Semi-Supervised Learning Approach for Optimizing Condition-based-Maintenance (CBM) Decisions

Abstract

Recent heightened enthusiasm towards Industrial Artificial Intelligence (IAI) and Industrial Internet of Things (IIoT) coupled with developments in smart sensor technologies have resulted in simultaneous incorporation of several advanced Condition Monitoring (CM) technologies within manufacturing and industrial sectors. Efficient utilization of CM data leads to enhanced safety, reliability and availability of manufacturing systems. In this regard, the paper proposes an efficient and novel hybrid Maintenance Decision Support System (MDSS) for fault diagnostic and prognostic considering CM data along with event- triggered data. The proposed MDSS model is a hybrid Machine Learning (ML)-based solution coupled with statistical techniques. In order to find an optimal maintenance policy, we concentrate the attention on a time-dependent Proportional Hazards Model (PHM) augmented with a semi-supervised ML approach. The developed hybrid model is capable of inferring and fusing High-Dimensional and Multi-modal Streaming (HDMS) data sources in an adaptive and autonomous fashion to recommend optimal maintenance decisions without human intervention. To illustrate the complete structure of the proposed MDSS, experimental evaluations are designed based on a dataset provided by NASA containing run-to-failure and CM data associated with aircraft engines. The effectiveness of the proposed model is demonstrated through a comprehensive set of comparisons with different ML algorithms.