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

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.

Exploring Digital Twin-based Fault Monitoring: Challenges and Opportunities

“…Ref. [39] provides a data-driven approach to Smart Prognostics and Health Management (SPHM) of specifically a milling machine, using large amounts of data generated from shop floor devices for detecting the presence of a fault and estimating the Remaining Useful Life (RUL), and it highlights the need for a multifaceted approach or framework with Prognostics and Health Management (PHM) which includes three phases: Setup and Data Acquisition, Data Preparation and Analysis, and SPHM Modeling and Evaluation. These three phases explain that predictive maintenance is a collection of methods (machine learning, deep learning, reliability, etc.).…”

Artificial Intelligence-Based Smart Quality Inspection for Manufacturing

“…Prognostics and Health Management (PHM) is a discipline that monitors the system’s health, detects failures, diagnoses failures, and predicts the Remaining Useful Life (RUL) of components [ 11 ]. Using the Internet of Things (IoT) -powered sensors and field devices, operating conditions of critical tools and components can be monitored in real-time.…”

“…Hence, there is an advantage to using DL for prognostics and diagnostics applications. A detailed methodology developed in [ 11 ] reviews the various approaches to PHM: data-driven, physics-based, and hybrid and use-case on health monitoring of a milling machine tool.…”

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

“…These systems can help identify potential problems before they occur, allowing for early intervention and preventative measures. Integrating cyber-physical systems, AI, and machine learning in industry 5.0 is expected to bring about significant changes in manufacturing and production processes, leading to more efficient, flexible, and adaptable factories [ 6 , 7 , 8 ].…”