Journal ArticleDOI
Integrated Condition Monitoring and Fault Diagnosis for Modern Manufacturing Systems
TLDR
The overall hardware and software designs of this implemented system monitors power, vibration, temperature and pressure of the drives and spindles with a total of 72 diagnostic features and uses a cost-weighted function to identify diagnostic solutions with the lowest cost.About:
This article is published in CIRP Annals.The article was published on 2000-01-01. It has received 44 citations till now. The article focuses on the topics: Condition monitoring & Process development execution system.read more
Citations
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Journal ArticleDOI
Advanced monitoring of machining operations
TL;DR: In this paper, the past contributions of CIRP in these areas are reviewed and an up-to-date comprehensive survey of sensor technologies, signal processing, and decision making strategies for process monitoring is provided.
Journal ArticleDOI
Maintenance: Changing role in life cycle management
Shozo Takata,Fuminobu Kimura,F.J.A.M. van Houten,E. Westkämper,Moshe Shpitalni,Dariusz Ceglarek,Jay Lee +6 more
TL;DR: In this paper, the authors present a maintenance framework that shows management cycles of maintenance activities during the product life cycle, identifying technical issues of maintenance and discuss the advances of technologies supporting the change in the role of maintenance.
Journal ArticleDOI
Cloud-enabled prognosis for manufacturing
Robert X. Gao,Lihui Wang,Roberto Teti,David Dornfeld,Soundar R. T. Kumara,Masahiko Mori,Moneer Helu +6 more
TL;DR: The historical development of prognosis theories and techniques and their future growth enabled by the emerging cloud infrastructure are reviewed and techniques for cloud computing are highlighted.
Journal ArticleDOI
A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing
Dazhong Wu,Shaopeng Liu,Li Zhang,Janis P. Terpenny,Robert X. Gao,Thomas R. Kurfess,Judith Ann Guzzo +6 more
TL;DR: A new computational framework that enables remote real-time sensing, monitoring, and scalable high performance computing for diagnosis and prognosis is introduced and a proof-of-concept prototype is developed to demonstrate how the framework can enable manufacturers to monitor machine health conditions and generate predictive analytics.
Journal ArticleDOI
Artificial intelligence and internet of things in small and medium-sized enterprises: A survey
TL;DR: A comprehensive survey and investigation of how widespread AI and IoT are among manufacturing SMEs is presented, and the current limitations and opportunities towards enabling predictive analytics are discussed.
References
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Journal ArticleDOI
A Step towards Intelligent Manufacturing: Modelling and Monitoring of Manufacturing Processes through Artificial Neural Networks
László Monostori,Janos Prohaszka +1 more
TL;DR: In this article, different approaches are described for applying artificial neural network techniques for modelling and monitoring of machining processes (turning, milling) by sensor integration, and special emphasis is placed on the incorporation of varying cutting conditions into the learning phase and solutions, which are based upon normal operation, i.e. cutting with sharp tools.
Journal ArticleDOI
A Predictive Monitoring and Diagnosis System for Manufacturing
S. A. Spiewak,M. Szafarczyk +1 more
TL;DR: In this paper, a systematic approach to the monitoring and diagnosis of machine tools and manufacturing processes is presented, which leads to a design methodology and a generic system structure for shop-floor applications.
Journal ArticleDOI
In-Process Monitoring Method for Machining Environment Based on Simultaneous Multiphenomena Sensing
TL;DR: In this article, a new multifunctional in-process monitoring method has been proposed, based on simultaneous multiphenomena sensing, can monitor multidimensional responses simultaneously through experiment and simulation, it has been verified that the method is effective and applicable to monitoring in a machining environment.
Book
Industrial Sensors and Applications for Condition Monitoring
TL;DR: Part 1 Machine tool monitoring systems: feed force sensors current/power sensors pressure sensors accelerometers torque-controlled machining tool setting system and discussion: sensors condition monitoring summary.