Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
Maintenance processes are of high importance for industrial plants. They have to be performed regularly and uninterruptedly. To assist maintenance personnel, industrial sensors monitored by distributed control systems observe and collect several machinery parameters in the cloud. Then, machine learn...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/16/4/202 |
| _version_ | 1827746243191767040 |
|---|---|
| author | Sotirios Kontogiannis Theodosios Gkamas Christos Pikridas |
| author_facet | Sotirios Kontogiannis Theodosios Gkamas Christos Pikridas |
| author_sort | Sotirios Kontogiannis |
| collection | DOAJ |
| description | Maintenance processes are of high importance for industrial plants. They have to be performed regularly and uninterruptedly. To assist maintenance personnel, industrial sensors monitored by distributed control systems observe and collect several machinery parameters in the cloud. Then, machine learning algorithms try to match patterns and classify abnormal behaviors. This paper presents a new deep learning model called stranded-NN. This model uses a set of NN models of variable layer depths depending on the input. This way, the proposed model can classify different types of emergencies occurring in different time intervals; real-time, close-to-real-time, or periodic. The proposed stranded-NN model has been compared against existing fixed-depth MLPs and LSTM networks used by the industry. Experimentation has shown that the stranded-NN model can outperform fixed depth MLPs 15–21% more in terms of accuracy for real-time events and at least 10–14% more for close-to-real-time events. Regarding LSTMs of the same memory depth as the NN strand input, the stranded NN presents similar results in terms of accuracy for a specific number of strands. Nevertheless, the stranded-NN model’s ability to maintain multiple trained strands makes it a superior and more flexible classification and prediction solution than its LSTM counterpart, as well as being faster at training and classification. |
| first_indexed | 2024-03-11T05:18:42Z |
| format | Article |
| id | doaj.art-350e1f1a91da4eb2bbe2305ed3a30537 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T05:18:42Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-350e1f1a91da4eb2bbe2305ed3a305372023-11-17T17:59:12ZengMDPI AGAlgorithms1999-48932023-04-0116420210.3390/a16040202Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered EventsSotirios Kontogiannis0Theodosios Gkamas1Christos Pikridas2Laboratory Team of Distributed Microcomputer Systems, Department of Mathematics, University of Ioannina, University Campus, 45110 Ioannina, GreeceLaboratory Team of Distributed Microcomputer Systems, Department of Mathematics, University of Ioannina, University Campus, 45110 Ioannina, GreeceSchool of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMaintenance processes are of high importance for industrial plants. They have to be performed regularly and uninterruptedly. To assist maintenance personnel, industrial sensors monitored by distributed control systems observe and collect several machinery parameters in the cloud. Then, machine learning algorithms try to match patterns and classify abnormal behaviors. This paper presents a new deep learning model called stranded-NN. This model uses a set of NN models of variable layer depths depending on the input. This way, the proposed model can classify different types of emergencies occurring in different time intervals; real-time, close-to-real-time, or periodic. The proposed stranded-NN model has been compared against existing fixed-depth MLPs and LSTM networks used by the industry. Experimentation has shown that the stranded-NN model can outperform fixed depth MLPs 15–21% more in terms of accuracy for real-time events and at least 10–14% more for close-to-real-time events. Regarding LSTMs of the same memory depth as the NN strand input, the stranded NN presents similar results in terms of accuracy for a specific number of strands. Nevertheless, the stranded-NN model’s ability to maintain multiple trained strands makes it a superior and more flexible classification and prediction solution than its LSTM counterpart, as well as being faster at training and classification.https://www.mdpi.com/1999-4893/16/4/202classification algorithmsIndustry 4.0industrial maintenance systemsindustrial IoTdeep learningdeep neural networks |
| spellingShingle | Sotirios Kontogiannis Theodosios Gkamas Christos Pikridas Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events Algorithms classification algorithms Industry 4.0 industrial maintenance systems industrial IoT deep learning deep neural networks |
| title | Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events |
| title_full | Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events |
| title_fullStr | Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events |
| title_full_unstemmed | Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events |
| title_short | Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events |
| title_sort | deep learning stranded neural network model for the detection of sensory triggered events |
| topic | classification algorithms Industry 4.0 industrial maintenance systems industrial IoT deep learning deep neural networks |
| url | https://www.mdpi.com/1999-4893/16/4/202 |
| work_keys_str_mv | AT sotirioskontogiannis deeplearningstrandedneuralnetworkmodelforthedetectionofsensorytriggeredevents AT theodosiosgkamas deeplearningstrandedneuralnetworkmodelforthedetectionofsensorytriggeredevents AT christospikridas deeplearningstrandedneuralnetworkmodelforthedetectionofsensorytriggeredevents |