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  • 1.
    Bektas, Oguz
    et al.
    Warwick Manufacturing Group, University of Warwick, Coventry, UK.
    Jones, Jeffrey A.
    Warwick Manufacturing Group, University of Warwick, Coventry, UK.
    Sankararaman, Shankar
    Data Science and Analytics Manager,Pricewaterhouse Cooper, San Jose, USA.
    Roychoudhury, Indranil
    Stinger Ghaffarian Technologies, Inc.NASA Ames Research Center, Moffett Field, USA.
    Goebel, Kai
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. NASA Ames Research Center, Moffett Field, USA.
    A neural network filtering approach for similarity-based remaining useful life estimation2019Ingår i: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 101, nr 1-4, s. 87-103Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The role of prognostics and health management is ever more prevalent with advanced techniques of estimation methods. However, data processing and remaining useful life prediction algorithms are often very different. Some difficulties in accurate prediction can be tackled by redefining raw data parameters into more meaningful and comprehensive health level indicators that will then provide performance information. Proper data processing has a significant importance on remaining useful life predictions, for example, to deal with data limitations or/and multi-regime operating conditions. The framework proposed in this paper considers a similarity-based prognostic algorithm that is fed by the use of data normalisation and filtering methods for operational trajectories of complex systems. This is combined with a data-driven prognostic technique based on feed-forward neural networks with multi-regime normalisation. In particular, the paper takes a close look at how pre-processing methods affect algorithm performance. The work presented herein shows a conceptual prognostic framework that overcomes challenges presented by short-term test datasets and that increases the prediction performance with regards to prognostic metrics.

  • 2.
    Bektas, Oguz
    et al.
    Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom.
    Jones, Jeffrey A.
    Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom.
    Sankararaman, Shankar
    Pricewaterhouse Cooper, San Jose, CA, United States.
    Roychoudhury, Indranil
    Stinger Ghaffarian Technologies, Inc., NASA Ames Research Center, Moffett Field, CA, United States.
    Goebel, Kai
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. NASA Ames Research Center, Moffett Field, CA, United States.
    A neural network framework for similarity-based prognostics2019Ingår i: MethodsX, ISSN 1258-780X, E-ISSN 2215-0161, Vol. 6, s. 383-390Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Prognostic performance is associated with accurately estimating remaining useful life. Difficulty in accurate prognostic applications can be tackled by processing raw sensor readings into more meaningful and comprehensive health condition indicators that will then provide performance information for remaining useful life estimations. To that end, typically, multiple tasks on data pre-processing and predictions have to be carried out such that tasks can be assessed using different methodological aspects. However, incompatible methods may result in poor performance and consequently lead to undesirable error rates.

    The present research evaluates data training and prediction stages. A data-driven prognostic method based on a feed-forward neural network framework is first defined to calculate the performance of a complex system. Then, the health indicators are used in a similarity based remaining useful life estimation method. This framework presents a conceptual prognostic protocol that overcomes challenges presented by multi-regime condition monitoring data.

  • 3.
    Bektas, Oguz
    et al.
    Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, United Kingdom.
    Jones, Jeffrey A.
    Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, United Kingdom.
    Sankararaman, Shankar
    PricewaterhouseCoopers, San Jose, CA 95110, United States.
    Roychoudhury, Indranil
    Stinger Ghaffarian Technologies, Inc., NASA Ames Research Center, Moffett Field, CA 94035, United States.
    Goebel, Kai
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. NASA Ames Research Center, Moffett Field, CA 94035, United States.
    Reconstructing secondary test database from PHM08 challenge data set2018Ingår i: Data in Brief, E-ISSN 2352-3409, Vol. 21, s. 2464-2469Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this data article, a reconstructed database, which provides information from PHM08 challenge data set, is presented. The original turbofan engine data were from the Prognostic Center of Excellence (PCoE) of NASA Ames Research Center (Saxena and Goebel, 2008), and were simulated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) (Saxena et al., 2008). The data set is further divided into "training", "test" and "final test" subsets. It is expected from collaborators to train their models using “training” data subset, evaluate the Remaining Useful Life (RUL) prediction performance on “test” subset and finally, apply the models to the “final test” subset for competition. However, the "final test" results can only be submitted once by email to PCoE. Before the results are sent for performance evaluation, in order to pre-validate the dataset with true RUL values, this data article introduces reconstructed secondary datasets derived from the noisy degradation patterns of original trajectories. Reconstructed database refers to data that were collected from the training trajectories. Fundamentally, it is formed of individual partial trajectories in which the RUL is known as a ground truth. Its use provides a robust validation of the model developed for the PHM08 data challenge that would otherwise be ambiguous due to the high-risk of one-time submission. These data and analyses support the research data article “A Neural Network Filtering Approach for Similarity-Based Remaining Useful Life Estimations” (Bektas et al., 2018).

  • 4.
    Hulse, Daniel
    et al.
    School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA.
    Hoyle, Christopher
    School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA.
    Goebel, Kai
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Discovery and Systems Health, Intelligent Systems Division, NASA Ames Research Center, Moffett Field, California, USA.
    Tumer, Irem Y.
    School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA.
    Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment2019Ingår i: Journal of mechanical design (1990), ISSN 1050-0472, E-ISSN 1528-9001, Vol. 141, nr 2, artikel-id MD-18-1503Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Complex engineered systems can carry risk of high failure consequences, and as a result, resilience—the ability to avoid or quickly recover from faults—is desirable. Ideally, resilience should be designed-in as early in the design process as possible so that designers can best leverage the ability to explore the design space. Toward this end, previous work has developed functional modeling languages which represent the functions which must be performed by a system and function-based fault modeling frameworks have been developed to predict the resulting fault propagation behavior of a given functional model. However, little has been done to formally optimize or compare designs based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. The work described herein closes this gap by introducing the resilience-informed scenario cost sum (RISCS), a scoring function which integrates with a fault scenario-based simulation, to enable the optimization and evaluation of functional model resilience. The scoring function accomplishes this by quantifying the expected cost of a design's fault response using probability information, and combining this cost with design and operational costs such that it may be parameterized in terms of designer-specified resilient features. The usefulness and limitations of using this approach in a general optimization and concept selection framework are discussed in general, and demonstrated on a monopropellant system design problem. Using RISCS as an objective for optimization, the algorithm selects the set of resilient features which provides the optimal trade-off between design cost and risk. For concept selection, RISCS is used to judge whether resilient concept variants justify their design costs and make direct comparisons between different model structures.

  • 5.
    Li, Z.
    et al.
    Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, United States.
    Goebel, Kai
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. NASA Ames Research Center, Moffett Field, United States.
    Wu, D.
    Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, United States.
    Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning2019Ingår i: Journal of engineering for gas turbines and power, ISSN 0742-4795, E-ISSN 1528-8919, Vol. 41, nr 4, artikel-id 041008Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Degradation modeling and prediction of remaining useful life (RUL) are crucial to prognostics and health management of aircraft engines. While model-based methods have been introduced to predict the RUL of aircraft engines, little research has been reported on estimating the RUL of aircraft engines using novel data-driven predictive modeling methods. The objective of this study is to introduce an ensemble learning-based prognostic approach to modeling an exponential degradation process due to wear as well as predicting the RUL of aircraft engines. The ensemble learning algorithm combines multiple base learners, including random forests (RFs), classification and regression tree (CART), recurrent neural networks (RNN), autoregressive (AR) model, adaptive network-based fuzzy inference system (ANFIS), relevance vector machine (RVM), and elastic net (EN), to achieve better predictive performance. The particle swarm optimization (PSO) and sequential quadratic optimization (SQP) methods are used to determine optimum weights that are assigned to the base learners. The predictive model trained by the ensemble learning algorithm is demonstrated on the data generated by the commercial modular aero-propulsion system simulation (C-MAPSS) tool. Experimental results have shown that the ensemble learning algorithm predicts the RUL of the aircraft engines with considerable robustness as well as outperforms other prognostic methods reported in the literature. 

  • 6.
    Matelli, José Alexandre
    et al.
    NASA Ames Research Center, Intelligent Systems Division, Discovery and Systems Health, Moffett Field, CA.
    Goebel, Kai
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. NASA Ames Research Center, Intelligent Systems Division, Discovery and Systems Health.
    Conceptual design of cogeneration plants under a resilient design perspective: Resilience metrics and case study2018Ingår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 215, s. 736-750Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The conceptual design phase is the first step in the design process of an engineering system. Most engineering systems, including cogeneration plants, may and likely will experience some malfunctions during its life cycle. The metrics typically considered in the conceptual design phase (and for analysis and optimization) of energy systems are cost, efficiency and environmental impacts. Quite rarely are operational considerations about malfunctions integrated during the conceptual design phase. Resilient design, or design for resilience, addresses this gap as illustrated here in the area of energy conversion and conservation of energy processes by examining the conceptual design of a cogeneration plant. Resilient design is a relatively new research field where the engineering system is designed such that it can optimally recover from failures. The main challenge is to quantify the resilience in early design phases, since there is not much detailed information about system components available at this point. To address these challenges, this paper introduces a novel resilient design framework that uses new metrics within a Monte Carlo-based assessment approach. The framework is exercised on conceptual designs of cogeneration plants. Results from this framework are compared against those from a methodology based on complex networks theory that has been previously suggested in the literature. The former presented more consistent results than the latter and we discuss the differences. Results also show that the concept with higher efficiency was not the one with higher resilience. Finally, we discuss how to integrate specific failure probabilities information into the framework (should that information be available), and deliberate on relations between resilience, fault handling strategies and design requirements.

  • 7.
    Matelli, José Alexandre
    et al.
    São Paulo State University (UNESP), School of Engineering, Department of Energy, Av. Ariberto Pereira da Cunha, Guaratinguetá, SP, Brazil.
    Goebel, Kai
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. NASA Ames Research Center, Intelligent Systems Division, Discovery and Systems Health, Moffett Field, CA.
    Resilience evaluation of the environmental control and life support system of a spacecraft for deep space travel2018Ingår i: Acta Astronomica, ISSN 0001-5237, Vol. 152, s. 360-369Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In deep space manned travels, the crew life will be totally dependent on the environment control and life support system of the spacecraft. A life-support system for manned missions is a set of technologies to regenerate the basic life-support elements, such as oxygen and water, which makes resilience a paramount feature of this system. The resilience of a complex engineered system is the ability of the system to withstand failures, continue operating and recover from those failures with minimum disruption. Resilient design is a new design framework on which the main goal is to quantify system resilience upfront in order to guide the design team during the conceptual design stage. In this article, we present a tool that combines a rule-based approach with a Monte Carlo-based approach to evaluate the resilience of a proposed environment control and life support system designed for deep space travel. Based on the results found, we explore a few design alternatives in order to increase system resilience.

  • 8.
    Sierra, G.
    et al.
    Department of Electrical Engineering, University of Chile, Santiago.
    Orchard, M.
    Department of Electrical Engineering, University of Chile, Santiago, Chile.
    Goebel, Kai
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. NASA Ames Research Center, Moffett Field, CA, USA.
    Kulkarni, C.
    SGT Inc., NASA Ames Research Center, Moffett Field, CA, USA.
    Battery Health Management for Small-size Battery-powered Rotary-wing Unmanned Aerial Vehicles: An Efficient Approach for Constrained Computing Platforms2019Ingår i: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 182, s. 166-178Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This article presents a holistic framework for the design, implementation and experimental validation of Battery Management Systems (BMS) in rotatory-wing Unmanned Aerial Vehicles (UAVs) that allows to accurately (i) estimate the State of Charge (SOC), and (ii) predict the End of Discharge (EOD) time of lithium-polymer batteries in small-size multirotors by using a model-based prognosis architecture that is efficient and feasible to implement in low-cost hardware. The proposed framework includes a simplified battery model that incorporates the electric load dependence, temperature dependence and SOC dependence by using the concept of Artificial Evolution to estimate some of its parameters, along with a novel Outer Feedback Correction Loop (OFCL) during the estimation stage which adjusts the variance of the process noise to diminish bias in Bayesian state estimation and helps to compensate problems associated with incorrect initial conditions in a non-observable dynamic system. Also, it provides an aerodynamic-based characterization of future power consumption profiles. A quadrotor has been used as validation platform. The results of this work will allow making decisions about the flight plan and having enough confidence in those decisions so that the mission objectives can be optimally achieved.

  • 9.
    Zhang, Xiaoge
    et al.
    Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, USA.
    Mahadevan, Sankaran
    Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, USA..
    Goebel, Kai
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Systems Sciences Lab, Palo Alto Research Center, Palo Alto, CA, USA.
    Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events2019Ingår i: Risk Analysis, ISSN 0272-4332, E-ISSN 1539-6924Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Evacuating residents out of affected areas is an important strategy for mitigating the impact of natural disasters. However, the resulting abrupt increase in the travel demand during evacuation causes severe congestions across the transportation system, which thereby interrupts other commuters' regular activities. In this article, a bilevel mathematical optimization model is formulated to address this issue, and our research objective is to maximize the transportation system resilience and restore its performance through two network reconfiguration schemes: contraflow (also referred to as lane reversal) and crossing elimination at intersections. Mathematical models are developed to represent the two reconfiguration schemes and characterize the interactions between traffic operators and passengers. Specifically, traffic operators act as leaders to determine the optimal system reconfiguration to minimize the total travel time for all the users (both evacuees and regular commuters), while passengers act as followers by freely choosing the path with the minimum travel time, which eventually converges to a user equilibrium state. For each given network reconfiguration, the lower-level problem is formulated as a traffic assignment problem (TAP) where each user tries to minimize his/her own travel time. To tackle the lower-level optimization problem, a gradient projection method is leveraged to shift the flow from other nonshortest paths to the shortest path between each origin-destination pair, eventually converging to the user equilibrium traffic assignment. The upper-level problem is formulated as a constrained discrete optimization problem, and a probabilistic solution discovery algorithm is used to obtain the near-optimal solution. Two numerical examples are used to demonstrate the effectiveness of the proposed method in restoring the traffic system performance.

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