Optimal Strategy in Predicting Equipment Sensor Failure Using Genetic Programming and Histogram of Residual
Detecting sensor abnormality is challenging because the data are normally acquired using IoT approach and stored offline in a dedicated server (data logs). The objectives of this research are to device an approach to detect sensor abnormality and perform this in a ”white box” approach. In the propos...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/94363 |
| _version_ | 1848765860480548864 |
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| author | Wong, Pauline |
| author_facet | Wong, Pauline |
| author_sort | Wong, Pauline |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Detecting sensor abnormality is challenging because the data are normally acquired using IoT approach and stored offline in a dedicated server (data logs). The objectives of this research are to device an approach to detect sensor abnormality and perform this in a ”white box” approach. In the proposed approach, the compressor sensor output is modelled as a function of other sensors using static approach, comparing regression results of Genetic Programming (GP) with Multiple Linear Regression (MLR) and Neural Network Regression (ANN). Subsequently, the model output is used for detecting abnormality by observing the residuals. |
| first_indexed | 2025-11-14T11:41:58Z |
| format | Thesis |
| id | curtin-20.500.11937-94363 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:41:58Z |
| publishDate | 2023 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-943632024-02-16T06:39:37Z Optimal Strategy in Predicting Equipment Sensor Failure Using Genetic Programming and Histogram of Residual Wong, Pauline Detecting sensor abnormality is challenging because the data are normally acquired using IoT approach and stored offline in a dedicated server (data logs). The objectives of this research are to device an approach to detect sensor abnormality and perform this in a ”white box” approach. In the proposed approach, the compressor sensor output is modelled as a function of other sensors using static approach, comparing regression results of Genetic Programming (GP) with Multiple Linear Regression (MLR) and Neural Network Regression (ANN). Subsequently, the model output is used for detecting abnormality by observing the residuals. 2023 Thesis http://hdl.handle.net/20.500.11937/94363 Curtin University fulltext |
| spellingShingle | Wong, Pauline Optimal Strategy in Predicting Equipment Sensor Failure Using Genetic Programming and Histogram of Residual |
| title | Optimal Strategy in Predicting Equipment Sensor Failure
Using Genetic Programming and Histogram of Residual |
| title_full | Optimal Strategy in Predicting Equipment Sensor Failure
Using Genetic Programming and Histogram of Residual |
| title_fullStr | Optimal Strategy in Predicting Equipment Sensor Failure
Using Genetic Programming and Histogram of Residual |
| title_full_unstemmed | Optimal Strategy in Predicting Equipment Sensor Failure
Using Genetic Programming and Histogram of Residual |
| title_short | Optimal Strategy in Predicting Equipment Sensor Failure
Using Genetic Programming and Histogram of Residual |
| title_sort | optimal strategy in predicting equipment sensor failure
using genetic programming and histogram of residual |
| url | http://hdl.handle.net/20.500.11937/94363 |