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Goebel, Kai
Publications (8 of 8) Show all publications
Bektas, O., Jones, J. A., Sankararaman, S., Roychoudhury, I. & Goebel, K. (2019). A neural network filtering approach for similarity-based remaining useful life estimation. The International Journal of Advanced Manufacturing Technology, 101(1-4), 87-103
Open this publication in new window or tab >>A neural network filtering approach for similarity-based remaining useful life estimation
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2019 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 101, no 1-4, p. 87-103Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
C-MAPPS datasets, Data-driven prognostics, Neural networks, Similarity-based RUL calculation
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-71863 (URN)10.1007/s00170-018-2874-0 (DOI)000461051300007 ()
Note

Validerad;2019;Nivå 2;2019-04-12 (johcin)

Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-04-12Bibliographically approved
Bektas, O., Jones, J. A., Sankararaman, S., Roychoudhury, I. & Goebel, K. (2019). A neural network framework for similarity-based prognostics. MethodsX, 6, 383-390
Open this publication in new window or tab >>A neural network framework for similarity-based prognostics
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2019 (English)In: MethodsX, ISSN 1258-780X, E-ISSN 2215-0161, Vol. 6, p. 383-390Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Similarity based RUL calculation, Artificial neural networks, Data-driven prognostics
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-73190 (URN)10.1016/j.mex.2019.02.015 (DOI)000493729600043 ()30859074 (PubMedID)2-s2.0-85062036500 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-03-14 (johcin)

Available from: 2019-03-14 Created: 2019-03-14 Last updated: 2019-11-22Bibliographically approved
Li, Z., Goebel, K. & Wu, D. (2019). Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning. Journal of engineering for gas turbines and power, 41(4), Article ID 041008.
Open this publication in new window or tab >>Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning
2019 (English)In: Journal of engineering for gas turbines and power, ISSN 0742-4795, E-ISSN 1528-8919, Vol. 41, no 4, article id 041008Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
ASME Press, 2019
Keywords
remaining useful life prediction, prognostics and health management (PHM), degradation modeling, aircraft engines, ensemble learning
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-71864 (URN)10.1115/1.4041674 (DOI)000462020200008 ()2-s2.0-85056854179 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-12-03 (svasva)

Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-04-05Bibliographically approved
Goebel, K., Smith, B. & Bajawa, A. (2019). Ethics in prognostics and health management. International Journal of Prognostics and Health Management, 10(1), Article ID 012.
Open this publication in new window or tab >>Ethics in prognostics and health management
2019 (English)In: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 10, no 1, article id 012Article in journal (Refereed) Published
Abstract [en]

As we are entering an era where intelligent systems are omnipresent and where they also penetrate Prognostics and Health Management (PHM), the discussion of moral machines or ethics in engineering will inevitably engulf PHM as well. This article explores the topic of ethics within the PHM domain: how it is relevant, and how it may be dealt with in a conscientious way. The paper provides a historical perspective on ethics-related developments that resulted in the formulation of engineering ethics codes, regulations, and policies. By virtue of these developments, ethics has already been encapsulated in PHM systems. The specific areas that have traditionally driven ethics considerations include safety and security, and they increasingly include privacy, and environmental protection. During the course of future technology development, innovations will increasingly impact all of these topics. It is argued that consciously embracing these issues will increase the competitive advantage of a PHM technology solution. As a guideline, specific ethics attributes are derived from professional engineering ethics codes, and a path towards insertion into a requirements flowdown is suggested.

Place, publisher, year, edition, pages
Prognostics and Health Management Society, 2019
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75916 (URN)2-s2.0-85071151160 (Scopus ID)
Note

Validerad;2019;Nivå 1;2019-09-09 (johcin)

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-09Bibliographically approved
Zhang, X., Mahadevan, S. & Goebel, K. (2019). Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events. Risk Analysis, 39(9), 2054-2075
Open this publication in new window or tab >>Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events
2019 (English)In: Risk Analysis, ISSN 0272-4332, E-ISSN 1539-6924, Vol. 39, no 9, p. 2054-2075Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019
Keywords
Network reconfiguration, optimization, resilience, traffic assignment
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-74600 (URN)10.1111/risa.13320 (DOI)000484531200012 ()31039286 (PubMedID)2-s2.0-85065195673 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-10-08 (johcin)

Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-10-08Bibliographically approved
Hulse, D., Hoyle, C., Goebel, K. & Tumer, I. Y. (2019). Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment. Journal of mechanical design (1990), 141(2), Article ID MD-18-1503.
Open this publication in new window or tab >>Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment
2019 (English)In: Journal of mechanical design (1990), ISSN 1050-0472, E-ISSN 1528-9001, Vol. 141, no 2, article id MD-18-1503Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
ASME Press, 2019
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-72829 (URN)10.1115/1.4041571 (DOI)000456049900010 ()2-s2.0-85059064059 (Scopus ID)
Note

Konferensartikel i tidskrift

Available from: 2019-02-08 Created: 2019-02-08 Last updated: 2019-02-08Bibliographically approved
Bektas, O., Jones, J. A., Sankararaman, S., Roychoudhury, I. & Goebel, K. (2018). Reconstructing secondary test database from PHM08 challenge data set. Data in Brief, 21, 2464-2469
Open this publication in new window or tab >>Reconstructing secondary test database from PHM08 challenge data set
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2018 (English)In: Data in Brief, E-ISSN 2352-3409, Vol. 21, p. 2464-2469Article in journal (Refereed) Published
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).

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Commercial modular aero-propulsion system simulation, C-MAPPS datasets, PHM08 challenge data set, Data-driven prognostics
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-71982 (URN)10.1016/j.dib.2018.11.085 (DOI)000457925900336 ()2-s2.0-85057839272 (Scopus ID)
Available from: 2018-12-10 Created: 2018-12-10 Last updated: 2019-03-08Bibliographically approved
Matelli, J. A. & Goebel, K. (2018). Resilience evaluation of the environmental control and life support system of a spacecraft for deep space travel. Acta Astronomica, 152, 360-369
Open this publication in new window or tab >>Resilience evaluation of the environmental control and life support system of a spacecraft for deep space travel
2018 (English)In: Acta Astronomica, ISSN 0001-5237, Vol. 152, p. 360-369Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Resilience, Reliability, Design Optimization, Operations
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-70747 (URN)10.1016/j.actaastro.2018.08.045 (DOI)000449235500038 ()2-s2.0-85052749740 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-11-23 (svasva)

Available from: 2018-09-04 Created: 2018-09-04 Last updated: 2018-11-29Bibliographically approved
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