Cyber-Physical system based real-time management for tapioca starch industry

The purpose of this work is to develop a new Key Performance Indicator (KPI) that can quantify the cost of Six Big Losses developed by Nakajima and implements it in a Cyber Physical System (CPS), achieving a real-time monitorization of the KPI. This paper follows the methodology explained below. A cost model has been used to accurately develop this indicator together with the Six Big Losses description. At the same time, the machine tool has been integrated into a CPS, enhancing the real-time data acquisition, using the Industry 4.0 technologies. Once the KPI has been defined, we have developed the software that can turn these real-time data into relevant information (using Python) through the calculation of our indicator. Finally, we have carried out a case of study showing our new KPI results and comparing them to other indicators related with the Six Big Losses but in different dimensions. As a result, our research quantifies economically the Six Big Losses, enhances the detection of the bigger ones to improve them, and enlightens the importance of paying attention to different dimensions, mainly, the productive, sustainable, and economic at the same time.

Download Full-text

Internet of Things based Cyber-Physical System framework for Real-Time Operations

2020 IEEE 23rd International Symposium on Real-Time Distributed Computing (ISORC) ◽

10.1109/isorc49007.2020.00031 ◽

2020 ◽

Author(s):

Vatsal Maru ◽

Saideep Nannapaneni ◽

Krishna Krishnan

Keyword(s):

Internet Of Things ◽

Real Time ◽

Physical System ◽

Cyber Physical System ◽

System Framework

Download Full-text

A New Real-Time Flight Simulator for Military Training Using Mechatronics and Cyber-Physical System Methods

Military Engineering ◽

10.5772/intechopen.86586 ◽

2020 ◽

Author(s):

César Villacís ◽

Walter Fuertes ◽

Luis Escobar ◽

Fabián Romero ◽

Santiago Chamorro

Keyword(s):

Real Time ◽

Physical System ◽

Military Training ◽

Flight Simulator ◽

Cyber Physical System

Download Full-text

An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4045663 ◽

2020 ◽

Vol 20 (2) ◽

Cited By ~ 1

Author(s):

Ming-Chuan Chiu ◽

Chien-De Tsai ◽

Tung-Lung Li

Keyword(s):

Machine Learning ◽

Computational Complexity ◽

Fault Detection ◽

Real Time ◽

Physical System ◽

Shop Floor ◽

Cyber Physical System ◽

Machine Learning Method ◽

Learning Method ◽

The Impact

Abstract A cyber-physical system (CPS) is one of the key technologies of industry 4.0. It is an integrated system that merges computing, sensors, and actuators, controlled by computer-based algorithms that integrate people and cyberspace. However, CPS performance is limited by its computational complexity. Finding a way to implement CPS with reduced complexity while incorporating more efficient diagnostics, forecasting, and equipment health management in a real-time performance remains a challenge. Therefore, the study proposes an integrative machine-learning method to reduce the computational complexity and to improve the applicability as a virtual subsystem in the CPS environment. This study utilizes random forest (RF) and a time-series deep-learning model based on the long short-term memory (LSTM) networking to achieve real-time monitoring and to enable the faster corrective adjustment of machines. We propose a method in which a fault detection alarm is triggered well before a machine fails, enabling shop-floor engineers to adjust its parameters or perform maintenance to mitigate the impact of its shutdown. As demonstrated in two empirical studies, the proposed method outperforms other times-series techniques. Accuracy reaches 80% or higher 3 h prior to real-time shutdown in the first case, and a significant improvement in the life of the product (281%) during a particular process appears in the second case. The proposed method can be applied to other complex systems to boost the efficiency of machine utilization and productivity.

Download Full-text