Enrique López Droguett

University of California, Los Angeles | UCLA · Department of Civil and Environmental Engineering

Efficient evacuation of wildfire-threatened communities is a pressing challenge. A reliable evacuation planning and execution requires a comprehensive understanding of the diverse and interdependent physical, social, and behavioral components, and advanced, yet easy to use decision support system. This paper proposes the Wildfire Safe Egress (WiSE)...

Interpretability of deep learning models is essential for their massification in the context of prognostics and health management (PHM), as it is useful for transparency, bias detection, and accountability. These properties help to build trust, which is necessary for deployment in industrial environments. Among different approaches, counterfactuals...

Anomalies in wind turbines pose significant risks of costly downtime and maintenance, underscoring the importance of early detection for reliable operation. However, conventional fault detection methods, often reliant on standalone anomaly detection models, struggle with generalization in such complex settings, leading to suboptimal prediction perf...

Worldwide, wind farms are updated due to technological advances and the end of life from different aspects: social, environmental, technical, and economical. In Brazil, by 2030, around 50 wind farms will reach the age of 20 years of operation, representing more than 600 wind turbines and a total power of around 1,000 MW. This study aims to develop...

The risk significance of seismic dependencies has received growing attention from the nuclear power industry, particularly following the Fukushima disaster in 2011. As a result, many efforts have been devoted to dependency modeling for seismic probabilistic risk assessment (PRA) of nuclear power plants. However, there is still no consensus on a uni...

Over the last decade, concepts such as industry 4.0 and the Internet of Things (IoT) have contributed to the increase in the availability and affordability of sensing technology. In this context, structural health monitoring (SHM) arises as an especially interesting field to integrate and develop these new sensing capabilities, given the criticalit...

Interpretability of neural networks aims at the development of models that can give information to the end-user about its inner workings and/or predictions, while keeping the high levels of performance of neural networks. In the context of fault diagnosis, interpretability is necessary for bias detection, model debugging and building trust. This is...

Large amounts of unlabeled data are produced from wind turbine condition monitoring systems to catch their operational status. With this unmanageable amount of data, developing robust systems with good performance on unseen test data to detect incipient wind turbine faults is crucial to maximizing wind farm performance. This paper presents an imple...

Over the last decade, concepts such as industry 4.0 and the Internet of Things (IoT) have contributed to the increase in the availability and affordability of sensing technology. In this context, Structural Health Monitoring (SHM) arises as an especially interesting field to integrate and develop these new sensing capabilities, given the criticalit...

Worldwide, buildings are responsible for almost 30% of energy consumption, and those buildings that intensively use refrigeration systems, such as supermarkets and grocery stores, are also among the most energy-intensive consumers. Refrigeration devices, either commercial or residential, are responsible for a significant part of net emissions. Base...

Prognostics and Health Management (PHM) concerns predicting machines' behavior to support maintenance decisions through failure modes diagnosis and prognosis. Diagnosis is broadly applied in the context of rotating machines' state classification using several traditional Machine Learning (ML) and Deep Learning (DL) methods. Recently, Quantum Comput...

Iris Recognition (IR) is one of the market’s most reliable and accurate biometric systems. Today, it is challenging to build NIR-capturing devices under the premise of hardware price reduction. Commercial NIR sensors are protected from modification. The process of building a new device is not trivial because it is required to start from scratch wit...

Iris Recognition (IR) is one of the market's most reliable and accurate biometric systems. Today, it is challenging to build NIR-capturing devices under the premise of hardware price reduction. Commercial NIR sensors are protected from modification. The process of building a new device is not trivial because it is required to start from scratch wit...

Deep learning-based models, while highly effective for prognostics and health management, fail to reliably detect the data unknown in the training stage, referred to as out-of-distribution (OOD) data. This restricts their use in safety-critical assets, where unknowns may impose significant risks and cause serious consequences. To address this issue...

Interpretability is a key aspect in deep learning-based prognostics and health management. Nowadays, neural networks are able to achieve outstanding results in recognizing failure patterns within data. However, neural networks work as black boxes as it is almost impossible to track the input value transformations that lead to the output value. This...

This research proposes a method to detect alcohol consumption from Near-Infra-Red (NIR) periocular eye images. The study focuses on determining the effect of external factors such as alcohol on the Central Nervous System (CNS). The goal is to analyse how this impacts on iris and pupil movements and if it is possible to capture these changes with a...

This research proposes a new database and method to detect the reduction of alertness conditions due to alcohol, drug consumption and sleepiness deprivation from Near-Infra-Red (NIR) periocular eye images. The study focuses on determining the effect of external factors on the Central Nervous System (CNS). The goal is to analyse how this impacts iri...

Deep learning-based models for system prognostics and health management have received significant attention in the reliability and safety fields. However, limited progress has been achieved in the usage of deep learning for system reliability assessment. This paper aims to bridge this gap and explore the interface between deep learning and system r...

System risk analysis and safety assessments of Autonomous Driving Systems (ADS) have mostly focused on aspects of the vehicle's functionality, performance, and interactions with other road users under various driving scenarios. However, as the deployment of ADS becomes more common, the importance of addressing risks arising from fleet management op...

In this paper, we develop a generic physics-informed neural network (PINN)-based framework to assess the reliability of multi-state systems (MSSs). The proposed framework follows a two-step procedure. In the first step, we recast the reliability assessment of MSS as a machine learning problem using the framework of PINN. A feedforward neural networ...

In the U.S., the Nuclear Energy Institute (NEI) guidance document NEI 04-10 describes methods to extend the time interval between inspections of surveillance test intervals (STIs) for risk-informed applications. One example of this is the surveillance frequency control program (SFCP). The methodology includes a step to account for a periodic reasse...

Prognostics and health management (PHM) has become a key instrument in the reliability community. Great efforts have gone into estimating systems' remaining useful life (RUL) by taking advantage of monitoring data and data-driven models (DDMs). The latter have gained significant attention since they are model-independent and do not require previous...

One of the most critical phases of a ship voyage is the port arrival and departure. In addition to the conventional bridge team, these activities also employ maritime pilots with advanced local knowledge to support the safe navigation. Recent works addressed the risk assessment of maritime pilotage operations based on expert opinions, AIS data, and...

Safety-critical systems cannot afford to wait for data from multiple high-consequence events to become available in order to inform safety recommendations. Counterfactual reasoning has been widely used in system safety to address this issue, enabling the incorporation of evidence from single events with an analyst’s current knowledge of a system to...

Wildfire is a significant threat to many communities in Wildland Urban Interface (WUI) areas and ensuring an efficient evacuation of these communities in case of wildfire is a pressing challenge. Wildfire evacuation modeling consists of three main layers: fire model, human decision-making, and traffic models. Thus, an efficient evacuation planning...

Wildland Urban Interface (WUI) can be defined as "the zone of transition between unoccupied land and human development." The communities in these areas are particularly vulnerable to wildfires that start and propagate in wildlands. Numerous efforts have been undertaken to address the dangers of wildfires, including building more resilient infrastru...

Driven by the development of machine learning (ML) and deep learning techniques, prognostics and health management (PHM) has become a key aspect of reliability engineering research. With the recent rise in popularity of quantum computing algorithms and public availability of first-generation quantum hardware, it is of interest to assess their poten...

Fault tree analysis is a technique widely used in risk and reliability analysis of complex engineering systems given its deductive nature and relatively simple interpretation. In a fault tree, events are usually represented by a binary variable that indicates whether an event occurs or not, traditionally associated with the values 1 and 0, respecti...

The early detection of drowsiness has become vital to ensure the correct and safe development of several industries' tasks. Due to the transient mental state of a human subject between alertness and drowsiness, automated drowsiness detection is a complex problem to tackle. The electroencephalography signals allow us to record variations in an indiv...

For Civil Engineering System Structural Health Monitoring (SHM), damage identification is typically based on the observation of appropriate response features. A commonly selected feature is the variation of modal frequency due to its high sensitivity to global damage. However, this parameter also has a high sensitivity to variables unrelated to dam...

The early detection of drowsiness has become vital to ensure the correct and safe development of several industries’ tasks. Due to the transient mental state of a human subject between alertness and drowsiness, automated drowsiness detection is a complex problem to tackle. The electroencephalography signals allow us to record variations in an indiv...

Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of industrial machinery. Deep learning has been extensively investigated in fault diagnosis, exhibiting state-of-the-art performance. However, since deep learning is inherently uninterpretable, the low trustworthiness of the diagnostic results given by these bla...

This paper proposes a new method to estimate behavioural curves from a stream of Near-Infra-Red (NIR) iris video frames. This method can be used in a Fitness For Duty system (FFD). The research focuses on determining the effect of external factors such as alcohol, drugs, and sleepiness on the Central Nervous System (CNS). The aim is to analyse how...

A challenging problem in risk and reliability analysis of Complex Engineering Systems (CES) is performing and updating risk and reliability assessments on the whole system with sufficiently high frequency. The challenge stems from both operational data complexity and systems’ complexity. The data complexity calls for novel and advanced data-driven...

A mathematical architecture is developed for system-level condition monitoring. This architecture is built toward performing end-to-end operation risk and condition monitoring. The streaming monitoring data is given to the architecture as the input and system-level and component-level operation health states are computed as the output. This archite...

Deep Learning has seen an incredible popularity surge in recent years mostly due to the state-of-the-art results obtained by neural networks. Nevertheless, within the Prognostics and Health Management community, even though its application in research endeavors is extensive, its use in practice still has many challenges to overcome. A major one is...

The ship’s behaviour and manoeuvrability change as depth of water decreases and/or when the ship is near a bank or shoal. This paper conducts a review on shallow water effects (SWE) and bank effects (BE). It summarizes the varying opinions from both experienced mariners and hydrodynamicists about SWE on factors such as resistance, trim, steering, m...

In this paper, we leverage the recent advances in physics-informed neural network (PINN) and develop a generic PINN-based framework to assess the reliability of multi-state systems (MSSs). The proposed methodology consists of two major steps. In the first step, we recast the reliability assessment of MSS as a machine learning problem using the fram...

Presentation at the 2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2021)

Although the data required to build adequate human reliability analysis (HRA) models remains scarce, there is an abundance of operating experience that could inform HRA modeling. Especially through Bayesian Networks (BNs), HRA causal modeling could benefit from information extrapolated from narrative accounts of relatively common occurrences such a...

Reliability, availability and maintainability (RAM) analysis is used for various strategic purposes in the operations of productive assets to identify key points to apply improvements to. The conventional RAM approaches are, nevertheless, practical guides developed for case studies, not for analyses that consider any level of complexity asset. This...

Currently, a Dynamic Position (DP) System is commonly used for offshore operations. However, DP failures may generate environmental and economic losses; thus, this paper presents the Reliability, Availability and Maintainability (RAM) analysis for two different generations of DP system (DP2 and DP3) used in drilling operations. In addition to the R...

As bridge inspection becomes more advanced and more ubiquitous, artificial intelligence (AI) techniques, such as machine and deep learning, could offer suitable solutions to the nation’s problems of overdue bridge inspections. AI coupling with various data that can be captured by unmanned aerial vehicles (UAVs) enables fully automated bridge inspec...

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Se...

There has been a growing interest in deep learning-based prognostic and health management (PHM) for building end-to-end maintenance decision support systems, especially due to the rapid development of autonomous systems. However, the low trustworthiness of PHM hinders its applications in safety-critical assets when handling data from an unknown dis...

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Se...

As the complexity of modern engineering systems increases, data-driven approaches have become valuable tools to aid maintenance decision-making. However, raw data collected from monitoring sensors require a comprehensive and systematic preprocessing to separate healthy from faulty states before their use in data-driven models. Frequently, anomaly d...

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/i...

Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laboratories around the globe have started to experiment with models that lie in the juxtaposition between machine learning and quantum co...

Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laboratories around the globe have started to experiment with models that lie in the juxtaposition between machine learning and quantum co...

Considerable research has been devoted to deep learning-based predictive models for system prognostics and health management in the reliability and safety community. However, there is limited study on the utilization of deep learning for system reliability assessment. This paper aims to bridge this gap and explore this new interface between deep le...

This paper proposes a new framework to detect, segment, and estimate the localization of the eyes from a periocular Near-Infra-Red iris image under alcohol consumption. This stage will take part in the final solution to measure the fitness for duty. Fitness systems allow us to determine whether a person is physically or psychologically able to perf...

Common cause failure (CCF) events are significant contributors to the risk imposed on most engineering systems and notably on nuclear power plants. Typically, the impacts of CCF are modeled by parametric models. However, these models are built mainly from generic operational experience and are usually not specific to the components’ operating condi...

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)...

This paper proposes a new framework to detect, segment, and estimate the localization of the eyes from a periocular Near-Infra-Red iris image under alcohol consumption. The purpose of the system is to measure the fitness for duty. Fitness systems allow us to determine whether a person is physically or psychologically able to perform their tasks. Ou...

Deep learning-based approach has emerged as a promising solution to handle big machinery data from multi-sensor suites in complex physical assets and predict their remaining useful life (RUL). However, most recent deep learning-based approaches deliver a single-point estimate of RUL as these models represent the weights of a neural network as a det...

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-o...