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

Sarvesh Sundaram,Abe Zeid

doi:10.3390/s21185994

Abstract

Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation.

Highlights

The modern manufacturing era has enabled the collection of large amounts of data from factories and production plants
There is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of Prognostics and Health Management (PHM)
The first two phases of Smart Prognostics and Health Management (SPHM) are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research

Summary

Introduction

The modern manufacturing era has enabled the collection of large amounts of data from factories and production plants. Data from all levels of an enterprise can be analyzed using Machine Learning (ML) and Deep Learning (DL) techniques. Interdisciplinary approaches such as Industry 4.0 (I4.0), Cyber-Physical Systems (CPS), Cloud-Based Manufacturing (CBM) and Smart Manufacturing (SM) allow the real-time monitoring of operations in manufacturing facilities. These approaches greatly benefit maintenance operations by reducing downtime and thereby cutting costs. Monte-Carlo estimations suggest that annual costs concerning maintenance amount to approximately USD 222 Billion in the United. One of the reasons for these relatively high costs is manufacturing organizations preferring corrective or preventive maintenance as opposed to predictive maintenance

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