Research paper accepted by IEEE Transactions on Instrumentation and Measurement

Deep learning has achieved remarkable success in the field of bearing fault diagnosis. However, this success comes with larger models and more complex computations, which cannot be transferred into industrial fields requiring models to be of high speed, strong portability, and low power consumption. In this paper, we propose a lightweight and deployable model for bearing fault diagnosis, referred to as BearingPGA-Net, to address these challenges. Firstly, aided by a well-trained large model, we train BearingPGA-Net via decoupled knowledge distillation. Despite its small size, our model demonstrates excellent fault diagnosis performance compared to other lightweight state-of-the-art methods. Secondly, we design an FPGA acceleration scheme for BearingPGA-Net using Verilog. This scheme involves the customized quantization and designing programmable logic gates for each layer of BearingPGA-Net on the FPGA, with an emphasis on parallel computing and module reuse to enhance the computational speed. To the best of our knowledge, this is the first instance of deploying a CNN-based bearing fault diagnosis model on an FPGA. Experimental results reveal that our deployment scheme achieves over 200 times faster diagnosis speed compared to CPU, while achieving a lower-than-0.4% performance drop in terms of F1, Recall, and Precision score on our independently-collected bearing dataset. Our code is available at https://github.com/asdvfghg/BearingPGA-Net.

Research paper accepted by IEEE Transactions on Reliability

As safety is the top priority in mission-critical engineering applications, uncertainty quantification emerges as a linchpin to the successful deployment of AI models in these high-stakes domains. In this paper, we seamlessly encode a simple and principled uncertainty quantification module Spectral-normalized Neural Gaussian Process (SNGP) into GoogLeNet to detect various defects in steel wire ropes (SWRs) accurately and reliably. To this end, the developed methodology consists of three coherent steps. In the first step, raw Magnetic Flux Leakage (MFL) signals in waveform associated with normal and defective SWRs that are manifested in the number of broken wires are collected via a dedicated experimental setup. Next, the proposed approach utilizes Gramian Angular Field to represent the MFL signal in 1-D time series as 2-D images while preserving key spatial and temporal structures in the data. Thirdly, built atop the backbone of GoogLeNet, we systematically integrate SNGP by adding the spectral normalization (SN) layer to normalize the weights and replacing the output layers with a Gaussian process (GP) in the main network and auxiliary classifiers of GoogLeNet accordingly, where SN enables to preserve the distance in data transformation and GP makes the output layer of neural network distance aware when assigning uncertainty. Comprehensive comparisons with the state-of-the-art models highlight the advantages of the developed methodology in classifying SWR defects and identifying out-of-distribution (OOD) SWR instances. In addition, a thorough ablation study is performed to quantitatively illustrate the significant role played by SN and GP in the principledness of the estimated uncertainty towards detecting SWR instances with varying OODness.

Prof. Stefan Feuerrigel gave a talk on “Learning policies for decision-making with causal machine learning: The case of development financing”

The Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by “leaving no one behind”, and, to achieve the SDGs by 2030, poor countries require immense volumes of development aid. In this work, we develop a causal machine learning framework for estimating heterogeneous treatment effects of aid disbursements that inform optimal aid allocation. We demonstrate the effectiveness of our method using data with official development aid earmarked to end HIV/AIDS in 105 countries, amounting to more than USD 5.2 billion. For this, we first show that our method successfully computes heterogeneous treatment-response curves using semi-synthetic data. Then, using real-world HIV data, we find that an optimal aid allocation suggested by our method could reduce the total number of new HIV infections compared to current allocation practice. Our findings indicate the effectiveness of causal machine learning to inform cost-efficient allocations of development aid that maximize progress towards the SDGs.

Two group members attended the 5th International Conference on System Reliability and Safety Engineering (SRSE 2023) in Beijing, China

Two group members attended the 5th International Conference on System Reliability and Safety Engineering (SRSE 2023) in Beijing, China from October 20-23, 2023, conjunction with the annual meeting of Institute for Quality and Reliability (IQR), Tsinghua University. The conference is sponsored by Tsinghua University, supported by National University of Singapore, organized by Institute for Quality and Reliability, Tsinghua University, co-organized by Department of Industrial Engineering, Tsinghua University, patrons with Beijing Institute of Technology, Harbin Institute of Technology, Nanjing University of Science and Technology, Qingdao University, Shanghai University, Shanghai Jiao Tong University, Northwestern Polytechnical University, Sun Yat-sen University, City University of Hong Kong, University of Alberta, etc.

Dr. Xiaoge Zhang delivered a talk on “Safety assessment and risk analysis of complex systems under uncertainty” at Nanjing University, China

This talk showcases two different strategies to assess and analyze the safety of air transportation system. In the first place, considering the rich information in the historical aviation accident events, we analyzed the accidents reported in the National Transporation Safety Board (NTSB) over the past two decades, and developed a large-scale Bayesian network to model the causal relationships among a variety of factors contributing to the occurrence of aviation accidents. The construction of Bayesian network greatly facilitates the root cause diagnosis and outcome analysis of aviation accident. Next, we analyze how to leverage deep learning to forecast flight trajectory. Using Bayesian neural network, we fully characterize the effect of exogenous variables on the flight trajectory. The predicted trajectory is then expanded to multiple flights, and used to assess safety based on horizontal and vertical separation distance between two flights, thus enabling real-time monitoring of in-flight safety.

Dr. Xiaoge Zhang delivered a talk on “A Review on Uncertainty Quantification of Neural Network and Its Application for Reliable Detection of Steel Wire Rope Defects” at Hunan University, China

This talk provides a holistic lens on emerging uncertainty quantification (UQ) methods for ML models with a particular focus on neural networks and gives a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process (SNGP). Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Based on the findings of the comparison, we exploit the advantages of SNGP in UQ and develop an uncertainty-aware deep neural network to detect the defects of steel wire rope. Computational experiments and comparisons with state-of-the-art models suggest that the principled uncertainty quantified by SNGP not only substantially enhances the prediction performance, but also provides an essential layer of protection for neural network against out-of-distribution data.

Research paper accepted by Mechanical Systems and Signal Processing

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Toward this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. Two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).

Dr. Xiaoge Zhang delivered an online talk on “A Tutorial on Uncertainty Quantification of Neural Network and Its Application for Reliable Detection of Steel Wire Rope Defects” at University of Tennessee, Knoxville (UTK)

This talk provides a holistic lens on emerging uncertainty quantification (UQ) methods for ML models with a particular focus on neural networks and gives a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process (SNGP). Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Based on the findings of the comparison, we exploit the advantages of SNGP in UQ and develop an uncertainty-aware deep neural network to detect the defects of steel wire rope. Computational experiments and comparisons with state-of-the-art models suggest that the principled uncertainty quantified by SNGP not only substantially enhances the prediction performance, but also provides an essential layer of protection for neural network against out-of-distribution data.

Prof. Indranil Bose gave a talk on “Toward a Cognitive Understanding of Mobile Augmented Reality”

Augmented Reality (AR) has become increasingly popular in different areas of application and is transforming the way mobile commerce is conducted through mobile apps. It imbibes the sensory experience of local presence during online browsing. We investigate the impact of AR-based presentation on sales rank of products using data from Amazon AR View. This is followed by our investigation of the impact of AR on perceived diagnosticity, perceived risk, perceived cognitive load, and emotion of users. Using a mixed-method approach, we find that the use of AR on a mobile app significantly improves sales rank, enhances perceived diagnosticity, and reduces perceived risk. These effects are greater for technology products. Additionally, we find that AR significantly increases perceived cognitive load for non-technology products. We capture various touch movements, such as pan, pinch, and rotate as well as touch pressure, in consumers’ AR interactions and find that they significantly impact the generated emotion. Our research contributes to the literature on mobile commerce and provides directions on when to use the AR interface for product presentation and how to assess consumers’ reactions to it.

Welcome five new PhD students to join the group!

The group for risk, reliability, and resilience informatics warmly welcomes the following PhD students to join the team:

Yanwen Jin, Ruonan Zhu, Chenyu Li, Pingping Dong, Jingxiao Liao.