Brain-Like Cognition-Driven Model Factory for IIoT Fault Diagnosis by Combining LLMs With Small Models

Abstract

Fault diagnosis is important for predictive maintenance in smart manufacturing, which involves intelligent human-machine interactions in order to make smart decisions for potential problems. Large language model (LLM) is promising in providing general artificial intelligence capabilities in this regard. However, LLM itself can not accurately analyze faults due to heterogeneous data from different Industrial Internet of Things (IIoT) devices in different processes during the complete production process. To accurately diagnose faults and facilitate human-machine interaction, this article proposes a brain-like cognition-driven model factory (BC-MF), using an LLM as a supervisor to adaptively generate personalized small-scale models according to the features of these heterogeneous data, where the vertical federated learning (VFL) idea is adopted. This BC-MF-based fault diagnosis approach includes a preliminary diagnosis phase and a precise diagnosis phase. The preliminary diagnosis is accomplished by prompting the LLM using a brain-like chain of thoughts (BLCoTs). A hypernetwork uses the preliminary diagnostic results and the feature maps trained by each node in the VFL to generate dedicated diagnostic small models and uses these models for final precise diagnostics. The LLM provides fault maintenance recommendations interactively according to the final diagnostic results. Comprehensive evaluations are conducted using four open IIoT datasets and one self-made dataset. It shows that the proposed BC-MF approach is significantly better than the existing approaches, in terms of model accuracy, comprehension of faults, and so on.

Topics & Concepts