PrognosEase: A data generator for health deterioration prognosis

Abstract

This paper presents PrognosEase; a software that provides an easier way to produce different types of run-to-failure data mimicking real-world conditions to simplify prognosis studies in terms of data collection and improvement in machine learning degradation modeling process. Different types of degradation types made available to meet different types of applications. Besides, some primary machine learning experiments were performed to ensure that complexity patterns of real systems could be observed in the training/testing predictions attitude. This paper also presents the impacts, limitations and potential improvements of the data generator.

Keywords

• Data generator
• Dataset
• Deep learning
• Health index
• Machine learning
• Prognosis and health management