PrismPatNet: Novel prism pattern network for accurate fault classification using engine sound signals
Article
Sahin, Sakir Engin, Gulhan, Gokhan, Barua, Prabal Datta, Tuncer, Turker, Dogan, Sengul, Faust, Oliver and Acharya, U. Rajendra. 2023. "PrismPatNet: Novel prism pattern network for accurate fault classification using engine sound signals." Expert Systems: the journal of knowledge engineering. 40 (8). https://doi.org/10.1111/exsy.13312
| Article Title | PrismPatNet: Novel prism pattern network for accurate fault classification using engine sound signals |
|---|---|
| ERA Journal ID | 17851 |
| Article Category | Article |
| Authors | Sahin, Sakir Engin, Gulhan, Gokhan, Barua, Prabal Datta, Tuncer, Turker, Dogan, Sengul, Faust, Oliver and Acharya, U. Rajendra |
| Journal Title | Expert Systems: the journal of knowledge engineering |
| Journal Citation | 40 (8) |
| Article Number | e13312 |
| Number of Pages | 19 |
| Year | 2023 |
| Publisher | John Wiley & Sons |
| Place of Publication | United Kingdom |
| ISSN | 0266-4720 |
| 1468-0394 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1111/exsy.13312 |
| Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1111/exsy.13312 |
| Abstract | Engines are prone to various types of faults, and it is crucial to detect and indeed classify them accurately. However, manual fault type detection is time-consuming and error-prone. Automated fault type detection promises to reduce inter- and intra-observer variability while ensuring time invariant attention during the observation duration. We have proposed an automated fault-type detection model based on sound signals to realize these advantageous properties. We have named the detection model prism pattern network (PrismPatNet) to reflect the fact that our design incorporates a novel feature extraction algorithm that was inspired by a 3D prism shape. Our prism pattern model achieves high accuracy with low-computational complexity. It consists of three main phases: (i) prism pattern inspired multilevel feature generation and maximum pooling operator, (ii) feature ranking and feature selection using neighbourhood component analysis (NCA), and (iii) support vector machine (SVM) based classification. The maximum pooling operator decomposes the sound signal into six levels. The proposed prism pattern algorithm extracts parameter values from both the signal itself and its decompositions. The generated parameter values are merged and fed to the NCA algorithm, which extracts 512 features from that input. The resulting feature vectors are passed on to the SVM classifier, which labels the input as belonging to 1 of 27 classes. We have validated our model with a newly collected dataset containing the sound of (1) a normal engine and (2) 26 different types of engine faults. Our model reached an accuracy of 99.19% and 98.75% using 80:20 hold-out validation and 10-fold cross-validation, respectively. Compared with previous studies, our model achieved the highest overall classification accuracy even though our model was tasked with identifying significantly more fault classes. This performance indicates that our PrismPatNet model is ready to be installed in real-world applications. |
| Keywords | engine fault classification; fault detection; machine learning; prism pattern; sound analysis; textural feature generation |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Malatya Turgut Ozal University, Turkiye |
| School of Business | |
| Cogninet Australia, Australia | |
| University of Technology Sydney | |
| Australian International Institute of Higher Education, Australia | |
| University of New England | |
| Taylor's University, Malaysia | |
| SRM Institute of Science and Technology, India | |
| Kumamoto University, Japan | |
| University of Sydney | |
| Firat University Hospital, Turkey | |
| Anglia Ruskin University, United Kingdom | |
| School of Mathematics, Physics and Computing |
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| Expert Systems - 2023 - Sahin - PrismPatNet Novel prism pattern network for accurate fault classification using engine.pdf | ||
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