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Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework.

Sarvesh Sundaram¹, Abe Zeid¹

¹College of Engineering, Northeastern University, Boston, MA 02135, USA.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

This summary is machine-generated.

This study reviews Prognostics and Health Management (PHM) approaches and proposes a Smart PHM (SPHM) framework for manufacturing. The framework enables data-driven fault detection and Remaining Useful Life (RUL) estimation in smart manufacturing environments.

Keywords:

Data Mining Deep Learning Machine Learning Smart Manufacturing Smart Prognostics and Health Management data preparation interoperability

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Area of Science:

Manufacturing Engineering
Industrial Informatics
Systems Engineering

Background:

Modern manufacturing employs Industry 4.0, Cyber-Physical Systems, and Digital Twins, evolving from hierarchical to interconnected architectures.
Shop floor devices generate vast data crucial for predictive maintenance and operational insights.
Existing research on Prognostics and Health Management (PHM) often emphasizes tool wear and Condition-Based Monitoring (CBM), neglecting broader PHM applications.

Purpose of the Study:

To conduct a comprehensive review of current Prognostics and Health Management (PHM) approaches and research trends.
To propose a novel, three-phased, interoperable framework for implementing Smart Prognostics and Health Management (SPHM).
To demonstrate the applicability of the SPHM framework through a use-case analysis.

Main Methods:

Literature review of PHM techniques and Smart Manufacturing (SM) trends.
Development of a three-phased SPHM framework: data acquisition, data preparation/analysis, and modeling/prediction/deployment.
Application of the initial two phases of the SPHM framework to real-world milling machine operational data.

Main Results:

The review highlights gaps in current PHM research, particularly concerning its multifaceted applications.
A unique, adaptable SPHM framework is proposed, applicable across diverse manufacturing operations.
The initial phases of the SPHM framework were successfully applied to milling machine data, validating its practical implementation.

Conclusions:

The proposed SPHM framework offers a structured approach to leverage manufacturing data for enhanced predictive maintenance.
The framework's interoperability and phased implementation facilitate its adoption in various industrial settings.
Further research will focus on Phase 3 of the SPHM framework, encompassing advanced modeling and deployment strategies.