Reliability Modeling of NC Machine tools Based on Artificial Intelligence
- 1. College of Mechanical Science and Engineering, Jilin University, Changchun, Jilin, 130022 (China)
Description
The level of reliability for NC machine tools represents the development level of the country's manufacturing industry, and its reliability modeling is very important. In order to improve the reliability of NC machine tools, this paper proposes the application of artificial intelligence model for NC machine toolreliability. Firstly, the time history of the NC machine tool reliability is analyzed. Secondly, a reliability evaluation framework based on neural network and bayesian network, intelligent fault diagnosis based on deep learning, and intelligent fault prediction framework based on least squares support vector machine (LS-SVM) are established to achieve remote maintenance of NC machine tools. Finally, with the background of big data, the development vision about the application of artificial intelligence model for NC machine toolreliability is summarized and forecasted. (paper)
Availability note (English)
Available from http://dx.doi.org/10.1088/1757-899X/435/1/012057Additional details
Identifiers
Publishing Information
- Journal Title
- IOP Conference Series. Materials Science and Engineering (Online)
- Journal Volume
- 435
- Journal Issue
- 1
- Journal Page Range
- [6 p.]
- ISSN
- 1757-899X
Conference
- Title
- 2. International Conference on Artificial Intelligence Applications and Technologies
- Acronym
- AIAAT 2018
- Dates
- 8-10 Aug 2018
- Place
- Shanghai (China)
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 52096962
- Subject category
- S42: ENGINEERING; S97: MATHEMATICAL METHODS AND COMPUTING;
- Resource subtype / Literary indicator
- Conference
- Descriptors DEI
- BAYESIAN STATISTICS; COMPUTERIZED SIMULATION; FAULT TREE ANALYSIS; LEAST SQUARE FIT; MACHINE LEARNING; MACHINE TOOLS; MAINTENANCE; MANUFACTURING; NEURAL NETWORKS; RELIABILITY
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; EQUIPMENT; LEARNING; MATHEMATICAL LOGIC; MATHEMATICAL SOLUTIONS; MATHEMATICS; MAXIMUM-LIKELIHOOD FIT; NUMERICAL SOLUTION; SIMULATION; STATISTICS; SYSTEM FAILURE ANALYSIS; SYSTEMS ANALYSIS; TOOLS