{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:28:35Z","timestamp":1783009715322,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T00:00:00Z","timestamp":1743292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Anomaly detection in oil and gas pipelines based on acoustic signals currently faces challenges, including limited anomalous samples, varying audio data distributions across different operating conditions, and interference from background noise. These challenges lead to reduced accuracy and efficiency in pipeline anomaly detection. The primary challenge in reconstruction-based pipeline audio anomaly detection is to prevent the loss of critical information and ensure the high-quality reconstruction of feature maps. This paper proposes a pipeline anomaly detection method termed Multi-scale Feature Fusion GANomaly with Dilated Neighborhood Attention. Firstly, to mitigate information loss during network deepening, a Multi-scale Feature Fusion module is proposed to merge the encoded and decoded feature maps at different dimensions, enhancing low-level detail and high-level semantic information. Secondly, a Dilated Neighborhood Attention module is introduced to assign varying weights to neighborhoods at various dilation rates, extracting channel interactions and spatial relationships between the current pixel and its neighborhoods. Finally, to enhance the quality of the reconstructed spectrum, a loss function based on the Structure Similarity Index Measure is designed, considering both pixel-level and structural differences to maintain the structural characteristics of the reconstructed spectrum. MFDNA-GANomaly achieved 92.06% AUC, 93.96% Accuracy, and 0.955 F1-score on the test set, demonstrating that the proposed method can effectively enhance pipeline anomaly detection performance. Additionally, MFDNA-GANomaly exhibited competitive performance on the ToyTrain and Bearing subsets of the development dataset in the DCASE Challenge 2023 Task 2, confirming the generalization capability of the model.<\/jats:p>","DOI":"10.3390\/info16040279","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T03:25:02Z","timestamp":1743391502000},"page":"279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Scale Feature Fusion GANomaly with Dilated Neighborhood Attention for Oil and Gas Pipeline Sound Anomaly Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2146-6044","authenticated-orcid":false,"given":"Yizhuo","family":"Zhang","sequence":"first","affiliation":[{"name":"Aliyun School of Big Data, School of Software, School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213159, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1619-9061","authenticated-orcid":false,"given":"Zhengfeng","family":"Sun","sequence":"additional","affiliation":[{"name":"Aliyun School of Big Data, School of Software, School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213159, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4081-1337","authenticated-orcid":false,"given":"Shen","family":"Shi","sequence":"additional","affiliation":[{"name":"Aliyun School of Big Data, School of Software, School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213159, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2942-4523","authenticated-orcid":false,"given":"Huiling","family":"Yu","sequence":"additional","affiliation":[{"name":"Aliyun School of Big Data, School of Software, School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213159, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117623","DOI":"10.1016\/j.apenergy.2021.117623","article-title":"Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model","volume":"303","author":"Li","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.tre.2018.01.010","article-title":"Network-based optimization modeling of manhole setting for pipeline transportation","volume":"113","author":"Chen","year":"2018","journal-title":"Transp. Res. E Logist. Transp. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"24644","DOI":"10.3390\/s150924644","article-title":"A location method using sensor arrays for continuous gas leakage in integrally stiffened plates based on the acoustic characteristics of the stiffener","volume":"15","author":"Bian","year":"2015","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1016\/j.petrol.2018.12.078","article-title":"An integrated detection and location model for leakages in liquid pipelines","volume":"175","author":"Liu","year":"2019","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"119720","DOI":"10.1016\/j.apenergy.2022.119720","article-title":"Frequency response function method for dynamic gas flow modeling and its application in pipeline system leakage diagnosis","volume":"324","author":"Li","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1080\/21642583.2020.1765218","article-title":"Pipeline signal feature extraction with improved VMD and multi-feature fusion","volume":"8","author":"Zhou","year":"2020","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.jlp.2015.05.006","article-title":"Technical analysis and research suggestions for long-distance oil pipeline leakage monitoring","volume":"35","author":"Liu","year":"2015","journal-title":"J. Loss Prev. Process Ind."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112691","DOI":"10.1016\/j.measurement.2023.112691","article-title":"Pipeline leak detection method based on acoustic-pressure information fusion","volume":"212","author":"Wang","year":"2023","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1016\/j.psep.2023.08.011","article-title":"Hybrid method for enhancing acoustic leak detection in water distribution systems: Integration of handcrafted features and deep learning approaches","volume":"177","author":"Wu","year":"2023","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neucom.2020.04.105","article-title":"An SAE-based resampling SVM ensemble learning paradigm for pipeline leakage detection","volume":"403","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108212","DOI":"10.1016\/j.asoc.2021.108212","article-title":"Novel leakage detection by ensemble 1DCNN-VAPSO-SVM in oil and gas pipeline systems","volume":"115","author":"Yang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"121975","DOI":"10.1016\/j.apenergy.2023.121975","article-title":"Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction","volume":"352","author":"Yao","year":"2023","journal-title":"Appl. Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3520013","DOI":"10.1109\/TIM.2023.3284940","article-title":"One-dimensional residual GANomaly network-based deep feature extraction model for complex industrial system fault detection","volume":"72","author":"Deng","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"195741","DOI":"10.1109\/ACCESS.2020.3034015","article-title":"A novel wireless network intrusion detection method based on adaptive synthetic sampling and an improved convolutional neural network","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1109\/TII.2023.3280246","article-title":"A semisupervised approach for industrial anomaly detection via self-adaptive clustering","volume":"20","author":"Ma","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_16","first-page":"221","article-title":"Xa-ganomaly: An explainable adaptive semi-supervised learning method for intrusion detection using ganomaly","volume":"76","author":"Han","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Akcay, S., Atapour-Abarghouei, A., and Breckon, T.P. (2018, January 2\u20136). GANomaly: Semi-supervised Anomaly Detection via Adversarial Training. Proceedings of the 14th Asian Conference on Computer Vision (ACCV), Perth, Australia.","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tagawa, Y., Maskeli\u016bnas, R., and Dama\u0161evi\u010dius, R. (2021). Acoustic anomaly detection of mechanical failures in noisy real-life factory environments. Electronics, 10.","DOI":"10.3390\/electronics10192329"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, S., Li, J., Ke, W., and Yin, H. (2024, January 14\u201319). Multi-Attention Enhanced Discriminator for GAN-Based Anomalous Sound Detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Republic of Korea.","DOI":"10.1109\/ICASSP48485.2024.10447924"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104813","DOI":"10.1016\/j.dsp.2024.104813","article-title":"A multi-scale dual-decoder autoencoder model for domain-shift machine sound anomaly detection","volume":"156","author":"Chen","year":"2024","journal-title":"Digit. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"80099","DOI":"10.1109\/ACCESS.2024.3409350","article-title":"A RES-GANomaly method for machine sound anomaly detection","volume":"12","author":"Huang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ak\u00e7ay, S., Atapour-Abarghouei, A., and Breckon, T.P. (2019, January 14\u201319). Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851808"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5841","DOI":"10.3233\/JIFS-230647","article-title":"Intelligent detection of foreign objects over coal flow based on improved GANomaly","volume":"46","author":"Wang","year":"2024","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dai, Y., Fan, F., and He, C. (2022). Anomaly Detection of GAN Industrial Image Based on Attention Feature Fusion. Sensors, 23.","DOI":"10.3390\/s23010355"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"122361","DOI":"10.1016\/j.eswa.2023.122361","article-title":"Industrial surface defect detection and localization using multi-scale information focusing and enhancement GANomaly","volume":"238","author":"Peng","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1177\/00405175221129654","article-title":"Attention-based Feature Fusion Generative Adversarial Network for yarn-dyed fabric defect detection","volume":"93","author":"Zhang","year":"2023","journal-title":"Text. Res. J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 14\u201319). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_28","unstructured":"Bergmann, P., L\u00f6we, S., Fauser, M., Sattlegger, D., and Steger, C. (2024, July 05). Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Available online: https:\/\/arxiv.org\/abs\/1807.02011."},{"key":"ref_29","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., and Langs, G. (2024, March 20). Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. Available online: https:\/\/arxiv.org\/abs\/1703.05921."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Purohit, H., Tanabe, R., Ichige, K., Endo, T., Nikaido, Y., Suefusa, K., and Kawaguchi, Y. (2019, January 25\u201330). MIMII dataset: Sound dataset for malfunctioning industrial machine investigation and inspection. Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019), New York, NY, USA.","DOI":"10.33682\/m76f-d618"},{"key":"ref_31","unstructured":"Zenati, H., Foo, C., Lecouat, B., Manek, G., and Chandrasekhar, V. (2024, February 17). Efficient Gan-Based Anomaly Detection. Available online: https:\/\/arxiv.org\/abs\/1802.06222."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jiang, A., Zhang, W., Deng, Y., Fan, P., and Liu, J. (2023, January 4\u201310). Unsupervised anomaly detection and localization of machine audio: A gan-based approach. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10096813"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"19501","DOI":"10.1007\/s11042-023-16317-6","article-title":"Memory enhancement method based on Skip-GANomaly for anomaly detection","volume":"83","author":"Jiang","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_34","unstructured":"Zhao, Z., Tan, Y., Qian, K., Xu, K., and Hu, B. (2024, June 10). Ensemble Systems with GAN and Auto-Encoder Models for Anomalous Sound Detection. Available online: https:\/\/dcase.community\/documents\/challenge2023\/technical_reports\/DCASE2023_QianXuHu_95_t2.pdf."},{"key":"ref_35","unstructured":"Fujimura, T., Kuroyanagi, I., Hayashi, T., and Toda, T. (2024, June 10). Anomalous Sound Detection by End-to-End Training of Outlier Exposure and Normalizing Flow with Domain Generalization Techniques. Available online: https:\/\/dcase.community\/documents\/challenge2023\/technical_reports\/DCASE2023_Fujimura_75_t2.pdf."},{"key":"ref_36","unstructured":"Wilkinghoff, K. (2024, June 10). Fraunhofer FKIE Submission for Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring. Available online: https:\/\/dcase.community\/documents\/challenge2023\/technical_reports\/DCASE2023_Wilkinghoff_4_t2.pdf?utm_source=chatgpt.com."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"134246","DOI":"10.1109\/ACCESS.2020.3006491","article-title":"Vibration signals analysis by explainable artificial intelligence (XAI) approach: Application on bearing faults diagnosis","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"57551","DOI":"10.1007\/s11042-023-17776-7","article-title":"Attention guided grad-CAM: An improved explainable artificial intelligence model for infrared breast cancer detection","volume":"83","author":"Raghavan","year":"2024","journal-title":"Multimed. Tools Appl."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/4\/279\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:05:53Z","timestamp":1760029553000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/4\/279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,30]]},"references-count":39,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["info16040279"],"URL":"https:\/\/doi.org\/10.3390\/info16040279","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,30]]}}}