Unsupervised Learning for Industrial Robot Health Monitoring: Trends, Techniques, and Challenges
TL;DR
A thorough analysis of unsupervised learning techniques used in the health monitoring of industrial robots explores significant trends and key algorithms, such as clustering, autoencoders, and generative models, assessing their effectiveness in identifying faults and performance degradation.
Abstract
As industrial robots become increasingly essential to modern manufacturing and automation systems, ensuring their durability and operational integrity has emerged as a key concern. Traditional defect detection methods typically depend on labeled datasets and supervised learning techniques, which can be difficult and impractical to implement in real-world industries. In contrast, unsupervised learning presents a compelling alternative by facilitating anomaly detection and fault diagnosis without the need for labeled data. This article offers a thorough analysis of unsupervised learning techniques used in the health monitoring of industrial robots. We explore significant trends and key algorithms, such as clustering, autoencoders, and generative models, assessing their effectiveness in identifying faults and performance degradation. The research addresses the unique challenges associated with high-dimensional sensor data, variable operating conditions, and the lack of ground truth labels. Additionally, we highlight unresolved research questions and potential future directions, emphasizing the need for scalable, interpretable, and real-time solutions. This survey serves as a foundational reference for researchers and practitioners aiming to develop resilient and autonomous health monitoring systems for industrial robots.