A General End-to-end Diagnosis Framework for Manufacturing Systems

TL;DR

This paper introduces a versatile, data-driven end-to-end deep learning framework for monitoring manufacturing systems, capable of fault detection and prediction across diverse applications, enhancing smart manufacturing diagnostics.

Contribution

The paper presents a novel general framework leveraging deep learning for fault diagnosis and prediction in manufacturing, applicable across various datasets and contexts.

Findings

Framework performs well on ten diverse datasets

Effective in detecting and predicting faults and wear conditions

Potential to serve as a foundational tool in smart manufacturing

Abstract

The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on ten representative datasets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical corner stone in smart manufacturing.