Optimizing Pipeline Leak Detection: Leveraging Attention-based 1DCNN-BiLSTM for Enhanced Accuracy and Minimal False Alarms
| dc.contributor.advisor | Moshirpour, Mohammad | |
| dc.contributor.advisor | Far, Behrouz | |
| dc.contributor.author | Khazali, Sahar | |
| dc.contributor.committeemember | Drew, Steve | |
| dc.contributor.committeemember | Kawash, Jalal | |
| dc.date.accessioned | 2024-09-24T21:54:55Z | |
| dc.date.available | 2024-09-24T21:54:55Z | |
| dc.date.issued | 2024-09-20 | |
| dc.description.abstract | Pipelines are an essential infrastructure for the transportation of fluids and gases in many industries. Leaks in pipelines present significant environmental and economic concerns, making accurate and timely leak detection crucial. Recent advances in deep learning, particularly sequential models, have shown promising capabilities in anomaly detection for time series data. However, the challenge remains to detect leaks accurately while minimizing false alarms. This paper presents a novel approach combining the CB-AttentionNet model, which integrates a 1D convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and multi-head attention mechanisms to capture local and long time series dependencies. Additionally, we introduce a probabilistic search framework using Monte Carlo methods to optimize window sizes dynamically, addressing the limitations of fixed window sizes in handling variable-length sequential data. Experimental results demonstrate that our method performs better in terms of accuracy and reducing false positives across various simulations with industrial pipeline data. Optimized window sizes, particularly between 45 and 60 seconds, offer an effective balance between reducing misclassified leaks and maintaining high training accuracy. Furthermore, our analysis of resource usage and evaluation time shows that the model’s performance is efficient and manageable within typical operational constraints. | |
| dc.identifier.citation | Khazali, S. (2024). Optimizing pipeline leak detection: leveraging attention-based 1DCNN-BiLSTM for enhanced accuracy and minimal false alarms (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | |
| dc.identifier.uri | https://hdl.handle.net/1880/119884 | |
| dc.language.iso | en | |
| dc.publisher.faculty | Schulich School of Engineering | |
| dc.publisher.institution | University of Calgary | |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | Leak Detection | |
| dc.subject | Pipeline | |
| dc.subject | Deep Learning | |
| dc.subject | CNN | |
| dc.subject | LSTM | |
| dc.subject | Attention Mechanism | |
| dc.subject.classification | Engineering--Industrial | |
| dc.subject.classification | Engineering--Electronics and Electrical | |
| dc.title | Optimizing Pipeline Leak Detection: Leveraging Attention-based 1DCNN-BiLSTM for Enhanced Accuracy and Minimal False Alarms | |
| dc.type | master thesis | |
| thesis.degree.discipline | Engineering – Electrical & Computer | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Master of Science (MSc) | |
| ucalgary.thesis.accesssetbystudent | I require a thesis withhold – I need to delay the release of my thesis due to a patent application, and other reasons outlined in the link above. I have/will need to submit a thesis withhold application. |