Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management

TL;DR

This paper introduces a comprehensive data preprocessing pipeline for machinery health monitoring data, emphasizing expert knowledge and validated through case studies, to improve data-driven prognostics models.

Contribution

It provides a formal, step-by-step data preprocessing methodology specifically designed for real-world PHM applications, addressing a gap in existing research.

Findings

Validated preprocessing pipeline through two case studies

Produced clean datasets with accurate health labels

Enhanced data quality for training prognostics models

Abstract

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several da-ta-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive, step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.