Open-Set Fault Diagnosis in Multimode Processes via Fine-Grained Deep Feature Representation

TL;DR

This paper introduces a novel open-set fault diagnosis model that leverages fine-grained deep feature representations and extreme value theory to accurately classify known states and identify unknown faults in multimode processes.

Contribution

The paper proposes FGCRN, a new model combining multiscale convolution, RNN, and attention mechanisms with a distance-based loss and unsupervised learning for open-set fault diagnosis.

Findings

Outperforms existing methods in fault classification accuracy.

Effectively detects unknown faults with high reliability.

Demonstrates robustness across various multimode process datasets.

Abstract

A reliable fault diagnosis system should not only accurately classify known health states but also effectively identify unknown faults. In multimode processes, samples belonging to the same health state often show multiple cluster distributions, making it difficult to construct compact and accurate decision boundaries for that state. To address this challenge, a novel open-set fault diagnosis model named fine-grained clustering and rejection network (FGCRN) is proposed. It combines multiscale depthwise convolution, bidirectional gated recurrent unit and temporal attention mechanism to capture discriminative features. A distance-based loss function is designed to enhance the intra-class compactness. Fine-grained feature representations are constructed through unsupervised learning to uncover the intrinsic structures of each health state. Extreme value theory is employed to model the distance between sample features and their corresponding fine-grained representations, enabling effective identification of unknown faults. Extensive experiments demonstrate the superior performance of the proposed method.