Development and Enhancement of Data-Driven Methods for Fuel Loss Detection Using Deep Learning
Creators: name: Chu, Ruimin
Publisher: name: RMIT University
Year: 2025
Subjects: classificationCode: 461103
subject: Deep learning

description: This thesis focuses on developing and improving data-driven methods for detecting fuel loss issues, leveraging Deep Learning (DL) to automatically extract features from complex sequential data. Ensuring the integrity of petroleum product storage at service stations is important, as fuel loss may pose significant economic and environmental consequences at service stations. Despite the growing deployment of sensors at service stations that provide abundant data, these resources remain underutilised in advancing data-driven detection methods. These methods identify fuel loss issues by actively monitoring sensor data, offering the advantage of remote online operation and reduced costs. Manual analysis of large datasets is impractical, especially with challenges such as noise in real-world data and variability in tank conditions. Automated tools for feature extraction are therefore essential for enhancing efficiency and accuracy. Machine Learning (ML), particularly DL, is a key technology to overcome the above challenges and revolutionise fuel loss detection techniques, given its success in extracting valuable insights from complex and large-scale data. 

The first contribution of this thesis is the development of the first data-driven method for meter error detection. Meter error is a critical issue in fuel loss, yet its detection traditionally relies on infrequent physical inspections, which can leave issues undetected for long periods. To overcome this, we propose a framework that uses data available at service stations to enable offsite detection that can be performed at any time, eliminating the need for onsite visits. Change Point Detection (CPD) is applied to analyse the sequences of key indicators related to meter error, with Long Short-Term Memory-Variational Autoencoder (LSTM-VAE) employed for representation learning to facilitate accurate detection. Experiments show the framework’s effectiveness in detecting meter errors and its potential for broader CPD applications. Overall, the proposed approach offers a more efficient and timely solution for identifying meter errors.

The second part of this research focuses on developing a real-time fuel leakage detection method. Fuel leakage is a significant concern for site operators, desiring early detection to minimise its consequent impacts. However, existing data-driven studies perform tests based on daily data collected over weeks, leading to detection delays. To address this, we introduce a novel memory-based online CPD method that continuously monitors and processes real-time fuel variance data for early detection. This method maintains a size-constrained memory of representative historical data and supports both statistical techniques and ML methods to measure shifts in fuel variance data over time. Experiments with simulated fuel leakage data and benchmark CPD datasets demonstrate the method's effectiveness in detection accuracy. It also achieves a much shorter detection delay compared to the general turnaround time of fuel leakage detection in practice. In summary, this real-time approach offers a more responsive solution for detecting fuel leaks, enhancing operational efficiency and safety.

The final contribution of this thesis is the development of an explainable fuel leakage detection method. Apart from real-time detection, explainability is another major limitation in current data-driven fuel leakage detection studies. False alarms are common in practice, so providing practitioners with explanations or interpretations can greatly assist in their result validation, especially when the systems are built with complex algorithms whose decision-making processes are opaque. Explainability helps build user trust and aids in verifying the system’s reliability. Therefore, we propose the first explainable fuel leakage detection approach in the field, which combines a high-performance DL model for accurate online detection with a transparent ML model with great interpretability to generate clear textual explanations. Through case studies, we demonstrate the method’s ability to generate intuitive explanations using linguistic terms to describe the changes in features indicative of fuel leakage. The approach also maintains accurate early detection, as shown through experiments with real-world fuel data with induced leakage. This work highlights the importance of ensuring both accuracy and explainability to improve fuel leakage detection and foster user confidence.