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

Abstract

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.

Logistic Model Tree Forest for Steel Plates Faults Prediction

“…The results indicated that the LSTM models achieved higher accuracy than DT, NN, and weighted NN models based on different metrics for no fault, light fault, and severe fault states. In the other study [64], a smart machinery monitoring system based on machine learning was implemented to simulate the operating state of machinery for fault detection with a reduced volume of transmission information in an industrial IoT. The obtained accuracy from the non-linear SVM algorithm was higher than the results of the NB, RF, DT, KNN, and AdaBoost algorithms.…”

Similarity-based data transmission reduction solution for edge-cloud collaborative AI

“…This solution has been used in different situations such in [16][17][18][19] to reduce temperature, humidity, light, and voltage data sent from sensors to the sink node. In [20], authors proposed a prediction model to reduce the periodical information uploaded from smart industrial machines by not sending data of the same value or data increasing/decreasing linearly because it can be easily predicted.…”

Data transmission reduction formalization for cloud offloading-based IoT systems

“…As in [ 22 – 25 ] using prediction models (Neural Networks, long short-term memory (LSTM), Least Mean Square algorithm (LMS)) to reduce temperature, humidity, light, and voltage data sent from the sensors to the sink node in a wireless sensor network. In [ 26 ], authors proposed a prediction model to reduce the periodical information uploaded from smart industrial machines to the monitoring platform by not sending data of the same value or data increasing/decreasing linearly because it can be easily deduced by comparing the last two values received.…”