Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Prognostics and Availability for Industrial Equipment Using High Performance Computing (HPC) and AI Technology

Version 1 : Received: 2 September 2021 / Approved: 3 September 2021 / Online: 3 September 2021 (14:21:24 CEST)

How to cite: Darveau, P. Prognostics and Availability for Industrial Equipment Using High Performance Computing (HPC) and AI Technology. Preprints 2021, 2021090068 (doi: 10.20944/preprints202109.0068.v1). Darveau, P. Prognostics and Availability for Industrial Equipment Using High Performance Computing (HPC) and AI Technology. Preprints 2021, 2021090068 (doi: 10.20944/preprints202109.0068.v1).

Abstract

The Industrial Internet of things (IIoT) enabled smart system has entered into a golden era of rapid technology growth. IIoT is a concept to make every system interrelated such that they are able to collect and transfer data over a wireless network without human intervention. In this paper, we discuss the development of an IoT enabled system to monitor the vibration signature of equipment as part of prognosis and availability management system (P&AM) that serves to prevent unplanned operation downtime and catastrophic failure of a whole system. In order to simply the complexity of processing video content and performing inference, the Intel OpenVINO platform was selected because of it’s simplicity, portability across Intel AI processors, performance and comprehensiveness of it’s analytical and diagnostics capabilities that can be tested in Intel’s DevCloud. The IIoT system consists of a High Performance Computing (HPC) platform based on Intel’s Xeon processors and Movidius AI accelerator, Intel’s OpenVINO toolkit for AI, a Regul high performance programmable controller capturing vibration data through sensors and a low-latency network connection. Notifications of anomalies are sent to a smartphone. This paper reveals an approach for the features extraction and selection, known as feature engineering, of the equipment component we want to protect. Feature engineering is the first step for the P&AM of these components and extends to the whole system. The broader aim of this paper is to help technical leaders at the exploring or experimenting stages of their AI framework to learn the concepts of implementing algorithms using datasets that have real value to their companies. Datasets generated and referred to in this paper were generated by simulation under various material failure scenarios.

Keywords

Feature engineering; vibration; high performance computing (HPC); dataset; prognostics and availability management (P&AM)

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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