Smart Machinery Monitoring System with Reduced Information Transmission and Fault Prediction Methods Using Industrial Internet of Things

A monitoring system for smart machinery has been considered to be one of the most important goals in recent enterprises. This monitoring system will encounter huge difficulties, such as more data uploaded by smart machines, and the available internet bandwidth will influence the transmission speed of data and the reliability of the equipment monitoring platform. This paper proposes reducing the periodical information that has been uploaded to the monitoring platform by setting an upload event through the traits of production data from machines. The proposed methods reduce bandwidth and power consumption. The monitoring information is reconstructed by the proposed methods, so history data will not reduce storage in the cloud server database. In order to reduce the halt time caused by machine error, the proposed system uses machine-learning technology to model the operating status of machinery for fault prediction. In the experimental results, the smart machinery monitoring system using the Industrial Internet of Things reduces the volume of information uploaded by 54.57% and obtains a 98% prediction accuracy.

[1]  Suzan Bayhan,et al.  Key Advances in Pervasive Edge Computing for Industrial Internet of Things in 5G and Beyond , 2020, IEEE Access.

[2]  Kwan-Hee Yoo,et al.  Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning , 2020, Applied Sciences.

[3]  Rene de Jesus Romero-Troncoso,et al.  Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults , 2020, IEEE Transactions on Industrial Informatics.

[4]  Dong Hoon Kim,et al.  Peak-Load Forecasting for Small Industries: A Machine Learning Approach , 2020, Sustainability.

[5]  Rui Guo,et al.  Research on State Recognition and Failure Prediction of Axial Piston Pump Based on Performance Degradation Data , 2020 .

[6]  Bo-Lin Jian,et al.  Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System , 2020, IEEE Access.

[7]  Tharam S. Dillon,et al.  A Global Manufacturing Big Data Ecosystem for Fault Detection in Predictive Maintenance , 2020, IEEE Transactions on Industrial Informatics.

[8]  Stamatis Voliotis,et al.  Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects , 2019, Sensors.

[9]  Irfan Khan,et al.  A Novel Architecture for Condition Based Machinery Health Monitoring on Marine Vessels Using Deep Learning and Edge Computing , 2019, 2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR).

[10]  Mohd Salman Leong,et al.  Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review , 2019, IEEE Access.

[11]  Mohammadreza Sadeghi,et al.  IoT Enabled Vibration Monitoring Toward Smart Maintenance , 2019, 2019 3rd International Conference on Internet of Things and Applications (IoT).

[12]  Michal Pajak,et al.  Fuzzy identification of a threat of the inability state occurrence , 2018, J. Intell. Fuzzy Syst..

[13]  Zhou Fengxing,et al.  Transform-domain sparse representation based classification for machinery vibration signals , 2018 .

[14]  Wei Qiao,et al.  Sensor Fault Detection and Isolation for a Wireless Sensor Network-Based Remote Wind Turbine Condition Monitoring System , 2018, IEEE Transactions on Industry Applications.

[15]  Wenliao Du,et al.  Identification of multi-fault in rotor-bearing system using spectral kurtosis and EEMD , 2017 .

[16]  P. Geethanjali,et al.  Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibration Signal , 2017, IEEE Sensors Journal.

[17]  Łukasz Muślewski,et al.  Analysis of vibration time histories in the time domain for propulsion systems of minesweepers , 2015 .

[18]  Michal Pajak,et al.  Identification of the operating parameters of a complex technical system important from the operational potential point of view , 2018, J. Syst. Control. Eng..

[19]  Y C Chu,et al.  An effective method for monitoring the vibration data of bearings to diagnose and minimize defects , 2018 .