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Dr. Enrique Droguett
Department of Mechanical Engineering, University of Chile, 1058 Santiago, Chile

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0 Reliability
0 Risk
0 Uncertainty
0 IA in PHM

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Preprint content
Published: 16 July 2021
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ACS Style

Sergio Cofre-Martel; Enrique Lopez-Droguett; Mohammad Modarres. Uncovering the Underlying Physics of Degrading System Behavior Through a Deep Neural Network Framework: The Case of Rul Prognosis. 2021, 1 .

AMA Style

Sergio Cofre-Martel, Enrique Lopez-Droguett, Mohammad Modarres. Uncovering the Underlying Physics of Degrading System Behavior Through a Deep Neural Network Framework: The Case of Rul Prognosis. . 2021; ():1.

Chicago/Turabian Style

Sergio Cofre-Martel; Enrique Lopez-Droguett; Mohammad Modarres. 2021. "Uncovering the Underlying Physics of Degrading System Behavior Through a Deep Neural Network Framework: The Case of Rul Prognosis." , no. : 1.

Research article
Published: 01 July 2021 in Shock and Vibration
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The vibrational behavior of composite structures has been demonstrated as a useful feature for identifying debonding damage. The precision of the damage localization can be greatly improved by the addition of more measuring points. Therefore, full-field vibration measurements, such as those obtained using high-speed digital image correlation (DIC) techniques, are particularly useful. In this study, deep learning techniques, which have demonstrated excellent performance in image classification and segmentation, are incorporated into a novel approach for assessing damage in composite structures. This article presents a damage-assessment algorithm for composite sandwich structures that uses full-field vibration mode shapes and deep learning. First, the vibration mode shapes are identified using high-speed 3D DIC measurements. Then, Gaussian process regression is implemented to estimate the mode shape curvatures, and a baseline-free gapped smoothing method is applied to compute the damage images. The damage indices, which are represented as grayscale images, are processed using a convolutional-neural-network-based algorithm to automatically identify damaged regions. The proposed methodology is validated using numerical and experimental data from a composite sandwich panel with different damage configurations.

ACS Style

Viviana Meruane; Diego Aichele; Rafael Ruiz; Enrique López Droguett. A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures. Shock and Vibration 2021, 2021, 1 -12.

AMA Style

Viviana Meruane, Diego Aichele, Rafael Ruiz, Enrique López Droguett. A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures. Shock and Vibration. 2021; 2021 ():1-12.

Chicago/Turabian Style

Viviana Meruane; Diego Aichele; Rafael Ruiz; Enrique López Droguett. 2021. "A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures." Shock and Vibration 2021, no. : 1-12.

Research article
Published: 27 May 2021 in Shock and Vibration
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Remaining useful life (RUL) estimation is one of the main objectives of prognostics and health management (PHM) frameworks. For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. Although the state-of-art results have been achieved in benchmark problems, most DL-PHM algorithms are treated as black-box functions, giving little-to-no control over data interpretation. This becomes an issue when the models unknowingly break the governing laws of physics when no constraints are imposed. The latest research efforts have focused on applying complex DL models to achieve low prediction errors rather than studying how they interpret the data’s behavior and the system itself. This paper proposes an open-box approach using a deep neural network framework to explore the physics of a complex system’s degradation through partial differential equations (PDEs). This proposed framework is an attempt to bridge the gap between statistic-based PHM and physics-based PHM. The framework has three stages, and it aims to discover the health state of the system through a latent variable while still providing a RUL estimation. Results show that the latent variable can capture the failure modes of the system. A latent space representation can also be used as a health state estimator through a random forest classifier with up to a 90% performance on new unseen data.

ACS Style

Sergio Cofre-Martel; Enrique Lopez Droguett; Mohammad Modarres. Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables. Shock and Vibration 2021, 2021, 1 -15.

AMA Style

Sergio Cofre-Martel, Enrique Lopez Droguett, Mohammad Modarres. Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables. Shock and Vibration. 2021; 2021 ():1-15.

Chicago/Turabian Style

Sergio Cofre-Martel; Enrique Lopez Droguett; Mohammad Modarres. 2021. "Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables." Shock and Vibration 2021, no. : 1-15.

Journal article
Published: 29 March 2021 in Sustainability
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In this study, GMAW and CMT welding technologies were evaluated in terms of their technological lifecycles based on their patent datasets together with the S-curve concept, and the joints were evaluated in terms of their welding characteristics. To predict the future trends for both technologies, different models based on the time-series and growth-curve methods were tested. From a process point of view, the results showed better performance and stability for the CMT process based on the heat input to the base material and the frequency of the short circuits. The temperature distribution in the sample revealed that the GMAW process delivers higher values and, consequently, greater heat transfer. Regarding the technological lifecycle, the analyses revealed that the CMT welding process, despite being recent, is already in its mature phase. Moreover, the GMAW welding process is positioned in the growth phase on the S-curve, indicating a possibility of advancement. The main findings indicated that through mathematical modelling, it is possible to predict, in a precise way, the inflection points and the maturity phases of each technology and chart their trends with expert opinions. The new perspectives for analysing maturity levels and welding characteristics presented herein will be essential for a broaden decision-making market process.

ACS Style

André Oliveira; Raphael Santos; Bruno Silva; Lilian Guarieiro; Matthias Angerhausen; Uwe Reisgen; Renelson Sampaio; Bruna Machado; Enrique Droguett; Paulo Silva; Rodrigo Coelho. A Detailed Forecast of the Technologies Based on Lifecycle Analysis of GMAW and CMT Welding Processes. Sustainability 2021, 13, 3766 .

AMA Style

André Oliveira, Raphael Santos, Bruno Silva, Lilian Guarieiro, Matthias Angerhausen, Uwe Reisgen, Renelson Sampaio, Bruna Machado, Enrique Droguett, Paulo Silva, Rodrigo Coelho. A Detailed Forecast of the Technologies Based on Lifecycle Analysis of GMAW and CMT Welding Processes. Sustainability. 2021; 13 (7):3766.

Chicago/Turabian Style

André Oliveira; Raphael Santos; Bruno Silva; Lilian Guarieiro; Matthias Angerhausen; Uwe Reisgen; Renelson Sampaio; Bruna Machado; Enrique Droguett; Paulo Silva; Rodrigo Coelho. 2021. "A Detailed Forecast of the Technologies Based on Lifecycle Analysis of GMAW and CMT Welding Processes." Sustainability 13, no. 7: 3766.

Original research article
Published: 09 March 2021 in Risk Analysis
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In the last decade, Bayesian networks (BNs) have been widely used in engineering risk assessment due to the benefits that they provide over other methods. Among these, the most significant is the ability to model systems, causal factors, and their dependencies in a probabilistic manner. This capability has enabled the community to do causal reasoning through associations, which answers questions such as: “How does new evidence x ′ about the occurrence of event X change my belief about the occurrence of event Y ?” Associative reasoning has helped risk analysts to identify relevant risk‐contributing factors and perform scenario analysis by evidence propagation. However, engineering risk assessment has yet to explore other features of BNs, such as the ability to reason through interventions, which enables the BN model to support answering questions of the form “How does doing X = x ′ change my belief about the occurrence of event Y ?” In this article, we propose to expand the scope of use of BN models in engineering risk assessment to support intervention reasoning. This will provide more robust risk‐informed decision support by enabling the modeling of policies and actions before being implemented. To do this, we provide the formal mathematical background and tools to model interventions in BNs and propose a framework that enables its use in engineering risk assessment. This is demonstrated in an illustrative case study on third‐party damage of natural gas pipelines, showing how BNs can be used to inform decision‐makers about the effect that new actions/policies can have on a system.

ACS Style

Andres Ruiz‐Tagle; Enrique Lopez Droguett; Katrina M. Groth. Exploiting the Capabilities of Bayesian Networks for Engineering Risk Assessment: Causal Reasoning through Interventions. Risk Analysis 2021, 1 .

AMA Style

Andres Ruiz‐Tagle, Enrique Lopez Droguett, Katrina M. Groth. Exploiting the Capabilities of Bayesian Networks for Engineering Risk Assessment: Causal Reasoning through Interventions. Risk Analysis. 2021; ():1.

Chicago/Turabian Style

Andres Ruiz‐Tagle; Enrique Lopez Droguett; Katrina M. Groth. 2021. "Exploiting the Capabilities of Bayesian Networks for Engineering Risk Assessment: Causal Reasoning through Interventions." Risk Analysis , no. : 1.

Journal article
Published: 13 January 2021 in Applied Mathematical Modelling
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In this paper, we propose using the q-Weibull distribution to directly model the failure time of a series system composed of dependent components, discuss the connection between the q-Weibull distribution and the Clayton copula, and show that their parameters are equivalent. Moreover, we propose a Nonhomogeneous Poisson Process with q-Weibull as the underlying time to first failure distribution for reliability analysis of a series system composed of dependent components. The proposed model has fewer parameters and is an approximation to the Clayton copula approach. The maximum likelihood estimators are developed for the parameters of the proposed model. Confidence intervals based on the maximum likelihood asymptotic theory are also developed. The results of simulation experiments demonstrate the accuracy of the proposed model; moreover, estimating the parameters of the proposed model does not require information about which components failed, which is necessary for accurately estimating the parameters of the Clayton model. The procedure is applied to a data set of real failure times for a load-haul-dump machine that is characterized by a bathtub-shaped hazard rate.

ACS Style

Meng Xu; Jeffrey W. Herrmann; Enrique Lopez Droguett. Modeling dependent series systems with q-Weibull distribution and Clayton copula. Applied Mathematical Modelling 2021, 94, 117 -138.

AMA Style

Meng Xu, Jeffrey W. Herrmann, Enrique Lopez Droguett. Modeling dependent series systems with q-Weibull distribution and Clayton copula. Applied Mathematical Modelling. 2021; 94 ():117-138.

Chicago/Turabian Style

Meng Xu; Jeffrey W. Herrmann; Enrique Lopez Droguett. 2021. "Modeling dependent series systems with q-Weibull distribution and Clayton copula." Applied Mathematical Modelling 94, no. : 117-138.

Research article
Published: 07 December 2020 in Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
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Due to its capital-intensive nature, the Oil and Gas industry requires high operational standards to meet safety and environmental requirements, while maintaining economical returns. In this context, maintenance policies play a crucial role in the avoidance of unplanned downtimes and enhancement of productivity. In particular, Condition-Based Maintenance is an approach in which maintenance actions are performed depending on the assets’ health state that is evaluated through different kinds of sensors. In this paper, Deep Learning methods are explored and different models are proposed for health state prognostics of physical assets in two real-life cases from the Oil and Gas industry: a Natural Gas treatment plant in an offshore production platform where elevated levels of CO2 must be predicted, and a sea water injection pump for oil extraction stimulation, in which several degradation levels must be predicted. A general methodology for preprocessing the available multi-sensor data and developing proper models is proposed and apply in both case studies. In the first one, a LSTM autoencoder is developed, achieving precision values over 83.5% when predicting anomalous states up to 8 h ahead. In the second case study, a CNN-LSTM model is proposed for the pump’s health state prognostics 48 h ahead, achieving precision values above 99% for all possible pump health states.

ACS Style

Joaquín Figueroa Barraza; Luis Guarda Bräuning; Ruben Benites Perez; Carlos Bittencourt Morais; Marcelo Ramos Martins; Enrique Lopez Droguett. Deep learning health state prognostics of physical assets in the Oil and Gas industry. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2020, 1 .

AMA Style

Joaquín Figueroa Barraza, Luis Guarda Bräuning, Ruben Benites Perez, Carlos Bittencourt Morais, Marcelo Ramos Martins, Enrique Lopez Droguett. Deep learning health state prognostics of physical assets in the Oil and Gas industry. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2020; ():1.

Chicago/Turabian Style

Joaquín Figueroa Barraza; Luis Guarda Bräuning; Ruben Benites Perez; Carlos Bittencourt Morais; Marcelo Ramos Martins; Enrique Lopez Droguett. 2020. "Deep learning health state prognostics of physical assets in the Oil and Gas industry." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability , no. : 1.

Journal article
Published: 27 November 2020 in Energies
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Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.

ACS Style

Yonas Zewdu Ayele; Mostafa Aliyari; David Griffiths; Enrique Lopez Droguett. Automatic Crack Segmentation for UAV-Assisted Bridge Inspection. Energies 2020, 13, 6250 .

AMA Style

Yonas Zewdu Ayele, Mostafa Aliyari, David Griffiths, Enrique Lopez Droguett. Automatic Crack Segmentation for UAV-Assisted Bridge Inspection. Energies. 2020; 13 (23):6250.

Chicago/Turabian Style

Yonas Zewdu Ayele; Mostafa Aliyari; David Griffiths; Enrique Lopez Droguett. 2020. "Automatic Crack Segmentation for UAV-Assisted Bridge Inspection." Energies 13, no. 23: 6250.

Journal article
Published: 07 October 2020 in Sensors
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Gas pipeline systems are one of the largest energy infrastructures in the world and are known to be very efficient and reliable. However, this does not mean they are prone to no risk. Corrosion is a significant problem in gas pipelines that imposes large risks such as ruptures and leakage to the environment and the pipeline system. Therefore, various maintenance actions are performed routinely to ensure the integrity of the pipelines. The costs of the corrosion-related maintenance actions are a significant portion of the pipeline’s operation and maintenance costs, and minimizing this large cost is a highly compelling subject that has been addressed by many studies. In this paper, we investigate the benefits of applying reinforcement learning (RL) techniques to the corrosion-related maintenance management of dry gas pipelines. We first address the rising need for a simulated testbed by proposing a test bench that models corrosion degradation while interacting with the maintenance decision-maker within the RL environment. Second, we propose a condition-based maintenance management approach that leverages a data-driven RL decision-making methodology. An RL maintenance scheduler is applied to the proposed test bench, and the results show that applying the proposed condition-based maintenance management technique can reduce up to 58% of the maintenance costs compared to a periodic maintenance policy while securing pipeline reliability.

ACS Style

Zahra Mahmoodzadeh; Keo-Yuan Wu; Enrique Lopez Droguett; Ali Mosleh. Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion. Sensors 2020, 20, 5708 .

AMA Style

Zahra Mahmoodzadeh, Keo-Yuan Wu, Enrique Lopez Droguett, Ali Mosleh. Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion. Sensors. 2020; 20 (19):5708.

Chicago/Turabian Style

Zahra Mahmoodzadeh; Keo-Yuan Wu; Enrique Lopez Droguett; Ali Mosleh. 2020. "Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion." Sensors 20, no. 19: 5708.

Journal article
Published: 01 October 2020 in Applied Soft Computing
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One of the current challenges in structural health monitoring (SHM) is to take the most advantage of large amounts of data to deliver accurate damage measurements and predictions. Deep Learning methods tackle these problems by finding complex relations hidden in the data available. Amongst these, Capsule Neural Networks (CapsNets) have recently been developed, achieving promising results in benchmark Deep Learning problems. In this paper, Capsule Networks are expanded to locate and to quantify structural damage. The proposed approach is evaluated in two case studies: a system with springs and masses that simulate a structure, and a beam with different damage scenarios. For both case studies, training and validation sets are created using Finite Element (FE) models and calibrated with experimental data, which is also used for testing. The main contributions of this study are: A novel CapsNets-based method for dual classification-regression task in SHM, analysis of both routing algorithms (dynamic routing and Expectation-Maximization routing) in the context of SHM, and analysis of generalization between FE models and real-life experiments. The results show that the proposed Capsule Networks with dynamic routing achieve better results than Convolutional Neural Networks (CNN), especially when it comes to false positive values.

ACS Style

Joaquín Figueroa Barraza; Enrique Lopez Droguett; Viviana Meruane Naranjo; Marcelo Ramos Martins. Capsule Neural Networks for structural damage localization and quantification using transmissibility data. Applied Soft Computing 2020, 97, 106732 .

AMA Style

Joaquín Figueroa Barraza, Enrique Lopez Droguett, Viviana Meruane Naranjo, Marcelo Ramos Martins. Capsule Neural Networks for structural damage localization and quantification using transmissibility data. Applied Soft Computing. 2020; 97 ():106732.

Chicago/Turabian Style

Joaquín Figueroa Barraza; Enrique Lopez Droguett; Viviana Meruane Naranjo; Marcelo Ramos Martins. 2020. "Capsule Neural Networks for structural damage localization and quantification using transmissibility data." Applied Soft Computing 97, no. : 106732.

Research article
Published: 14 July 2020 in Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
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Sensing technologies have been used to gather massive amounts of data to improve system reliability analysis with the use of deep learning. Their use has been mainly focused on specific components or for the whole system, resulting in a drawback when dealing with complex systems as the interactions among components are not explicitly taken into account. Here, we propose a system-level prognostics and health management framework based on geometrical deep learning where a system, its components with their interactions, and sensor data are represented as a graph. This enables reliability analysis at different hierarchical levels by means of (1) a system-level module for system health diagnosis and prognosis based on embeddings of the system’s learned features from a graph convolutional network; (2) a component-level module based on a deep graph convolutional network for health state diagnosis for the system’s components; (3) a component interactions module based on a graph convolutional network autoencoder that allows for the identification of interactions among components when the system is in a degraded state. The framework is exemplified via a case study involving a chlorine dioxide generation system, in which it is shown that integrating both components’ interactions and sensor data in the form of a graph improves health state diagnosis capabilities.

ACS Style

Andrés Ruiz-Tagle Palazuelos; Enrique López Droguett. System-level prognostics and health management: A graph convolutional network–based framework. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2020, 235, 120 -135.

AMA Style

Andrés Ruiz-Tagle Palazuelos, Enrique López Droguett. System-level prognostics and health management: A graph convolutional network–based framework. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2020; 235 (1):120-135.

Chicago/Turabian Style

Andrés Ruiz-Tagle Palazuelos; Enrique López Droguett. 2020. "System-level prognostics and health management: A graph convolutional network–based framework." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235, no. 1: 120-135.

Journal article
Published: 15 May 2020 in Journal of Offshore Mechanics and Arctic Engineering
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During a ship life cycle, one of the most critical phases in terms of safety refers to harbor maneuvers, which take place in restricted and congested waters, leading to higher collision and grounding risks in comparison to open sea navigation. In this scenario, a single accident may stop the harbor's traffic as well as incur in patrimonial damage, environmental pollution, human casualties, and reputation losses. In order to support the vessel's captain during the maneuver, local experienced maritime pilots stay on board coordinating the ship navigation while in restricted waters. Aiming to assess the main factors contributing to human errors in pilot-assisted harbor ship maneuvers, this work proposes a Bayesian network model for human reliability analysis (HRA), supported by a prospective human performance model for quantification. The novelty of this work resides into two aspects: (a) incorporation of harbor specific conditions for maritime navigation HRA, including the performance of ship's crew and maritime pilots and (b) the use of a prospective human performance model as an alternative to expert's opinion for quantification purposes. To illustrate the usage of the proposed methodology, this paper presents an analysis of the route keeping task along waterways, starting from the quantification of human error probabilities (HEP) and including the ranking of the main performance shaping factors that contribute to the HEP.

ACS Style

Danilo Taverna Martins Pereira De Abreu; Marcos Coelho Maturana; Enrique Andrés López Droguett; Marcelo Ramos Martins. Human Reliability Analysis of Ship Maneuvers in Harbor Areas. Journal of Offshore Mechanics and Arctic Engineering 2020, 142, 1 -31.

AMA Style

Danilo Taverna Martins Pereira De Abreu, Marcos Coelho Maturana, Enrique Andrés López Droguett, Marcelo Ramos Martins. Human Reliability Analysis of Ship Maneuvers in Harbor Areas. Journal of Offshore Mechanics and Arctic Engineering. 2020; 142 (6):1-31.

Chicago/Turabian Style

Danilo Taverna Martins Pereira De Abreu; Marcos Coelho Maturana; Enrique Andrés López Droguett; Marcelo Ramos Martins. 2020. "Human Reliability Analysis of Ship Maneuvers in Harbor Areas." Journal of Offshore Mechanics and Arctic Engineering 142, no. 6: 1-31.

Original article
Published: 21 February 2020 in International Journal of Machine Learning and Cybernetics
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Solving combinatorial optimization problems is of great interest in the areas of computer science and operations research. Optimization algorithms and particularly metaheuristics are constantly improved in order to reduce execution times, increase the quality of solutions and address larger instances. In this work, an improvement of the binarization framework which uses the K-means technique is developed. To achieve this, a perturbation operator based on the K-nearest neighbor technique is incorporated into the framework with the aim of generating more robust binarized algorithms. The technique of K-nearest neighbors is used for improving the properties of diversification and intensification of metaheuristics in its binary version. The contribution of the K-nearest neighbors perturbation operator to the final results is systematically analyzed. Particle Swarm Optimization and Cuckoo Search are used as metaheuristic techniques. To verify the results, the well-known multidimensional knapsack problem is tackled. A computational comparison is made with the state-of-the-art of metaheuristic techniques that use general mechanisms of binarization. The results show that our improved framework produces consistently better results. In this sense, the contribution of the operator which uses the K-nearest neighbors technique is investigated finding that this operator contributes significantly to the quality of the results.

ACS Style

José García; Eduardo Lalla-Ruiz; Stefan Voß; Enrique López Droguett. Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem. International Journal of Machine Learning and Cybernetics 2020, 11, 1951 -1970.

AMA Style

José García, Eduardo Lalla-Ruiz, Stefan Voß, Enrique López Droguett. Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem. International Journal of Machine Learning and Cybernetics. 2020; 11 (9):1951-1970.

Chicago/Turabian Style

José García; Eduardo Lalla-Ruiz; Stefan Voß; Enrique López Droguett. 2020. "Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem." International Journal of Machine Learning and Cybernetics 11, no. 9: 1951-1970.

Journal article
Published: 27 December 2019 in Sensors
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Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.

ACS Style

David Verstraete; Enrique Droguett; Mohammad Modarres. A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics. Sensors 2019, 20, 176 .

AMA Style

David Verstraete, Enrique Droguett, Mohammad Modarres. A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics. Sensors. 2019; 20 (1):176.

Chicago/Turabian Style

David Verstraete; Enrique Droguett; Mohammad Modarres. 2019. "A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics." Sensors 20, no. 1: 176.

Preprint
Published: 23 September 2019
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Multi-sensor systems are proliferating the asset management industry and by proxy, the structural health management community. Asset managers are beginning to require a prognostics and health management system to predict and assess maintenance decisions. These systems handle big machinery data and multi-sensor fusion and integrate remaining useful life prognostic capabilities. We introduce a deep adversarial learning approach to damage prognostics. A non-Markovian variational inference-based model incorporating an adversarial training algorithm framework was developed. The proposed framework was applied to a public multi-sensor data set of turbofan engines to demonstrate its ability to predict remaining useful life. We find that using the deep adversarial based approach results in higher performing remaining useful life predictions.

ACS Style

David Verstraete; Enrique Droguett; Mohammad Modarres. A deep adversarial approach based on multi-sensor fusion for remaining useful life prognostics. 2019, 1 .

AMA Style

David Verstraete, Enrique Droguett, Mohammad Modarres. A deep adversarial approach based on multi-sensor fusion for remaining useful life prognostics. . 2019; ():1.

Chicago/Turabian Style

David Verstraete; Enrique Droguett; Mohammad Modarres. 2019. "A deep adversarial approach based on multi-sensor fusion for remaining useful life prognostics." , no. : 1.

Research article
Published: 08 September 2019 in Shock and Vibration
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Damage diagnosis has become a valuable tool for asset management, enhanced by advances in sensor technologies that allows for system monitoring and providing massive amount of data for use in health state diagnosis. However, when dealing with massive data, manual feature extraction is not always a suitable approach as it is labor intensive requiring the intervention of domain experts with knowledge about the relevant variables that govern the system and their impact on its degradation process. To address these challenges, convolutional neural networks (CNNs) have been recently proposed to automatically extract features that best represent a system’s degradation behavior and are a promising and powerful technique for supervised learning with recent studies having shown their advantages for feature identification, extraction, and damage quantification in machine health assessment. Here, we propose a novel deep CNN-based approach for structural damage location and quantification, which operates on images generated from the structure’s transmissibility functions to exploit the CNNs’ image processing capabilities and to automatically extract and select relevant features to the structure’s degradation process. These feature maps are fed into a multilayer perceptron to achieve damage localization and quantification. The approach is validated and exemplified by means of two case studies involving a mass-spring system and a structural beam where training data are generated from finite element models that have been calibrated on experimental data. For each case study, the models are also validated using experimental data, where results indicate that the proposed approach delivers satisfactory performance and thus being an appropriate tool for damage diagnosis.

ACS Style

Sergio Cofre-Martel; Philip Kobrich; Enrique Lopez Droguett; Viviana Meruane. Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data. Shock and Vibration 2019, 2019, 1 -27.

AMA Style

Sergio Cofre-Martel, Philip Kobrich, Enrique Lopez Droguett, Viviana Meruane. Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data. Shock and Vibration. 2019; 2019 ():1-27.

Chicago/Turabian Style

Sergio Cofre-Martel; Philip Kobrich; Enrique Lopez Droguett; Viviana Meruane. 2019. "Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data." Shock and Vibration 2019, no. : 1-27.

Accepted manuscript
Published: 05 September 2019 in Smart Materials and Structures
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Real-time monitoring systems that can automatically locate and identify impacts as they occur have become increasingly attractive for ensuring safety and preventing catastrophic accidents in airspace structures. In most cases, a set of piezoelectric transducers distributed over the structure captures strain–time data, which are preprocessed to extract relevant features that are fed to a supervised learning algorithm to detect, locate, and quantify impacts. The best results achieved to date in feature extraction for impact identification have been obtained with the use of principal component analysis (PCA). However, this technique cannot handle complex nonlinear data. The primary contribution of this study is the implementation of a novel impact identification algorithm that uses a supervised learning algorithm called linear approximation with maximum entropy (LME) in conjunction with different linear and nonlinear dimensionality reduction techniques, including PCA, kernel PCA, Isomap, local linear embedding (LLE), and multilayer autoencoders. The performance of LME with the different reduction techniques is tested with two experimental applications. The results show that the techniques that do not employ graphs, such as PCA, kernel PCA, and autoencoders, perform better, and the method that provides the best results is LME in conjunction with autoencoders. It is further demonstrated that LME with autoencoders works better than the algorithms available in the literature for similar problems.

ACS Style

Viviana Meruane; Cony Espinoza; Enrique Lopez Droguett; Alejandro Ortiz-Bernardin. Impact identification using nonlinear dimensionality reduction and supervised learning. Smart Materials and Structures 2019, 28, 115005 .

AMA Style

Viviana Meruane, Cony Espinoza, Enrique Lopez Droguett, Alejandro Ortiz-Bernardin. Impact identification using nonlinear dimensionality reduction and supervised learning. Smart Materials and Structures. 2019; 28 (11):115005.

Chicago/Turabian Style

Viviana Meruane; Cony Espinoza; Enrique Lopez Droguett; Alejandro Ortiz-Bernardin. 2019. "Impact identification using nonlinear dimensionality reduction and supervised learning." Smart Materials and Structures 28, no. 11: 115005.

Conference paper
Published: 07 August 2019 in Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
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With the availability of cheaper multi-sensor systems, one has access to massive and multi-dimensional sensor data for fault diagnostics and prognostics. However, from a time, engineering and computational perspective, it is often cost prohibitive to manually extract useful features and to label all the data. To address these challenges, deep learning techniques have been used in the recent years. Within these, convolutional neural networks have shown remarkable performance in fault diagnostics and prognostics. However, this model present limitations from a prognostics and health management perspective: to improve its feature extraction generalization capabilities and reduce computation time, ill-based pooling operations are employed, which require sub-sampling of the data, thus loosing potentially valuable information regarding an asset’s degradation process. Capsule neural networks have been recently proposed to address these problems with strong results in computer vision–related classification tasks. This has motivated us to extend capsule neural networks for fault prognostics and, in particular, remaining useful life estimation. The proposed model, architecture and algorithm are tested and compared to other state-of-the art deep learning models on the benchmark Commercial Modular Aero Propulsion System Simulation turbofans data set. The results indicate that the proposed capsule neural networks are a promising approach for remaining useful life prognostics from multi-dimensional sensor data.

ACS Style

Andrés Ruiz-Tagle Palazuelos; Enrique López Droguett; Rodrigo Pascual. A novel deep capsule neural network for remaining useful life estimation. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2019, 234, 151 -167.

AMA Style

Andrés Ruiz-Tagle Palazuelos, Enrique López Droguett, Rodrigo Pascual. A novel deep capsule neural network for remaining useful life estimation. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2019; 234 (1):151-167.

Chicago/Turabian Style

Andrés Ruiz-Tagle Palazuelos; Enrique López Droguett; Rodrigo Pascual. 2019. "A novel deep capsule neural network for remaining useful life estimation." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 1: 151-167.

Research article
Published: 23 May 2019 in Structural Health Monitoring
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With the availability of cheaper multisensor suites, one has access to massive and multidimensional datasets that can and should be used for fault diagnosis. However, from a time, resource, engineering, and computational perspective, it is often cost prohibitive to label all the data streaming into a database in the context of big machinery data, that is, massive multidimensional data. Therefore, this article proposes both a fully unsupervised and a semi-supervised deep learning enabled generative adversarial network-based methodology for fault diagnostics. Two public datasets of vibration data from rolling element bearings are used to evaluate the performance of the proposed methodology for fault diagnostics. The results indicate that the proposed methodology is a promising approach for both unsupervised and semi-supervised fault diagnostics.

ACS Style

David Benjamin Verstraete; Enrique López Droguett; Viviana Meruane; Mohammad Modarres; Andrés Ferrada. Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings. Structural Health Monitoring 2019, 19, 390 -411.

AMA Style

David Benjamin Verstraete, Enrique López Droguett, Viviana Meruane, Mohammad Modarres, Andrés Ferrada. Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings. Structural Health Monitoring. 2019; 19 (2):390-411.

Chicago/Turabian Style

David Benjamin Verstraete; Enrique López Droguett; Viviana Meruane; Mohammad Modarres; Andrés Ferrada. 2019. "Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings." Structural Health Monitoring 19, no. 2: 390-411.

Research article
Published: 22 May 2019 in Structural Control and Health Monitoring
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Sandwich structures are subjected to imperfect bonding or debonding caused by defects during the manufacturing process, by fatigue, or by impact loads. In this context, their safety and functionality can be improved with the implementation of vibration‐based structural damage assessment methodologies. These methodologies involve the computation of second or higher order displacement derivatives, which are often obtained using the central difference method. Nevertheless, this method propagates and amplifies the measurement errors and noise. Therefore, a Gaussian process (GP) regression model to build smoothed (noise‐free) curvature mode shapes from noisy experimental mode shape displacements is presented in this paper. The proposed baseline‐free debonding assessment approach combines the gapped smoothing (GS) method, curvature mode shapes estimated using a GP regression, and the valley‐emphasis method to automatically find damaged regions. Experimental results indicate that our approach performs better than the conventional GS method in the presence of experimental noise.

ACS Style

Viviana Meruane; Ignacio Fernandez; Rafael O. Ruiz; Giuseppe Petrone; Enrique Lopez‐Droguett. Gapped Gaussian smoothing technique for debonding assessment with automatic thresholding. Structural Control and Health Monitoring 2019, e2371 .

AMA Style

Viviana Meruane, Ignacio Fernandez, Rafael O. Ruiz, Giuseppe Petrone, Enrique Lopez‐Droguett. Gapped Gaussian smoothing technique for debonding assessment with automatic thresholding. Structural Control and Health Monitoring. 2019; ():e2371.

Chicago/Turabian Style

Viviana Meruane; Ignacio Fernandez; Rafael O. Ruiz; Giuseppe Petrone; Enrique Lopez‐Droguett. 2019. "Gapped Gaussian smoothing technique for debonding assessment with automatic thresholding." Structural Control and Health Monitoring , no. : e2371.