Abstract

Due to advances in machine learning techniques and sensor technology, the data driven perspective is nowadays the preferred approach for improving the quality of maintenance for machines and processes in industrial environments. Our study reviews existing maintenance works by highlighting the main challenges and benefits and consequently, it shares recommendations and good practices for the appropriate usage of data analysis tools and techniques. Moreover, we argue that in any industrial setup, the quality of maintenance improves when the applied data driven techniques and technologies: (i) have economical justifications; and (ii) take into consideration the conformity with the industry standards. In order to classify the existing maintenance strategies, we explore the entire data driven model development life cycle: data acquisition and analysis, model development and model evaluation. Based on the surveyed literature we introduce taxonomies that cover relevant predictive models and their corresponding data driven maintenance techniques.

Highlights

  • T HE quality of maintenance is a relevant aspect in the assessment of any industrial product or process, and a challenging research problem

  • In order to deal with such high-dimensional problems, the predictive maintenance strategy uses a variety of techniques and prediction models that study both live and historical information

  • 1) Corrective Maintenance: Our survey shows that the fault recognition and diagnostic is generally seen as a process of pattern recognition i.e. the process of mapping the information i.e the features obtained in the measurement space to the machine faults in the fault space, as described in [19], [20], [21] and [22]

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Summary

INTRODUCTION

T HE quality of maintenance is a relevant aspect in the assessment of any industrial product or process, and a challenging research problem. In order to deal with such high-dimensional problems, the predictive maintenance strategy uses a variety of techniques and prediction models that study both live and historical information. Further on, this information is used to learn prognostics data and to make accurate diagnostics and predictions, as presented by [2], [3], and [4]. This information is used to learn prognostics data and to make accurate diagnostics and predictions, as presented by [2], [3], and [4] They argue that the implementation of effective prognosis for maintenance has a variety of benefits including increased system safety, improved operational reliability, reduced maintenance, inspection times, repair failures and life cycle costs. We argue that understanding the capabilities and challenges of existing multimodal data fusion methods and techniques has the potential to deliver better data analysis tools across all domains, including in the maintenance quality and management field of research

Maintenance Issues Relative to Prediction Quality
Classification of Maintenance Approaches
RESEARCH APPROACH
Description of the Criteria Used for Analysis
FINDINGS AND RESULTS
Analysis of Maintenance Strategies
Analysis of Data Driven Development Life Cycle
DISCUSSIONS
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