An Unsupervised Pairwise Comparison Learning Approach With Adaptive Network Structure for Equipment Health Quantitative Assessment

Accurate health assessment of large and complex equipment is essential to ensure their safety, availability, and affordability. Existing machine-learning-based approaches for health assessment face the challenges of difficult access to high-quality labeled data, large variation in operation for different equipment under complex operating conditions, and data-intensive and time-consuming network training. In this article, we propose an error minimized pairwise comparison learning approach (EM-PASCAL) that uses a lightweight network to achieve equipment health assessment under unsupervised conditions. Specifically, a lightweight network model with variable structure inspired by error minimized extreme learning machine (EM-ELM) is designed. Then, we propose EM-PASCAL, which makes full use of the nonincreasing characteristic of equipment health state, to identify the appropriate network structure dynamically and calculate the corresponding network parameters efficiently. The proposed method is expected to achieve promising evaluation results with a low computational effort. Experimental studies using publicly available dataset show that our approach not only enables quantitative equipment health assessment under unsupervised conditions but also offers advantages in terms of evaluation accuracy and computational effort compared with existing benchmarks of machine learning models.