Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network
This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, va...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/73855/ http://psasir.upm.edu.my/id/eprint/73855/1/Self-calibration%20algorithm%20for%20a%20pressure%20sensor%20with%20a%20real-time%20approach%20based%20on%20an%20artificial%20neural%20network.pdf |
| _version_ | 1848857380023959552 |
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| author | M. Almassri, Ahmed M. Wan Hasan, Wan Zuha Ahmad, Siti Anom Shafie, Suhaidi Wada, Chikamune Horio, Keiichi |
| author_facet | M. Almassri, Ahmed M. Wan Hasan, Wan Zuha Ahmad, Siti Anom Shafie, Suhaidi Wada, Chikamune Horio, Keiichi |
| author_sort | M. Almassri, Ahmed M. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment. |
| first_indexed | 2025-11-15T11:56:37Z |
| format | Article |
| id | upm-73855 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:56:37Z |
| publishDate | 2018 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-738552020-05-06T18:57:17Z http://psasir.upm.edu.my/id/eprint/73855/ Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network M. Almassri, Ahmed M. Wan Hasan, Wan Zuha Ahmad, Siti Anom Shafie, Suhaidi Wada, Chikamune Horio, Keiichi This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment. MDPI 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/73855/1/Self-calibration%20algorithm%20for%20a%20pressure%20sensor%20with%20a%20real-time%20approach%20based%20on%20an%20artificial%20neural%20network.pdf M. Almassri, Ahmed M. and Wan Hasan, Wan Zuha and Ahmad, Siti Anom and Shafie, Suhaidi and Wada, Chikamune and Horio, Keiichi (2018) Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network. Sensors, 18. pp. 1-16. ISSN 1424-8220; ESSN: 1424-8220 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111596/ 10.3390/s18082561 |
| spellingShingle | M. Almassri, Ahmed M. Wan Hasan, Wan Zuha Ahmad, Siti Anom Shafie, Suhaidi Wada, Chikamune Horio, Keiichi Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network |
| title | Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network |
| title_full | Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network |
| title_fullStr | Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network |
| title_full_unstemmed | Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network |
| title_short | Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network |
| title_sort | self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network |
| url | http://psasir.upm.edu.my/id/eprint/73855/ http://psasir.upm.edu.my/id/eprint/73855/ http://psasir.upm.edu.my/id/eprint/73855/ http://psasir.upm.edu.my/id/eprint/73855/1/Self-calibration%20algorithm%20for%20a%20pressure%20sensor%20with%20a%20real-time%20approach%20based%20on%20an%20artificial%20neural%20network.pdf |