A two-step precognitive maintenance framework for equipment fault diagnosis with imbalanced data

Effective equipment fault diagnosis can assist to schedule the proper maintenance and reduce breakdown risks for realistic engineering systems. In this paper, a novel two-step precognitive maintenance framework is proposed to diagnose the equipment health conditions based on its real-time Condition Monitoring (CM). The synthetic minority over-sampling technique is implemented firstly to balance a raw CM dataset for training two independent extreme learning machines. Next, our proposed framework will consist of two steps for the fault diagnosis, where Step 1 aims to classify the normal or abnormal categories, and Step 2 further diagnoses the underlying anomaly in details. The effectiveness of our proposed framework is testified on a machine fault simulator with an imbalanced dataset, and it achieves the diagnosis accuracies of more than 97.0%.