Performance Evaluation for Fleet-based and Unit-based Prediction Methods

Abhinav Saxena, Shankar Sankararaman, and Kai Goebel
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Full Paper
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phmce_14_040.pdf578.71 KBJune 5, 2014 - 5:38pm

Within the last decade as several new methods for prognostics have been developed, an overall understanding of the various issues involved in predictions for health management has also improved. However, it appears that there is still a lack of consensus on what constitutes good performance for prognostics. This paper first differentiates prognostics from other prediction approaches before highlighting key attributes of performance for prediction methods. Then it argues that it is important to understand what factors affect the performance of a prognostic approach. Factors such as the application and end use of a prognostic output, the various methods to make predictions, purpose of performance evaluation, etc. are discussed. This paper presents a comprehensive view of various such aspects that dictate or should dictate what performance evaluation must be as far as prognostics is concerned. It is also discussed what should be used as baseline to assess performance and how to interpret commonly used comparisons of algorithm predictions to observed failure times. The primary goal of this paper is to present some arguments of how these issues can be addressed and to stimulate a discussion about meaningful evaluation of prognostic performance. These discussions are followed by a brief description of prognostics metrics proposed recently, their applicability, and limitations.

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Submission Topic Areas: 
Standards and methodologies
Technology maturation
Uncertainty Quantification and Management in PHM
Verification and validation
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