systems. Prognostics algorithms are useful in that they can be employed to assess the current state of a system, propagate the system state throughout time, and predict potential anomalies or failures that may occur. However, effective prognosis can be challenging to achieve in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for some prognostics applications. In this work, we implement three new algorithmic approaches to prediction (sampling methods, variable prediction time step, variable prediction sample size) with the goal of improving computational efficiency while minimizing decrease in model accuracy. To quantitatively analyze our results, we examine a use-case of degradation of a Lithium-ion battery. Notably, through this work it was found that none of the sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. However, our results show that prediction accuracy is highly dependent on the time step used, and that an appropriate time step can optimize both model accuracy and simulation efficiency. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment in an application-specific manner may prove useful in some applications. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency of prognosis.

Prognostics↗
NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

In engineering and aerospace applications, it is vital to operational success to have insight into the expected performance and health of physical systems. The field of prognostics and health management provides quantitative methods for monitoring, predicting, and managing system health. Prognostics algorithms can be employed to assess the current state of a system, propagate the system throughout time, and predict potential anomalies or failures that may occur. While they can provide accurate prediction results, effective prognostics algorithms can be challenging to use in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for future remaining useful life predictions. In this work, we implement new algorithmic approaches for prediction, quantitatively compare them via a battery degradation use-case, and provide recommendations of potential improvements to a prognostics framework. One approach to prediction is through sampling, whereby the current state of a physical system is sampled many times and each sample is propagated forward until failure is reached, resulting in a distribution of failure values. To improve the efficiency of this process, we implemented five new algorithmic approaches to prediction, including three distinct sampling methods (standard Monte Carlo, Quasi-Monte Carlo, and Latin Hypercube Sampling), a variable time step algorithm, and a variable sample size algorithm. To compare the algorithms, we employ a variety of metrics designed specifically to analyze both computational efficiency and model accuracy. Our metrics include accuracy to compare the average predicted value to ground truth, mean absolute deviation to illustrate dispersion, specific percentile error to describe accuracy within a user-defined risk tolerance, and code run-time. To quantitatively analyze our results, we employ a use-case of degradation of a Lithium-ion battery. We use an electrochemistry-based model to describe the current health state of the battery, and implement our prediction algorithms to propagate forward in time until end-of-discharge (EOD) is reached. Notably, through this work it was found that none of our sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. We find that while the sampling methods are unique, the distributions they generate are similar, ultimately producing final predictions that are nearly identical. In exploring the effect of the time step within the prediction algorithm, we found that prediction accuracy was highly dependent on the time step used, and that implementing a variable time step within a particular prediction may provide an increase in computational efficiency while also maintaining prediction accuracy. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment can result in improved computation speed and maintained prediction accuracy. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency.

prognostics↗
NASA NTRS2012

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

Celaya, Jose R.↗
NASA NTRS2012

Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

Galvan, Jose Ramon↗
NASA NTRS2005

Real-Time Simulation for Verification and Validation of Diagnostic and Prognostic Algorithms

To verify that a health management system (HMS) performs as expected, a virtual system simulation capability, including interaction with the associated platform or vehicle, very likely will need to be developed. The rationale for developing this capability is discussed and includes the limited capability to seed faults into the actual target system due to the risk of potential damage to high value hardware. The capability envisioned would accurately reproduce the propagation of a fault or failure as observed by sensors located at strategic locations on and around the target system and would also accurately reproduce the control system and vehicle response. In this way, HMS operation can be exercised over a broad range of conditions to verify that it meets requirements for accurate, timely response to actual faults with adequate margin against false and missed detections. An overview is also presented of a real-time rocket propulsion health management system laboratory which is available for future rocket engine programs. The health management elements and approaches of this lab are directly applicable for future space systems. In this paper the various components are discussed and the general fault detection, diagnosis, isolation and the response (FDIR) concept is presented. Additionally, the complexities of V&V (Verification and Validation) for advanced algorithms and the simulation capabilities required to meet the changing state-of-the-art in HMS are discussed.

Aguilar, Robet↗
NASA NTRS2020

Verification of Prognostic Algorithms to Predict Remaining Flying Time for Electric Unmanned Vehicles

This paper addresses the problem of building trust in the online prediction of a eUAV’s remaining available flying time powered by lithium-ion polymer batteries. A series of ground tests are described that make use of an electric unmanned aerial vehicle (eUAV) to verify the performance of remaining flying time predictions. The algorithm verification procedure described is implemented on a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure (charge depletion) experiments. The vehicle under test is commanded to follow a predefined propeller RPM profile in order to create battery demand profiles similar to those expected during flight. The eUAV is repeatedly operated until the charge stored in powertrain batteries falls below a specified limit threshold. The time at which the limit threshold on battery charge is crossed is then used to measure the accuracy of the remaining flying time prediction. In our earlier work battery aging was not included. In this work we take into account aging of the batteries where the parameters were updated to make predictions. Accuracy requirements are considered for an alarm that warns operators when remaining flying time is estimated to fall below the specified limit threshold.

Edward F. Hogge↗
NASA NTRS2014

Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

Benchmarking of prognostic algorithms has been challenging due to limited availability of common datasets suitable for prognostics. In an attempt to alleviate this problem several benchmarking datasets have been collected by NASA's prognostic center of excellence and made available to the Prognostics and Health Management (PHM) community to allow evaluation and comparison of prognostics algorithms. Among those datasets are five C-MAPSS datasets that have been extremely popular due to their unique characteristics making them suitable for prognostics. The C-MAPSS datasets pose several challenges that have been tackled by different methods in the PHM literature. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than 70 publications have used the C-MAPSS datasets for developing data-driven prognostic algorithms. The C-MAPSS datasets are also shown to be well-suited for development of new machine learning and pattern recognition tools for several key preprocessing steps such as feature extraction and selection, failure mode assessment, operating conditions assessment, health status estimation, uncertainty management, and prognostics performance evaluation. This paper summarizes a comprehensive literature review of publications using C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.

Uncertainty↗
NASA NTRS2009

Evaluating Algorithm Performance Metrics Tailored for Prognostics

Prognostics has taken a center stage in Condition Based Maintenance (CBM) where it is desired to estimate Remaining Useful Life (RUL) of the system so that remedial measures may be taken in advance to avoid catastrophic events or unwanted downtimes. Validation of such predictions is an important but difficult proposition and a lack of appropriate evaluation methods renders prognostics meaningless. Evaluation methods currently used in the research community are not standardized and in many cases do not sufficiently assess key performance aspects expected out of a prognostics algorithm. In this paper we introduce several new evaluation metrics tailored for prognostics and show that they can effectively evaluate various algorithms as compared to other conventional metrics. Specifically four algorithms namely; Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Polynomial Regression (PR) are compared. These algorithms vary in complexity and their ability to manage uncertainty around predicted estimates. Results show that the new metrics rank these algorithms in different manner and depending on the requirements and constraints suitable metrics may be chosen. Beyond these results, these metrics offer ideas about how metrics suitable to prognostics may be designed so that the evaluation procedure can be standardized. 1

Saxena, Abhinav↗
NASA NTRS2014

Development and Implementation of a Hardware In-the-Loop Test Bed for Unmanned Aerial Vehicle Control Algorithms

Successful prediction and management of battery life using prognostic algorithms through ground and flight tests is important for performance evaluation of electrical systems. This paper details the design of test beds suitable for replicating loading profiles that would be encountered in deployed electrical systems. The test bed data will be used to develop and validate prognostic algorithms for predicting battery discharge time and battery failure time. Online battery prognostic algorithms will enable health management strategies. The platform used for algorithm demonstration is the EDGE 540T electric unmanned aerial vehicle (UAV). The fully designed test beds developed and detailed in this paper can be used to conduct battery life tests by controlling current and recording voltage and temperature to develop a model that makes a prediction of end-of-charge and end-of-life of the system based on rapid state of health (SOH) assessment.

Battery Testbed↗
NASA NTRS2012

Prognostics of Power Electronics, Methods and Validation Experiments

Abstract Failure of electronic devices is a concern for future electric aircrafts that will see an increase of electronics to drive and control safety-critical equipment throughout the aircraft. As a result, investigation of precursors to failure in electronics and prediction of remaining life of electronic components is of key importance. DC-DC power converters are power electronics systems employed typically as sourcing elements for avionics equipment. Current research efforts in prognostics for these power systems focuses on the identification of failure mechanisms and the development of accelerated aging methodologies and systems to accelerate the aging process of test devices, while continuously measuring key electrical and thermal parameters. Preliminary model-based prognostics algorithms have been developed making use of empirical degradation models and physics-inspired degradation model with focus on key components like electrolytic capacitors and power MOSFETs (metal-oxide-semiconductor-field-effect-transistor). This paper presents current results on the development of validation methods for prognostics algorithms of power electrolytic capacitors. Particularly, in the use of accelerated aging systems for algorithm validation. Validation of prognostics algorithms present difficulties in practice due to the lack of run-to-failure experiments in deployed systems. By using accelerated experiments, we circumvent this problem in order to define initial validation activities.

Kulkarni, Chetan S.↗
NASA NTRS2010

Distributed Prognostic Health Management with Gaussian Process Regression

Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper. we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.

Saha, Sankalita↗
NASA NTRS2020

Experimental Validation of Model-Based Prognostics for Pneumatic Valves

Because valves control many critical operations, they are prime candidates for deployment of prognostic algorithms. But, similar to the situation with most other components, examples of failures experienced in the field are hard to come by. This lack of data impacts the ability to test and validate prognostic algorithms. A solution sometimes employed to overcome this shortcoming is to perform run-to-failure experiments in a lab. However, the mean time to failure of valves is typically very high (possibly lasting decades), preventing evaluation within a reasonable time frame. Therefore, a mechanism to observe development of fault signatures considerably faster is sought. Described here is a testbed that addresses these issues by allowing the physical injection of leakage faults (which are the most common fault mode) into pneumatic valves. What makes this testbed stand out is the ability to modulate the magnitude of the fault almost arbitrarily fast. With that, the performance of end-of-life estimation algorithms can be tested. Further, the testbed is mobile and can be connected to valves in the field. This mobility helps to bring the overall process of prognostic algorithm development for this valve a step closer to validation. The paper illustrates the development of a model-based prognostic approach that uses data from the testbed for partial validation.

Chetan S Kulkarni↗
NASA NTRS2018

Validation of Model-Based Prognostics for Pneumatic Valves in a Cryogenic Fueling Demonstration Testbed

Because valves control many critical operations, they are prime candidates for deployment of prognostic algorithms. But, similar to the situation with most other components, examples of failures experienced in the field are hard to come by. This lack of data impacts the ability to test and validate prognostic algorithms. A solution sometimes employed to overcome this shortcoming is to perform run to failure experiments in a lab. However, the mean time to failure of valves is typically very high (possibly lasting decades), preventing evaluation within a reasonable time frame. Therefore, a mechanism to observe development of fault signatures considerably faster is sought. Described here is a testbed that addresses these issues by allowing the physical injection of leakage faults (which are the most common fault mode) into pneumatic valves. What makes this testbed stand out is the ability to modulate the magnitude of the fault almost arbitrarily fast. With that, the performance of end-of-life estimation algorithms can be tested. Further, the testbed is mobile and can be connected to valves in the field. This mobility helps to bring the overall process of prognostic algorithm development for this valve a step closer to validation. The paper illustrates the development of a model-based prognostic approach that uses data from the testbed for partial validation.

Kulkarni, Chetan S.↗
NASA NTRS2011

Accelerated Aging with Electrical Overstress and Prognostics for Power MOSFETs

Power electronics play an increasingly important role in energy applications as part of their power converter circuits. Understanding the behavior of these devices, especially their failure modes as they age with nominal usage or sudden fault development is critical in ensuring efficiency. In this paper, a prognostics based health management of power MOSFETs undergoing accelerated aging through electrical overstress at the gate area is presented. Details of the accelerated aging methodology, modeling of the degradation process of the device and prognostics algorithm for prediction of the future state of health of the device are presented. Experiments with multiple devices demonstrate the performance of the model and the prognostics algorithm as well as the scope of application. Index Terms Power MOSFET, accelerated aging, prognostics

Saha, Sankalita↗
NASA NTRSDate not supplied

Predicting Thermal Response in a Li-ion Cell on a UAV Fight Profile

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, a thermal runaway event can propagate from a single "failed" cell to the pack in a battery pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure. Battery prognostics algorithms allow the prediction of state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle) [1]. For this presentation, we will extend this two-level battery predictive algorithm to predict SOC, EOL, and estimated maximum temperature during a simulated flight. The model is extended by integrating a lumped physics-driven thermal model for high current densities [2]. The parameters used to control SOC and EOL are maximum storable charge, time constant for Li-ion diffusivity in the carbon particles, and internal cell resistance. Cycling leads to an increase in the heat generated by an aged Li-ion cell with a LiyCoO2 (LCO) cathode and a LixC6 (MCMB) anode. The aging of a cell leads to increase in SEI layer thickness, the diffusion time for the lithium ions inside the electrodes, and the local reaction rates, in addition to the thermodynamic abuse caused by fixed cycling voltages controlled by a Battery Management System. As the battery ages, the cell resistance increases, while the onset temperature of the thermal runaway decreases (depends on the cell chemistry and cell abuse history). Any large deviation of the cell temperature from the estimated (expected) value can identify a faulty cell. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition as onset temperature (similar to Ref. [3]). The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [4,5]. Coupling the electrochemical, thermal, and aging model allow the prognostic algorithm to estimate a typical cell voltage and temperature as a function of age (cycling and calendar), whose departure from measured values from the BMS is used to identify a safety event. In addition, we will present the results from two simulated flight scenarios for a UAV: typical and extreme, since the power requirements vary significantly during take-off, landing, and changing altitudes, while the power requirements remain low during the cruise. For this presentation, the power requirement for a battery pack in a UAV is scaled to a single cell. This cell is cycled through a simulated profile, and the data is collected and used to predict a safety event.

Li-ion↗
NASA NTRS2011

Multiple Damage Progression Paths in Model-Based Prognostics

Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active

Daigle, Matthew↗