Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept

Algorithms

Margarita Razgon¹  Alireza Mousavi¹ 

[1] Department of Computer Science, Brunel University, London, Uxbridge UB8 3PH, UK;

关键词: Predictive Maintenance;    failure prediction;    Rule Learning;    Decision Tree;    Machine Learning;   

DOI  :  10.3390/a13090219

来源: DOAJ

【 摘 要 】

In this paper we propose a novel approach of rule learning called Relaxed Separate-and- Conquer (RSC): a modification of the standard Separate-and-Conquer (SeCo) methodology that does not require elimination of covered rows. This method can be seen as a generalization of the methods of SeCo and weighted covering that does not suffer from fragmentation. We present an empirical investigation of the proposed RSC approach in the area of Predictive Maintenance (PdM) of complex manufacturing machines, to predict forthcoming failures of these machines. In particular, we use for experiments a real industrial case study of a Continuous Compression Moulding (CCM) machine which manufactures the plastic bottle closure (caps) in the beverage industry. We compare the RSC approach with a Decision Tree (DT) based and SeCo algorithms and demonstrate that RSC significantly outperforms both DT based and SeCo rule learners. We conclude that the proposed RSC approach is promising for PdM guided by rule learning.

【 授权许可】

Unknown