Development Of Predictive Maintenance Methodology Utilizing Machine Learning Technology To Support Plant Health Management

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

Download Full-text

Digital twins, artificial intelligence, and machine learning technology to identify a real personalized motion axis of the tibiotalar joint for robotics in total ankle arthroplasty

International Orthopaedics ◽

10.1007/s00264-021-05175-2 ◽

2021 ◽

Author(s):

Philippe Hernigou ◽

Romain Olejnik ◽

Adonis Safar ◽

Sagi Martinov ◽

Jacques Hernigou ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Total Ankle Arthroplasty ◽

Learning Technology ◽

Tibiotalar Joint ◽

Ankle Arthroplasty ◽

Digital Twins

Download Full-text

Implementation Framework for a Blockchain-Based Federated Learning Model for Classification Problems

Symmetry ◽

10.3390/sym13071116 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1116

Author(s):

Zeba Mahmood ◽

Vacius Jusas

Keyword(s):

Machine Learning ◽

Learning Model ◽

Global Perspective ◽

Learning Technology ◽

Classification Problems ◽

Zero Knowledge ◽

Privacy And Security ◽

Implementation Framework ◽

The Past

This paper introduces a blockchain-based federated learning (FL) framework with incentives for participating nodes to enhance the accuracy of classification problems. Machine learning technology has been rapidly developed and changed from a global perspective for the past few years. The FL framework is based on the Ethereum blockchain and creates an autonomous ecosystem, where nodes compete to improve the accuracy of classification problems. With privacy being one of the biggest concerns, FL makes use of the blockchain-based approach to ensure privacy and security. Another important technology that underlies the FL framework is zero-knowledge proofs (ZKPs), which ensure that data uploaded to the network are accurate and private. Basically, ZKPs allow nodes to compete fairly by only submitting accurate models to the parameter server and get rewarded for that. We have conducted an analysis and found that ZKPs can help improve the accuracy of models submitted to the parameter server and facilitate the honest participation of all nodes in FL.

Download Full-text

Research on the expression of new visual intelligence system based on machine learning technology

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189835 ◽

2021 ◽

pp. 1-9

Author(s):

Zuoshan Li

Keyword(s):

Machine Learning ◽

Intelligent System ◽

Science And Technology ◽

Learning Technology ◽

3D Animation ◽

Motion Data ◽

Intelligence System ◽

New Science ◽

Continuous Progress ◽

Internet Of Things Technology

With the continuous progress of society, the level of science and technology of the country has made a leap forward development, the research energy of various industries on new science and technology continues to deepen, greatly promoting the promotion of science and technology. At the same time, with the increase in social pressure, more and more people pursue spiritual relaxation, and appropriate leisure and entertainment activities have gradually become a part of people’s life. Film plays an irreplaceable role in leisure and entertainment. Mainly from the background of the development of the film industry towards intelligent direction, and then use machine learning technology to study the application of film animation production and film virtual assets analysis and investigation. Based on the Internet of things technology, we also vigorously develop the ways and methods of visual expression of movies, and at the same time introduce new expression modes to promote the expression effect of the intelligent system. Finally, by comparing various algorithms in machine learning technology, the results of intelligent expression of random number forest algorithm in machine learning technology are more accurate. The system is also applied to 3D animation production to observe the measurement error of 3D motion data and facial expression data.

Download Full-text

Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

Sensors ◽

10.3390/s21041044 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1044

Author(s):

Yassine Bouabdallaoui ◽

Zoubeir Lafhaj ◽

Pascal Yim ◽

Laure Ducoulombier ◽

Belkacem Bennadji

Keyword(s):

Machine Learning ◽

Model Development ◽

Predictive Maintenance ◽

Data Availability ◽

Machine Learning Techniques ◽

Automation System ◽

Building Maintenance ◽

Collection Data ◽

Iot Devices

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

Download Full-text