Research paper accepted by IEEE Transactions on Automation Science and Engineering
The demand for disruption-free fault diagnosis of mechanical equipment under a constantly changing operation environment poses a great challenge to the deployment of data-driven diagnosis models in practice. Extant continual learning-based diagnosis models suffer from consuming a large number of labeled samples to be trained for adapting to new diagnostic tasks and failing to account for the diagnosis of heterogeneous fault types across different machines. In this paper, we use a representative mechanical equipment – rotating machinery — as an example and develop an uncertainty-aware continual learning framework (UACLF) to provide a unified interface for fault diagnosis of rotating machinery under various dynamic scenarios: class continual scenario, domain continual scenario, and both. The proposed UACLF takes a three-step to tackle fault diagnosis of rotating machinery with homogeneous-heterogeneous faults under dynamic environments. In the first step, an inter-class classification loss function and an intra-class discrimination loss function are devised to extract informative feature representations from the raw vibration signal for fault classification. Next, an uncertainty-aware pseudo labeling mechanism is developed to select unlabeled fault samples that we are able to assign pseudo labels confidently, thus expanding the training samples for faults arising in the new environment. Thirdly, an adaptive prototypical feedback mechanism is used to enhance the decision boundary of fault classification and diminish the model misclassification rate. Experimental results on three datasets suggest that the proposed UACLF outperforms several alternatives in the literature on fault diagnosis of rotating machinery across various working conditions and different machines.
Prof. Tong Wang gave a talk on “Using Advanced LLMs to Enhance Smaller LLMs: An Interpretable Knowledge Distillation Approach”
Large language models (LLMs) like GPT-4 or LlaMa 3 provide superior performance in complex human-like interactions. But they are costly, or too large for edge devices such as smartphones and harder to self-host, leading to security and privacy concerns. This paper introduces a novel interpretable knowledge distillation approach to enhance the performance of smaller, more economical LLMs that firms can self-host. We study this problem in the context of building a customer service agent aimed at achieving high customer satisfaction through goal-oriented dialogues. Unlike traditional knowledge distillation, where the “student” model learns directly from the “teacher” model’s responses via fine-tuning, our interpretable “strategy” teaching approach involves the teacher providing strategies to improve the student’s performance in various scenarios. This method alternates between a “scenario generation” step and a “strategies for improvement” step, creating a customized library of scenarios and optimized strategies for automated prompting. The method requires only black-box access to both student and teacher models; hence it can be used without manipulating model parameters. In our customer service application, the method improves performance, and the learned strategies are transferable to other LLMs and scenarios beyond the training set. The method’s interpretabilty helps safeguard against potential harms through human audit.
Prof. Lei Ma gave a talk on “Towards Building the Trust of Complex AI Systems in the LLM Era”
In recent years, deep learning-enabled systems have made remarkable progress, powering a surge in advanced intelligent applications. This growth and its real-world impact have been further amplified by the advent of large foundation models (e.g., LLM, Stable Diffusion). Yet, the rapid evolution of these AI systems often proceeds without comprehensive quality assurance and engineering support. This gap is evident in the integration of standards for quality, reliability, and safety assurance, as well as the need for mature toolchain support that provides systematic and explainable feedback of the development lifecycle. In this talk, I will present a high-level overview of our team’s ongoing initiatives to lay the groundwork for Trustworthy Assurance of AI Systems and its industrial applications, e.g., including (1) AI software testing and analysis, (2) our latest trustworthiness assurance efforts for AI-driven Cyber-physical systems with an emphasis on sim2real transition. (3) risk and safety assessment for large foundational models, including those akin to large language models, and vision transformers.
