Reliability Evaluation and Prediction Method with Small Samples
How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliabi...
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| Format: | Article |
| Language: | English |
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Ram Arti Publishers
2023-08-01
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| Series: | International Journal of Mathematical, Engineering and Management Sciences |
| Subjects: | |
| Online Access: | https://www.ijmems.in/article_detail.php?vid=8&issue_id=39&article_id=503 |
| _version_ | 1797795971875209216 |
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| author | Hongyan Dui Xinghui Dong Junyong Tao |
| author_facet | Hongyan Dui Xinghui Dong Junyong Tao |
| author_sort | Hongyan Dui |
| collection | DOAJ |
| description | How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities.
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| first_indexed | 2024-03-13T03:26:06Z |
| format | Article |
| id | doaj.art-40a4695067c34b46a7808633904f35cd |
| institution | Directory Open Access Journal |
| issn | 2455-7749 |
| language | English |
| last_indexed | 2024-03-13T03:26:06Z |
| publishDate | 2023-08-01 |
| publisher | Ram Arti Publishers |
| record_format | Article |
| series | International Journal of Mathematical, Engineering and Management Sciences |
| spelling | doaj.art-40a4695067c34b46a7808633904f35cd2023-06-25T08:42:43ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492023-08-0184560580https://doi.org/10.33889/IJMEMS.2023.8.4.032Reliability Evaluation and Prediction Method with Small SamplesHongyan Dui0Xinghui Dong1Junyong Tao2School of Management, Zhengzhou University, Zhengzhou, Henan, China.School of Management, Zhengzhou University, Zhengzhou, 450001, China.Laboratory of Science and Technology on Integrated Logistics Support, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China.How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities. https://www.ijmems.in/article_detail.php?vid=8&issue_id=39&article_id=503reliability evaluationcox modellogistic regression modelreliability prediction |
| spellingShingle | Hongyan Dui Xinghui Dong Junyong Tao Reliability Evaluation and Prediction Method with Small Samples International Journal of Mathematical, Engineering and Management Sciences reliability evaluation cox model logistic regression model reliability prediction |
| title | Reliability Evaluation and Prediction Method with Small Samples |
| title_full | Reliability Evaluation and Prediction Method with Small Samples |
| title_fullStr | Reliability Evaluation and Prediction Method with Small Samples |
| title_full_unstemmed | Reliability Evaluation and Prediction Method with Small Samples |
| title_short | Reliability Evaluation and Prediction Method with Small Samples |
| title_sort | reliability evaluation and prediction method with small samples |
| topic | reliability evaluation cox model logistic regression model reliability prediction |
| url | https://www.ijmems.in/article_detail.php?vid=8&issue_id=39&article_id=503 |
| work_keys_str_mv | AT hongyandui reliabilityevaluationandpredictionmethodwithsmallsamples AT xinghuidong reliabilityevaluationandpredictionmethodwithsmallsamples AT junyongtao reliabilityevaluationandpredictionmethodwithsmallsamples |