Fault detection and predictive maintenance in IoT-based building management system using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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Brac University
2022
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| Rochtain ar líne: | http://hdl.handle.net/10361/16595 |
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10361-165952022-05-11T21:01:32Z Fault detection and predictive maintenance in IoT-based building management system using machine learning Sabuj, Khaja Sheikh Imran Chowdhury, Saifullah Mehedi, Fahim Hasan Chakraborty, Pritam Arnob, Shafarse Simeon Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University IoT Fault detection Building systems FDD algorithms Predictive maintenance Internet of things Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-35). Infrastructures in the modern era are incorporating the Internet of Things (IoT) in everything from complex building automation systems (BAS) to individual small devices, emphasizing the importance of Predictive Maintenance. As a result, early fault detection is required, especially in sensitive and massive structures such as hospitals, industries, and multipurpose buildings. In such infrastructures, even minor failures can result in tragedies such as fires or slow down productivity. In our research, we have used several machine learning fault detection and diagnostics (FDD) algorithms in building fault detection data. We collected two datasets, MZVAV-1 (SET-A) and MZVAV-2-1 (SET-B), which were split into train-test sets to deploy LogisticRegression, KNearestNeighbours, Naive Bayes classifier, Support Vector Classifier, RandomForestClassifier, Decision Tree, MLP Classifier and Extra Tree Classifier. We achieved the highest accuracy of 98.91% using the Decision Tree classifier and the lowest accuracy of 14.17% from Naive Bayes classifier on the MZVAV-1 dataset. RandomForestClassifier and ExtraTree classifier outperformed all other algorithms with 99.91% accuracy on the MZVAV-2-1 dataset. Khaja Sheikh Imran Sabuj Saifullah Chowdhury Fahim Hasan Mehedi Pritam Chakraborty Shafarse Simeon Arnob B. Computer Science 2022-05-11T08:43:36Z 2022-05-11T08:43:36Z 2022 2022-01 Thesis ID 16201071 ID 17201057 ID 17201108 ID 21141083 ID 16101235 http://hdl.handle.net/10361/16595 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 35 pages application/pdf Brac University |
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Brac University |
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Institutional Repository |
| language |
English |
| topic |
IoT Fault detection Building systems FDD algorithms Predictive maintenance Internet of things Machine learning |
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IoT Fault detection Building systems FDD algorithms Predictive maintenance Internet of things Machine learning Sabuj, Khaja Sheikh Imran Chowdhury, Saifullah Mehedi, Fahim Hasan Chakraborty, Pritam Arnob, Shafarse Simeon Fault detection and predictive maintenance in IoT-based building management system using machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Sabuj, Khaja Sheikh Imran Chowdhury, Saifullah Mehedi, Fahim Hasan Chakraborty, Pritam Arnob, Shafarse Simeon |
| format |
Thesis |
| author |
Sabuj, Khaja Sheikh Imran Chowdhury, Saifullah Mehedi, Fahim Hasan Chakraborty, Pritam Arnob, Shafarse Simeon |
| author_sort |
Sabuj, Khaja Sheikh Imran |
| title |
Fault detection and predictive maintenance in IoT-based building management system using machine learning |
| title_short |
Fault detection and predictive maintenance in IoT-based building management system using machine learning |
| title_full |
Fault detection and predictive maintenance in IoT-based building management system using machine learning |
| title_fullStr |
Fault detection and predictive maintenance in IoT-based building management system using machine learning |
| title_full_unstemmed |
Fault detection and predictive maintenance in IoT-based building management system using machine learning |
| title_sort |
fault detection and predictive maintenance in iot-based building management system using machine learning |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/16595 |
| work_keys_str_mv |
AT sabujkhajasheikhimran faultdetectionandpredictivemaintenanceiniotbasedbuildingmanagementsystemusingmachinelearning AT chowdhurysaifullah faultdetectionandpredictivemaintenanceiniotbasedbuildingmanagementsystemusingmachinelearning AT mehedifahimhasan faultdetectionandpredictivemaintenanceiniotbasedbuildingmanagementsystemusingmachinelearning AT chakrabortypritam faultdetectionandpredictivemaintenanceiniotbasedbuildingmanagementsystemusingmachinelearning AT arnobshafarsesimeon faultdetectionandpredictivemaintenanceiniotbasedbuildingmanagementsystemusingmachinelearning |
| _version_ |
1814307648759136256 |