Reliability engineering, system safety, and risk analysis are interrelated disciplines. More precisely, risk analysis provides the overarching conceptual framework for the other two. Reliability engineering aims at the development and application of methods and tools to (1) understand why and how components, systems, and processes fail, (2) measure, track, and predict levels of reliability during systems life cycle, (3) improve reliability by applying science and engineering to remove failure causes, and (4) provide input to decisions regarding system design and operation. The Garrick Institute’s CRSE leverages the range of expertise that is already present in various departments of the Henry Samueli School of Engineering and Applied Science to define areas of concentration, develop new research initiatives, secure new funding, and attract leading researchers to the CRSE.

The CRSE has identified five strategic research areas reflecting emerging industry and public sector needs that call for advanced methods and technologies:

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Prognostics and Health Management of Complex Systems

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Software

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WiSE

Wildfire Safe Egress (WiSE) integrates the fire dynamics, human behavior, and traffic model to predict the chance of safe egress by any given community during a wildfire evacuation. WISE framework presents a unified dependency diagram and workflow offering consistent granularity between sub-models and creates comparable evacuation scenarios. A human behavior model is used to predict the community decision making and action based on their socio-demographic vulnerability profile. An agent-based stochastic approach generates evacuation departure times. The travel times are calculated through a congestion-informed traffic simulation, and a Bayesian Network is used to combine the sub-models and to predict community safety (probability of successful evacuation) via probabilistic inference based on the integrated model.

DataBruin

It is an open access and web-based graphical programming environment developed at UCLA for preprocessing multi-sensor monitoring data and developing deep learning solutions in general, and for reliability and maintenance areas in particular. In DataBruin, implementation of the data flow diagrams in an intuitive drag-and-drop manner not only offers a fast prototyping and code-free platform by helping practitioners to focus on the concepts rather than the syntax but also makes the opportunity to guide them toward error-free prototyping of deep learning-based Prognostics and Health Management (PHM) models. Also, DataBruin provides standardization on the structure of deep learning PHM projects by offering a comprehensive path from preparing datasets all through the predictions and necessary assessments, besides the standard auto-generated Python code for each analysis step.

Projects

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Pilot Testing and Assessment of Safety and Integrity of Targeted Hydrogen Blending in Gas Infrastructure for Decarbonization

Source of Support: California Energy Commission (CEC)
Place of Performance: UCLA+UCI+DNV
Project Period: 2023-2026
Total Award Amount: $5,658,000 USD

Corrosion Control Knowledge and Technology Integration for Safer California Natural Gas Pipeline System

Source of Support: California Energy Commission (CEC)
Place of Performance: UCLA+DNV
Project Period: 2022-2025
Total Award Amount: $1,200,000 USD

Methods to Allow Agencies to Incorporate Quantitative Risk Assessment at Project and Network Level

Source of Support: US Transportation Research Board (NCHRP)
Place of Performance: UCLA
Project Period: 2022-2025
Total Award Amount: $500,000 USD

Plastic Pipeline Deficiency Inspection for Pipeline Integrity Management

Source of Support: California Energy Commission (CEC)
Place of Performance: UCLA+GTI+Lumina
Project Period: 2023-2026
Total Award Amount: $1,200,000 USD

A Risk Analysis Framework for Evaluating the Safety, Reliability, and Economic Implications of Electrolysis for Hydrogen Production at NPPs

Source of Support: DOE-NEUP
Place of Performance: UCLA+UMD
Project Period: 2023-2026
Total Award Amount: $1,000,000 USD

1. Barraza, J.; Lopez Droguett, E.; Martins, M. FS-SCF Network: Neural Network Interpretability Based on Counterfactual Generation and Feature Selection for Fault Diagnosis. Expert Systems with Applications, 2023.
2. Leite, G.N.P.; Gomes de Sa, T.; Costa, A.C.A.; Petribú, L.; Ochoa Villa, A.A.; Lopez Droguett, E. A Robust Fleet-Based Anomaly Detection Framework Applied to Wind Turbine Vibration Data. Engineering Applications of Artificial Intelligence, 2023.
3. Rodríguez, E.; Cornejo-Ponce, L.; Cardemil, J.M.; Sartke, A. R; Lopez Droguett, E. Estimation of One-Minute Direct Normal Irradiance Using a Deep Neural Network for Five Climate Zones. Renewable & Sustainable Energy Reviews, 2023.
4. Zhou. T.; Zhang, L.; Han, T.; Lopez Droguett, E.; Mosleh, A.; Chan, Felix T.S. An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applications. Reliability Engineering and System Safety, 2023.
5. Munguba, C.F.L.; Leite, G.N.P.; Ochoa Villa, A.A.; Lopez Droguett, E. Condition-Based Maintenance with Reinforcement Learning for Refrigeration Systems with Selected Monitored Features. Engineering Applications of Artificial Intelligence, 2023.
6. Maior, C. B. S.; Araújo, L. M. M.; Lins, I. D.; Moura, M. J. C.; López Droguett, E. Prognostics and Health Management of rotating machinery via Quantum Machine Learning. IEEE Access, 2023.
7. Paltrinieri, N.; Patriarca, R.; Lopez Drotoguett, E. Meta-Learning Peculiarities and Approaches for Facing Safety Issues and Enhancing Risk Management Process. Editorial, Safety Science, 2022.
8. Zhou. T.; Zhang, X.; Lopez Droguett, E.; Mosleh, A. A Generic Physics-Informed Neural Network-Based Framework for Reliability Assessment of Multi-State Systems. Reliability Engineering and System Safety, 2022.
9. Abreu, D.; Lopez Droguett, E.; Martins, M. R. Human Reliability Analysis of Conventional Maritime Pilotage Operations Supported by a Prospective Model. Reliability Engineering and System Safety, 2022.
10. Ruiz-Tagle, A.; Lopez Droguett, E.; Groth, K. M. A Novel Probabilistic Approach to Counterfactual Reasoning in System Safety. Reliability Engineering and System Safety, 2022.
11. Zhou, T.; Lopez Droguett, E; Mosleh, A. Physics-Informed Deep Learning: A Promising Technique for System Reliability Assessment. Applied Soft Computing, 2022.
12. San Martin, G.; Lopez Droguett. Temporal Variational Auto-Encoders for Semi-Supervised Remaining Useful Life and Fault Diagnosis. IEEE Access, 2022.
13. Zhou, T.; Han, T.; Lopez Droguett, E. Towards Trustworthy Machine Fault Diagnosis: A Probabilistic Bayesian Deep Learning Framework. Reliability Engineering and System Safety, 2022.
14. Correa-Julian, C.; Cofre-Martel, S.; San Martin, G.; Lopez Droguett, E.; Leite, G.; Costa, A. Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection. Energies, 2022.
15. Guarda, L.; Tapia, J.; Lopez Droguett, E.; Martins, M. A Novel Capsule Neural Network Based Model for Drowsiness Detection Using Electroencephalography Signals. Expert Systems with Applications, 2022.
16. Moradi, R.; Cofre-Martel, S.; Lopez Droguett, E.; Modarres, M.; Groth, K. Integration of Deep Learning and Bayesian Networks for Condition and Operation Risk Monitoring of Complex Engineering Systems. Reliability Engineering and System Safety, 2022.
17. Gonzalez, W. V.; Ferrada, A.; Lopez Droguett, E.; Boroschek, R. Caracterization of the Modal Response Using Deep Recurrent Neural Networks. Engineering Structures, 2022.
18. Moradi, R.; Ruiz-Tagle, A.; Lopez Droguett, E.; Groth, K. Toward a Framework for Risk Monitoring of Complex Engineering Systems with Online Operational Data: A Deep Learning-Based Solution. Journal of Risk and Reliability, 2022.
19. Cofre-Martel, S.; Lopez Droguett, E.; Modarres, M. Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management. Sensors, 2021.
20. Schleder, A; Martins, M R; Lopez Droguett, E. RAM Analysis of Dynamic Positioning System: An Approach Taking into Account Uncertainties and Criticality Equipment Ratings. Journal of Risk and Reliability, 2021.
21. Aliyari, M.; Ayele, Y., Lopez Droguett, E. UAV-Based Bridge Inspection via Transfer Learning. Sustainability, 2021.
22. Clavijo Mesa, M.V.; Patino-Rodriguez, C.E.; Guevara Carazas, F.J.; Gunawan, I; Lopez Droguett, E. Asset Management Strategies using Reliability, Availability and Maintainability (RAM) Analysis. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2021.
23. Barraza, J.; Lopez Droguett, E.; Martins, M. Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks. Sensors, 2021.
24. Tapia, J.; Lopez Droguett, E; Valenzuela, A.; Benalcazar, D.; Causa, L. Semantic Segmentation of Periocular Near-Infra-Red Eye Images Under Alcohol Effects. IEEE Access, 2021.
25. Meruane, V.; Aichele, D.; Ruiz, R.; Lopez Droguett, E. A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures. Journal of Shock and Vibration, 2021.
26. Caceres, J; Gonzalez, D.; Zhou, T; Lopez Droguett, E. Bayesian Recurrent Neural Network for Remaining Useful Life Prognostics Considering Epistemic and Aleatory Uncertainties. Structural Control and Health Monitoring, 2021.
27. Cofre-Martel, S.; Lopez Droguett, E.; Modarres, M. Remaining Useful Life Estimation Through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables. Journal of Shock and Vibration, 2021.
28. Zhou, T.; Modarres, M.; Lopez Droguett, E. Multi-Unit Nuclear Power Plant Probabilistic Risk Assessment: A Comprehensive Survey. Reliability Engineering and System Safety, 2021.
29. Ruiz-Tagle, A.; Lopez Droguett, E. Exploiting the Capabilities of Bayesian Networks for Engineering Risk Assessment: Causal Reasoning Through Interventions. Risk Analysis, 2021.
30. Oliveira, A.; Santos, R.; Silva, B.; Guarieiro, L.; Angerhausen, M.; Reisgen, U.; Sampaio, R.; Machado, B.; López Droguett, E.; Silva, P.; Coelho, R.; A Detailed Forecast of the Technologies based on Lifecycle Analysis and Basic Welding Characteristics of GMAW and CMT Processes. Sustainability, 2021.
31. Ruiz-Tagle, A.; Lopez Droguett, E. System-Level Prognostics and Health Management: A Graph Convolutional Network Based Framework. Journal of Risk and Reliability, 2021.
32. Xu, M.; Herrmann, J.; Lopez Droguett, E. Modeling Dependent Series Systems with q-Weibull Distribution and Clayton Copula. Applied Mathematical Modelling, 2021.