Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Machine learning for structural engineering: A state-of-the-art review

Prognostics and health management: A review from the perspectives of design, development and decision

State-level homicide victimization rates in the US in relation to survey measures of household firearm ownership, 2001-2003.

Artificial neural network surrogate modeling with its economic computational consumption and accurate generalization capabilities offers a feasible approach to aerodynamic design in the field of ra...

A review of the artificial neural network surrogate modeling in aerodynamic design

Machine learning for reliability engineering and safety applications: Review of current status and future opportunities

Machine learning (ML) pervades an increasing number of academic disciplines and industries. Its impact is profound, and several fields have been fundamentally altered by it, autonomy and computer vision for example; reliability engineering and safety will undoubtedly follow suit. There is already a large but fragmented literature on ML for reliability and safety applications, and it can be overwhelming to navigate and integrate into a coherent whole. In this work, we facilitate this task by providing a synthesis of, and a roadmap to this ever-expanding analytical landscape and highlighting its major landmarks and pathways. We first provide an overview of the different ML categories and sub-categories or tasks, and we note several of the corresponding models and algorithms. We then look back and review the use of ML in reliability and safety applications. We examine several publications in each category/sub-category, and we include a short discussion on the use of Deep Learning to highlight its growing popularity and distinctive advantages. Finally, we look ahead and outline several promising future opportunities for leveraging ML in service of advancing reliability and safety considerations. Overall, we argue that ML is capable of providing novel insights and opportunities to solve important challenges in reliability and safety applications. It is also capable of teasing out more accurate insights from accident datasets than with traditional analysis tools, and this in turn can lead to better informed decision-making and more effective accident prevention.

Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities

In transonic flight within a certain range of Mach number and angle of attack, the flowfield becomes unstable, and it produces an oscillating aerodynamic force: a phenomenon commonly known as trans...

Optimization of Supercritical Airfoil Design with Buffet Effect

Machine learning for helicopter accident analysis using supervised classification: Inference, prediction, and implications

Fire detection is a critical component of a building safety monitoring system and remains an important research area with weighty practical relevance. Significant advances have occurred in recent years in building automation, and the operation of buildings has become more complex and requires ever more effective monitoring systems. In this work, we develop a novel fire detection method using deep Long-Short Term Memory (LSTM) neural networks and variational autoencoder (VAE) to meet these increasingly stringent requirements and outperform existing fire detection methods. To evaluate the effectiveness of our method, we develop high-fidelity simulations, and we use datasets from real-world fire and non-fire experiments provided by NIST. We compare and discuss the performance of our proposed fire detection with alternative methods, including the standard LSTM, cumulative sum control chart (CUSUM), exponentially weighted moving average (EWMA), and two currently used fixed-temperature heat detectors. The results using the simulation-based and the real-world experiments are complementary, and they indicate that the LSTM-VAE robustly outperforms the other detection methods with, for example, statistically significant shorter alarm time lags, no missed detection, and no false alarms. The results also identify shortcomings of other detection methods and indicate a clear ranking among them (LSTM-VAE $\mathrm {\succ }EWMA\succ LSTM\mathrm {\succ }CUSUM$ ).

/pdf/advances-toward-the-next-generation-fire-detection-deep-lstm-f8av3pkra3.pdf

Zhaoyi Xu

Papers

Machine learning for reliability engineering and safety applications: Review of current status and future opportunities

Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities

Optimization of Supercritical Airfoil Design with Buffet Effect

Machine learning for helicopter accident analysis using supervised classification: Inference, prediction, and implications

Advances Toward the Next Generation Fire Detection: Deep LSTM Variational Autoencoder for Improved Sensitivity and Reliability