Abstract
Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied “from scratch”; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising.
Highlights
Prognostic health management (PHM) is a recent discipline supporting the realization of predictive maintenance in complex production systems
Results for predicting the remaining useful life (RUL) based on streaming data is introduced
Analysis is a critical part of an automatic machine, whose affects the at quality
Summary
Prognostic health management (PHM) is a recent discipline supporting the realization of predictive maintenance in complex production systems. It is based on several condition monitoring (CM). In previous research by the authors [4], an effort was made to collect, in a unique reference framework, the models and approaches proposed to date in the literature for feature extraction, diagnostics, and prognostics. The aim was to provide readers with a wide range of possible solutions for implementing PHM in all its parts, depending on the specific application and objectives.
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