SVM-RBM based predictive maintenance scheme for IoT-enabled smart factory
S Hwang, J Jeong, Y Kang - 2018 thirteenth international …, 2018 - ieeexplore.ieee.org
S Hwang, J Jeong, Y Kang
2018 thirteenth international conference on digital information …, 2018•ieeexplore.ieee.orgFault diagnosis of facility maintenance is very important. Unexpected equipment failures
during the process lead to significant losses to the plant. In this paper, in order to detect
defects and fault patterns, Support Vector Machine (SVM) which is one of the machine
learning algorithms, classifies the data received from the equipment as normal or abnormal.
After learning only normal data by using Restricted Boltzmann Machine (RBM). We propose
a model to identify the data, and then we analyze the faults of facilities in real-time.
during the process lead to significant losses to the plant. In this paper, in order to detect
defects and fault patterns, Support Vector Machine (SVM) which is one of the machine
learning algorithms, classifies the data received from the equipment as normal or abnormal.
After learning only normal data by using Restricted Boltzmann Machine (RBM). We propose
a model to identify the data, and then we analyze the faults of facilities in real-time.
Fault diagnosis of facility maintenance is very important. Unexpected equipment failures during the process lead to significant losses to the plant. In this paper, in order to detect defects and fault patterns, Support Vector Machine (SVM) which is one of the machine learning algorithms, classifies the data received from the equipment as normal or abnormal. After learning only normal data by using Restricted Boltzmann Machine (RBM). We propose a model to identify the data, and then we analyze the faults of facilities in real-time.
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