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Found 27 papers, 1 papers with code
Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks.
Predictive Analysis for Optimizing Port Operations
This research addresses a significant gap in port analysis models for vessel Stay and Delay times, offering a valuable contribution to the field of maritime logistics.
Optimal Load Shedding for Public Safety Power Shutoffs
This approach will help utilities to effectively manage PSPS events and reduce the risk of wildfires caused by the power lines.
An ensemble of convolution-based methods for fault detection using vibration signals
This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig.
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning
Sequential decision making in the real world often requires finding a good balance of conflicting objectives.
CDA: Contrastive-adversarial Domain Adaptation
Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target domains.
A Functional approach for Two Way Dimension Reduction in Time Series
The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision.
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network
While this method has shown promising performance on a hand pose estimation task in computer vision, it remained unexplored whether this method works as nicely for regression on tabular data, and how it competes with more recent advanced UQ methods such as NGBoost.
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control
Uncertainty quantification is one of the central challenges for machine learning in real-world applications.
K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents
Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of agents.
Deep Reinforcement Learning with Adjustments
Our method can learn complex policies to achieve long-term goals and at the same time it can be easily adjusted to address short-term requirements without retraining.
An Offline Deep Reinforcement Learning for Maintenance Decision-Making
Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years.
Data-driven Residual Generation for Early Fault Detection with Limited Data
Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation.
Deep Time Series Models for Scarce Data
Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research.
A Non-linear Function-on-Function Model for Regression with Time Series Data
Specifically, we aim to learn mathematical mappings from multiple chronologically measured numerical variables within a certain time interval S to multiple numerical variables of interest over time interval T. Prior arts, including the multivariate regression model, the Seq2Seq model, and the functional linear models, suffer from several limitations.
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance.
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
In this paper, we review the main challenges in using deep RL to address the dynamic dispatching problem in the mining industry.
Spatio-Temporal Functional Neural Networks
Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields.
Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning
In this paper, we propose a novel Deep Reinforcement Learning approach to solve the dynamic dispatching problem in mining.
Health Indicator Forecasting for Improving Remaining Useful Life Estimation
In these approaches, health indicator forecasting that constructs the health indicator curve over the lifespan using partially observed measurements (i. e., health indicator values within an initial period) plays a key role.
Building chatbots from large scale domain-specific knowledge bases: challenges and opportunities
Popular conversational agents frameworks such as Alexa Skills Kit (ASK) and Google Actions (gActions) offer unprecedented opportunities for facilitating the development and deployment of voice-enabled AI solutions in various verticals.
Regularized Operating Envelope with Interpretability and Implementability Constraints
However, a bigger challenge with these approaches is that they don't take into account two key features that are needed to operationalize operating envelopes: (i) interpretability of the envelope by the operator and (ii) implementability of the envelope from a practical standpoint.
Manufacturing Dispatching using Reinforcement and Transfer Learning
To address this, we enhance our deep RL model with an approach for dispatching policy transfer.
Generative Adversarial Networks for Failure Prediction
GAN-FP first utilizes two GAN networks to simultaneously generate training samples and build an inference network that can be used to predict failures for new samples.
Long-term planning, short-term adjustments
Our method can learn complex policies to achieve long-term goals and at the same time it can be easily adjusted to address short-term requirements without retraining.
Remaining Useful Life Estimation Using Functional Data Analysis
In this work, we propose a novel Functional Data Analysis (FDA) method called functional Multilayer Perceptron (functional MLP) for RUL estimation.
Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling
Classically, this problem has been posed in two different ways which are typically solved independently: (1) Remaining useful life (RUL) estimation as a long-term prediction task to estimate how much time is left in the useful life of the equipment and (2) Failure prediction (FP) as a short-term prediction task to assess the probability of a failure within a pre-specified time window.
