Prof. Zhisheng Ye delivered a talk on “Optimal Abort Policy for Mission-Critical Systems under Imperfect Condition Monitoring”
While most on-demand mission-critical systems are engineered to be reliable to support critical tasks, occasional failures may still occur during missions. To increase system survivability, a common practice is to abort the mission before an imminent failure. We consider optimal mission abort for a system whose deterioration follows a general three-state (normal, defective, failed) semi-Markov chain. The failure is assumed self-revealed, while the healthy and defective states have to be predicted from imperfect condition monitoring data. Due to the non-Markovian process dynamics, optimal mission abort for this partially observable system is an intractable stopping problem. For a tractable solution, we introduce a novel tool of Erlang mixtures to approximate non-exponential sojourn times in the semi-Markov chain. This allows us to approximate the original process by a surrogate continuous-time Markov chain whose optimal control policy can be solved through a partially observable Markov decision process (POMDP). We show that the POMDP optimal policies converge almost surely to the optimal abort decision rules when the Erlang rate parameter diverges. This implies that the expected cost by adopting the POMDP solution converges to the optimal expected cost. Next, we provide comprehensive structural results on the optimal policy of the surrogate POMDP. Based on the results, we develop a modified point-based value iteration algorithm to numerically solve the surrogate POMDP. We further consider mission abort in a multi-task setting where a system executes several tasks consecutively before a thorough inspection. Through a case study on an unmanned aerial vehicle, we demonstrate the capability of real-time implementation of our model, even when the condition-monitoring signals are generated with high frequency.
Congratulations on Jingxiao LIAO to pass his PhD oral defense!!!
In recent years, deep learning has achieved significant success in various fields, including natural language processing, autonomous driving, and computer vision. In the realm of prognostics and health management (PHM) for rolling bearings in rotating machinery—such as aero engines, wind turbines, and high-speed trains—numerous intelligent PHM methodologies have emerged to provide accurate and adaptable machinery fault diagnostics and prognostics. However, methodologically speaking, there is no one-size-fits-all approach. It is widely acknowledged that these data-driven approaches still possess considerable limitations, hindering their widespread adoption in industrial settings.
Three primary challenges persist: (1) the lack of interpretability in deep learning methods, particularly in machinery fault diagnosis, where diagnostic models must be transparent to foster trust in the results and inform maintenance decisions; (2) the limited generalizability and reliability of bearing remaining useful life (RUL) prediction models. When training data is scarce, even under identical operating conditions and with the same bearing types, current RUL models demonstrate suboptimal accuracy. In addition, ensuring the reliability of RUL predictions is an important consideration for making informed maintenance decisions in real-world scenarios; and (3) the difficulty in deploying intelligent diagnosis models to edge devices, which hinders their integration into real-world industrial settings.
Therefore, this dissertation aims to address these challenges by constructing the paradigm of integrating traditional signal processing and modern deep learning methods. We formally define this approach as signal processing-empowered neural networks, which synthesize the complementary strengths of both domains. This framework provides three key advantages: (1) integrating rigorous signal processing theory to improve model interpretability; (2) leveraging the robust feature representation capabilities of signal processing techniques to enhance deep learning model generalizability and auxiliary exponential model to quantify the reliability of RUL predictions; and (3) enabling faster computation and greater accuracy, thereby facilitating the edge device deployment of lightweight models. The research contents are summarized as follows:
Research paper accepted by IEEE Transactions on Emerging Topics in Computational Intelligence
Equipping deep learning models with a principled uncertainty quantification (UQ) has become essential for ensuring their reliable performance in the open world. To handle uncertainty arising from two prevalent sources – distribution shift and out-of-distribution (OOD) – in the open-world settings, this paper presents a unified uncertainty-informed approach for quantifying and managing the risks these factors pose to the dependable function of deep learning models. Toward this goal, we propose leveraging a principled UQ approach — Spectral-normalized Neural Gaussian Process (SNGP) — to quantify the epistemic uncertainty associated with model predictions. Unlike other UQ methods in the literature, SNGP is characterized by two unique properties: (1) applying spectral normalization to the weights of the neural network’s hidden layers to preserve the relative distances among data points during data transformations; (2) replacing the traditional output layer of neural networks with a Gaussian process to enable distance-aware uncertainty estimation. Based on SNGP’s uncertainty estimate, we apply Youden’s index to determine an optimal threshold for categorizing the uncertainty into distinct levels, thereby enabling decision-makers to make uncertainty-informed decisions. Two datasets of varying scale are used to demonstrate how the proposed method facilitates risk assessment and management of deep learning models in the open environment. Computational results reveal that the proposed method achieves prediction performance comparable to Monte Carlo dropout and deep ensemble methods. Importantly, the proposed approach outperforms the other two methods by providing a computationally efficient, consistent, and principled uncertainty estimation under no distribution shift, distribution shift, and OOD conditions.
