The Importance of Feature Descriptors in Identifying Fuel Types Using Machine Learning Models: An Ablation Study

Oil and gas industry operations are critical and laborious. Recent advances in artificial intelligence and machine learning (ML) have opened up a variety of Industry 4.0 applications in the oil and gas sector. This paper introduces the architecture of an automation system for identifying the fuel types in the downstream sectors. In this research, a real-time image dataset of fuel samples is collected and annotated with the corresponding class labels, i.e., petrol and diesel. The three core modules of the architecture are pre-processing, feature extraction and classification. In pre-processing, the input images are rescaled for spatial normalization. Then, discrete wavelet transform (DWT) is applied to extract approximate, vertical, horizontal and diagonal subimages. In the feature extraction module, the features from the DWT subimages are extracted to exploit the textural properties of the input images. This work is an ablation study of various individual image features and their combinations. During classification, the extracted features are modeled using six different ML models to provide a detailed study of the image features for identifying fuel types. To fine tune the ML models, the hyperparameters are adjusted using grid search and randomized search approaches. The results show that the extreme gradient boosting (XGB) model fine tuned with randomized search is the most effective classification model with an accuracy of 97.6% on the fuel classification task.