Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model
This study extracted the featured vectors in the same way from testing data and substituted these vectors into a trained hidden Markov model to get the log likelihood probability. The log likelihood probability was matched with the time–probability curve from where the gyro motor state evaluation an...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/12/11/1750 |
| _version_ | 1797550187748524032 |
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| author | Lei Dong Jianfei Wang Ming-Lang Tseng Zhiyong Yang Benfu Ma Ling-Ling Li |
| author_facet | Lei Dong Jianfei Wang Ming-Lang Tseng Zhiyong Yang Benfu Ma Ling-Ling Li |
| author_sort | Lei Dong |
| collection | DOAJ |
| description | This study extracted the featured vectors in the same way from testing data and substituted these vectors into a trained hidden Markov model to get the log likelihood probability. The log likelihood probability was matched with the time–probability curve from where the gyro motor state evaluation and prediction were realized. A core component of gyroscopes is linked to the reliability of the inertia system to conduct gyro motor state evaluation and prediction. This study features the vectors’ extraction from full life cycle gyro motor data and completes the training model to feature the vectors according to the time sequence and extraction to full life cycle data undergoing hidden Markov model training. This proposed model applies to full life cycle gyro motor data for validation, compared with traditional hidden Markov model predictive methods and health condition-trained data. The results suggest precise evaluation and prediction and provide an important basis for gyro motor repair and replacement strategies. |
| first_indexed | 2024-03-10T15:25:58Z |
| format | Article |
| id | doaj.art-c3f2a9dfb23e47838457bd37635acdae |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T15:25:58Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-c3f2a9dfb23e47838457bd37635acdae2023-11-20T18:05:32ZengMDPI AGSymmetry2073-89942020-10-011211175010.3390/sym12111750Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov ModelLei Dong0Jianfei Wang1Ming-Lang Tseng2Zhiyong Yang3Benfu Ma4Ling-Ling Li5School of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Navigation Instrument Research Institute, Tianjin 300131, ChinaInstitute of Innovation and Circular Economy, Asia University, Taichung 41354, TaiwanTianjin Navigation Instrument Research Institute, Tianjin 300131, ChinaTianjin Navigation Instrument Research Institute, Tianjin 300131, ChinaState Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, ChinaThis study extracted the featured vectors in the same way from testing data and substituted these vectors into a trained hidden Markov model to get the log likelihood probability. The log likelihood probability was matched with the time–probability curve from where the gyro motor state evaluation and prediction were realized. A core component of gyroscopes is linked to the reliability of the inertia system to conduct gyro motor state evaluation and prediction. This study features the vectors’ extraction from full life cycle gyro motor data and completes the training model to feature the vectors according to the time sequence and extraction to full life cycle data undergoing hidden Markov model training. This proposed model applies to full life cycle gyro motor data for validation, compared with traditional hidden Markov model predictive methods and health condition-trained data. The results suggest precise evaluation and prediction and provide an important basis for gyro motor repair and replacement strategies.https://www.mdpi.com/2073-8994/12/11/1750state systematic predictionhidden Markov modelfault diagnosisgyro motor |
| spellingShingle | Lei Dong Jianfei Wang Ming-Lang Tseng Zhiyong Yang Benfu Ma Ling-Ling Li Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model Symmetry state systematic prediction hidden Markov model fault diagnosis gyro motor |
| title | Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model |
| title_full | Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model |
| title_fullStr | Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model |
| title_full_unstemmed | Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model |
| title_short | Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model |
| title_sort | gyro motor state evaluation and prediction using the extended hidden markov model |
| topic | state systematic prediction hidden Markov model fault diagnosis gyro motor |
| url | https://www.mdpi.com/2073-8994/12/11/1750 |
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