{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T22:17:00Z","timestamp":1783203420469,"version":"3.54.6"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004835","name":"Zhejiang University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004835","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Reliability Engineering & System Safety"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.ress.2026.113083","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:17:08Z","timestamp":1782850628000},"page":"113083","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["NaFreBformer: Natural gas pipeline leakage forecasting using Nystr\u00f6m approximation Transformer without frequency bias"],"prefix":"10.1016","volume":"277","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0385-2925","authenticated-orcid":false,"given":"Xuguang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6283-1668","authenticated-orcid":false,"given":"Zhonglin","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3099-4803","authenticated-orcid":false,"given":"Hongke","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7055-7766","authenticated-orcid":false,"given":"Zheng","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ress.2026.113083_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.123482","article-title":"Wind electricity-hydrogen-natural gas coupling: An integrated optimization approach for enhancing wind energy accommodation and carbon reduction","volume":"369","author":"Yan","year":"2024","journal-title":"Appl Energy"},{"key":"10.1016\/j.ress.2026.113083_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.112118","article-title":"Learning to detect: Multimode temporal-spatial representation learning for leak detection of natural gas gathering pipelines","volume":"270","author":"Li","year":"2026","journal-title":"Reliab Eng Syst Saf"},{"issue":"6","key":"10.1016\/j.ress.2026.113083_b3","doi-asserted-by":"crossref","first-page":"6263","DOI":"10.1109\/TIE.2023.3294645","article-title":"Leak detection for natural gas gathering pipelines under multiple operating conditions using RP-1dConvLSTM-AE and multimodel decision","volume":"71","author":"Zuo","year":"2024","journal-title":"IEEE Trans Ind Electron"},{"issue":"D","key":"10.1016\/j.ress.2026.113083_b4","article-title":"Leak detection for natural gas gathering pipeline using spatio-temporal fusion of practical operation data","volume":"133","author":"Liang","year":"2024","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"10.1016\/j.ress.2026.113083_b5","first-page":"513","article-title":"Leak detection in natural gas pipelines based on unsupervised reconstruction of healthy flow data","volume":"38","author":"Liang","year":"2023","journal-title":"SPE Prod Oper"},{"key":"10.1016\/j.ress.2026.113083_b6","series-title":"ICASSP 2025-2025 IEEE international conference on acoustics, speech and signal processing","article-title":"Simple adaptive spectrum graph filters for rumor detection","author":"Yu","year":"2025"},{"key":"10.1016\/j.ress.2026.113083_b7","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2025.3556201","article-title":"A spatiotemporal data-driven framework for acoustic signal-based natural gas pipeline leak detection","volume":"74","author":"Yao","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"issue":"2","key":"10.1016\/j.ress.2026.113083_b8","doi-asserted-by":"crossref","first-page":"1950","DOI":"10.1109\/TII.2024.3495787","article-title":"Natural gas pipeline leak detection based on dual feature drift in acoustic signals","volume":"21","author":"Yao","year":"2025","journal-title":"IEEE Trans Ind Inform"},{"key":"10.1016\/j.ress.2026.113083_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.112038","article-title":"Intelligent prediction of flammable gas dispersion from urban gas pipeline leakage using physics-informed neural networks","volume":"268","author":"Li","year":"2026","journal-title":"Reliab Eng Syst Saf"},{"issue":"11","key":"10.1016\/j.ress.2026.113083_b10","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.14778\/3476249.3476258","article-title":"Butterfly-core community search over labeled graphs","volume":"14","author":"Dong","year":"2021","journal-title":"Proc VLDB Endow"},{"key":"10.1016\/j.ress.2026.113083_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.tust.2023.105367","article-title":"Detecting gas pipeline leaks in sandy soil with fiber-optic distributed acoustic sensing","volume":"141","author":"Chen","year":"2023","journal-title":"Tunn Undergr Space Technol"},{"key":"10.1016\/j.ress.2026.113083_b12","series-title":"Advances in neural information processing systems 30","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"issue":"2","key":"10.1016\/j.ress.2026.113083_b13","doi-asserted-by":"crossref","DOI":"10.1145\/3597022","article-title":"Contextualized knowledge graph embedding for explainable talent training course recommendation","volume":"42","author":"Yang","year":"2024","journal-title":"ACM Trans Inf Syst"},{"issue":"1","key":"10.1016\/j.ress.2026.113083_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104353","article-title":"Multi-resolution leak detection based on shared expert MoE forecasting for natural gas pipelines","volume":"63","author":"Li","year":"2026","journal-title":"Inf Process Manag"},{"key":"10.1016\/j.ress.2026.113083_b15","doi-asserted-by":"crossref","first-page":"3330","DOI":"10.1109\/LSP.2025.3599012","article-title":"Time series anomaly detection for natural gas pipeline leakage","volume":"32","author":"Li","year":"2025","journal-title":"IEEE Signal Process Lett"},{"key":"10.1016\/j.ress.2026.113083_b16","article-title":"Multiscale transformers with contrastive learning for UAV anomaly detection","volume":"74","author":"Hu","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2026.113083_b17","series-title":"Thirty-fifth AAAI conference on artificial intelligence, thirty-third conference on innovative applications of artificial intelligence and the eleventh symposium on educational advances in artificial intelligence","first-page":"11106","article-title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","volume":"vol. 35","author":"Zhou","year":"2021"},{"key":"10.1016\/j.ress.2026.113083_b18","unstructured":"Chen Peng, Zhang Yingying, Cheng Yunyao, Shu Yang, Wang Yihang, Wen Qingsong, Yang Bin, Guo Chenjuan. Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting. In: International conference on learning representations. ICLR, 2024."},{"key":"10.1016\/j.ress.2026.113083_b19","series-title":"Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"2400","article-title":"Fredformer: Frequency debiased transformer for time series forecasting","author":"Piao","year":"2024"},{"issue":"2","key":"10.1016\/j.ress.2026.113083_b20","doi-asserted-by":"crossref","DOI":"10.1002\/stc.2290","article-title":"Pipeline leakage identification and localization based on the fiber bragg grating hoop strain measurements and particle swarm optimization and support vector machine","volume":"26","author":"Jia","year":"2019","journal-title":"Struct Control Health Monit"},{"issue":"C","key":"10.1016\/j.ress.2026.113083_b21","article-title":"A dual SVM-based method for valve leakage fault identification with adaptive waveform extraction","volume":"182","author":"Lu","year":"2025","journal-title":"Eng Failure Anal"},{"key":"10.1016\/j.ress.2026.113083_b22","series-title":"Smart bird: Learnable sparse attention for efficient and effective transformer","author":"Wu","year":"2021"},{"key":"10.1016\/j.ress.2026.113083_b23","series-title":"Thirty-fifth AAAI conference on artificial intelligence, thirty-third conference on innovative applications of artificial intelligence and the eleventh symposium on educational advances in artificial intelligence","first-page":"14138","article-title":"Nystromformer: A nystrom-based algorithm for approximating self-attention","volume":"vol. 35","author":"Xiong","year":"2021"},{"issue":"B","key":"10.1016\/j.ress.2026.113083_b24","article-title":"Natural gas pipeline weak leakage detection based on negative pressure wave decomposition and feature enhancement","volume":"257","author":"Ye","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113083_b25","series-title":"2017 11th IEEE international conference on semantic computing","first-page":"156","article-title":"Querying intimate-core groups in weighted graphs","author":"Zheng","year":"2017"},{"key":"10.1016\/j.ress.2026.113083_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102424","article-title":"Localizing and tracking of in-pipe inspection robots based on distributed optical fiber sensing","volume":"60","author":"Zhu","year":"2024","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.113083_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107438","article-title":"Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network","volume":"209","author":"Liu","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113083_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.109051","article-title":"Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network","volume":"232","author":"Zhou","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"issue":"B","key":"10.1016\/j.ress.2026.113083_b29","article-title":"Research on multi-task leakage identification methods for gas drainage pipeline","volume":"267","author":"Ge","year":"2026","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113083_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109685","article-title":"Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines","volume":"241","author":"Li","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113083_b31","series-title":"2014 IEEE international conference on data mining","first-page":"350","article-title":"Social marketing meets targeted customers: A typical user selection and coverage perspective","author":"Liu","year":"2014"},{"issue":"7","key":"10.1016\/j.ress.2026.113083_b32","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1007\/s12665-011-1429-6","article-title":"Determination of main climate and ground factors controlling vegetation cover regrowth along oil and gas pipelines using multiple, spatial and geographically weighted regression procedures","volume":"66","author":"Bayramov","year":"2012","journal-title":"Environ Earth Sci"},{"key":"10.1016\/j.ress.2026.113083_b33","series-title":"WSDM\u201922: Proceedings of the fifteenth ACM international conference on web search and data mining","first-page":"353","article-title":"Outside in: Market-aware heterogeneous graph neural network for employee turnover prediction","author":"Hang","year":"2022"},{"issue":"4","key":"10.1016\/j.ress.2026.113083_b34","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.1109\/TKDE.2022.3140866","article-title":"MANE: Organizational network embedding with multiplex attentive neural networks","volume":"35","author":"Ye","year":"2023","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/j.ress.2026.113083_b35","series-title":"Advances in neural information processing systems 34","article-title":"Topic modeling revisited: A document graph-based neural network perspective","author":"Shen","year":"2021"},{"key":"10.1016\/j.ress.2026.113083_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2025.100535","article-title":"Transformer-based forecasting for high-frequency natural gas production data","volume":"21","author":"Ma","year":"2025","journal-title":"Energy AI"},{"key":"10.1016\/j.ress.2026.113083_b37","article-title":"Short-term natural gas load forecasting using a new informer framework based on temporal kolmogorov-arnold networks","author":"Qiao","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.ress.2026.113083_b38","doi-asserted-by":"crossref","first-page":"2542","DOI":"10.1109\/LSP.2022.3228131","article-title":"Fractional Fourier transform in time series prediction","volume":"29","author":"Koc","year":"2022","journal-title":"IEEE Signal Process Lett"},{"issue":"2","key":"10.1016\/j.ress.2026.113083_b39","doi-asserted-by":"crossref","DOI":"10.1145\/1883612.1883613","article-title":"Discrete wavelet transform-based time series analysis and mining","volume":"43","author":"Chaovalit","year":"2011","journal-title":"ACM Comput Surv"},{"key":"10.1016\/j.ress.2026.113083_b40","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ymssp.2017.06.005","article-title":"Wavelet-bounded empirical mode decomposition for measured time series analysis","volume":"99","author":"Moore","year":"2018","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.113083_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110824","article-title":"Anomaly detection for non-stationary rotating machinery based on signal transform and memory-guided multi-scale feature reconstruction","volume":"154","author":"Lu","year":"2025","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2026.113083_b42","series-title":"Proceedings of the 31st ACM SIGKDD conference on knowledge discovery and data mining v.2","first-page":"6206","article-title":"A survey on deep learning based time series analysis with frequency transformation","author":"Yi","year":"2025"},{"key":"10.1016\/j.ress.2026.113083_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2025.120020","article-title":"Revolving gate Fourier transform (RGFT): A novel time-frequency model for forecasting energy efficiency and CO2 neutrality","volume":"341","author":"Cheng","year":"2025","journal-title":"Energy Convers Manag"},{"issue":"6","key":"10.1016\/j.ress.2026.113083_b44","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1007\/s10033-017-0190-5","article-title":"Motor fault diagnosis based on short-time Fourier transform and convolutional neural network","volume":"30","author":"Wang","year":"2017","journal-title":"Chin J Mech Eng"},{"key":"10.1016\/j.ress.2026.113083_b45","series-title":"Frequency domain methods in recurrent neural networks for sequential data processing","author":"Wolter","year":"2021"},{"issue":"525","key":"10.1016\/j.ress.2026.113083_b46","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1080\/01621459.2017.1415908","article-title":"Adaptive Bayesian time-frequency analysis of multivariate time series","volume":"114","author":"Li","year":"2019","journal-title":"J Am Statist Assoc"},{"issue":"8","key":"10.1016\/j.ress.2026.113083_b47","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2106598119","article-title":"Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series","volume":"119","author":"Michau","year":"2022","journal-title":"Proc Natl Acad Sci USA"},{"key":"10.1016\/j.ress.2026.113083_b48","series-title":"International conference on machine learning, vol 162","article-title":"FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting","author":"Zhou","year":"2022"},{"issue":"1","key":"10.1016\/j.ress.2026.113083_b49","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/TIE.1983.356702","article-title":"An extension of parseval theorem and its use in calculating transient energy in the frequency-domain","volume":"30","author":"Kelkar","year":"1983","journal-title":"IEEE Trans Ind Electron"},{"key":"10.1016\/j.ress.2026.113083_b50","first-page":"2729","article-title":"Improving CUR matrix decomposition and the nystrom approximation via adaptive sampling","volume":"14","author":"Wang","year":"2013","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.ress.2026.113083_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.116751","article-title":"Ultra-rapid electrolyte leakage diagnosis for lithium-ion batteries based on ultrasonic guided waves","volume":"123","author":"Liu","year":"2025","journal-title":"J Energy Storage"},{"key":"10.1016\/j.ress.2026.113083_b52","series-title":"Thirty-fifth AAAI conference on artificial intelligence, thirty-third conference on innovative applications of artificial intelligence and the eleventh symposium on educational advances in artificial intelligence","first-page":"4027","article-title":"Graph neural network-based anomaly detection in multivariate time series","volume":"vol. 35","author":"Deng","year":"2021"},{"key":"10.1016\/j.ress.2026.113083_b53","series-title":"2023 3rd international conference on intelligent cybernetics technology & applications","first-page":"118","article-title":"Comparison of isolation forest and one class SVM in anomaly detection of gas pipeline operation","author":"Lukito","year":"2023"},{"key":"10.1016\/j.ress.2026.113083_b54","unstructured":"Nie Yuqi, H. Nguyen Nam, Sinthong Phanwadee, Kalagnanam Jayant. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. In: International conference on learning representations. 2023."},{"key":"10.1016\/j.ress.2026.113083_b55","unstructured":"Woo Gerald, Liu Chenghao, Kumar Akshat, Xiong Caiming, Savarese Silvio, Sahoo Doyen. Unified training of universal time series forecasting transformers, 2024. In: Forty-first international conference on machine learning. vol. 7, 2024."},{"key":"10.1016\/j.ress.2026.113083_b56","series-title":"The eleventh international conference on learning representations","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","author":"Wu","year":"2023"},{"key":"10.1016\/j.ress.2026.113083_b57","series-title":"Advances in neural information processing systems 36","article-title":"Frequency-domain MLPs are more effective learners in time series forecasting","author":"Yi","year":"2023"},{"key":"10.1016\/j.ress.2026.113083_b58","series-title":"International conference on machine learning","first-page":"79136","article-title":"KAN-AD: Time series anomaly detection with Kolmogorov-arnold networks","volume":"vol. 267","author":"Zhou","year":"2025"},{"issue":"12","key":"10.1016\/j.ress.2026.113083_b59","doi-asserted-by":"crossref","first-page":"9179","DOI":"10.1109\/JIOT.2021.3100509","article-title":"Learning graph structures with transformer for multivariate time-series anomaly detection in IoT","volume":"9","author":"Chen","year":"2022","journal-title":"IEEE Internet Things J"},{"key":"10.1016\/j.ress.2026.113083_b60","series-title":"Proceedings of the 63rd annual meeting of the association for computational linguistics, vol 1: long papers","first-page":"9840","article-title":"Attention entropy is a key factor: An analysis of parallel context encoding with full-attention-based pre-trained language models","author":"Zhang","year":"2025"}],"container-title":["Reliability Engineering & System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026008926?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026008926?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T21:54:11Z","timestamp":1783202051000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832026008926"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":60,"alternative-id":["S0951832026008926"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2026.113083","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"NaFreBformer: Natural gas pipeline leakage forecasting using Nystr\u00f6m approximation Transformer without frequency bias","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2026.113083","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113083"}}