{"id":"https://openalex.org/W7225770355","doi":"https://doi.org/10.1177/14759217261504351","title":"Edge-based explainable multisensor fault diagnosis for structural health monitoring","display_name":"Edge-based explainable multisensor fault diagnosis for structural health monitoring","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225770355","doi":"https://doi.org/10.1177/14759217261504351"},"language":"en","primary_location":{"id":"doi:10.1177/14759217261504351","doi":"https://doi.org/10.1177/14759217261504351","is_oa":false,"landing_page_url":"https://doi.org/10.1177/14759217261504351","pdf_url":null,"source":{"id":"https://openalex.org/S46950560","display_name":"Structural Health Monitoring","issn_l":"1475-9217","issn":["1475-9217","1741-3168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Structural Health Monitoring","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029456630","display_name":"Heba Al-Nasser","orcid":"https://orcid.org/0009-0000-6471-5919","observed_orcids":["https://orcid.org/0009-0000-6471-5919"]},"institutions":[{"id":"https://openalex.org/I884043246","display_name":"Hamburg University of Technology","ror":"https://ror.org/04bs1pb34","country_code":"DE","type":"education","lineage":["https://openalex.org/I884043246"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Heba Al-Nasser","raw_affiliation_strings":["Institute of Digital and Autonomous Construction, Hamburg University of Technology, Germany"],"raw_orcid":"https://orcid.org/0009-0000-6471-5919","affiliations":[{"raw_affiliation_string":"Institute of Digital and Autonomous Construction, Hamburg University of Technology, Germany","institution_ids":["https://openalex.org/I884043246"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085903459","display_name":"Kosmas Dragos","orcid":"https://orcid.org/0000-0002-4204-6547","observed_orcids":["https://orcid.org/0000-0002-4204-6547"]},"institutions":[{"id":"https://openalex.org/I884043246","display_name":"Hamburg University of Technology","ror":"https://ror.org/04bs1pb34","country_code":"DE","type":"education","lineage":["https://openalex.org/I884043246"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kosmas Dragos","raw_affiliation_strings":["Institute of Digital and Autonomous Construction, Hamburg University of Technology, Germany"],"raw_orcid":"https://orcid.org/0000-0002-4204-6547","affiliations":[{"raw_affiliation_string":"Institute of Digital and Autonomous Construction, Hamburg University of Technology, Germany","institution_ids":["https://openalex.org/I884043246"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087843977","display_name":"Kay Smarsly","orcid":"https://orcid.org/0000-0001-7228-3503","observed_orcids":["https://orcid.org/0000-0001-7228-3503"]},"institutions":[{"id":"https://openalex.org/I884043246","display_name":"Hamburg University of Technology","ror":"https://ror.org/04bs1pb34","country_code":"DE","type":"education","lineage":["https://openalex.org/I884043246"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kay Smarsly","raw_affiliation_strings":["Institute of Digital and Autonomous Construction, Hamburg University of Technology, Germany"],"raw_orcid":"https://orcid.org/0000-0001-7228-3503","affiliations":[{"raw_affiliation_string":"Institute of Digital and Autonomous Construction, Hamburg University of Technology, Germany","institution_ids":["https://openalex.org/I884043246"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5029456630"],"corresponding_institution_ids":["https://openalex.org/I884043246"],"apc_list":{"value":4400,"currency":"USD","value_usd":4400},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.002899999963119626,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.0015999999595806003,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5741999745368958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5547999739646912},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.5547000169754028},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.531499981880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48539999127388},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45739999413490295},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4327999949455261},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.41179999709129333},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.39070001244544983},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3822999894618988},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C2776247918","wikidata":"https://www.wikidata.org/wiki/Q1423713","display_name":"Structural health monitoring","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.3328999876976013},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.3208000063896179},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3131999969482422},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31209999322891235},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C176563091","wikidata":"https://www.wikidata.org/wiki/Q669238","display_name":"Intelligent sensor","level":3,"score":0.27489998936653137},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C139502532","wikidata":"https://www.wikidata.org/wiki/Q1122090","display_name":"Computational intelligence","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.2581000030040741}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1177/14759217261504351","doi":"https://doi.org/10.1177/14759217261504351","is_oa":false,"landing_page_url":"https://doi.org/10.1177/14759217261504351","pdf_url":null,"source":{"id":"https://openalex.org/S46950560","display_name":"Structural Health Monitoring","issn_l":"1475-9217","issn":["1475-9217","1741-3168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Structural Health Monitoring","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2081987572","https://openalex.org/W2157331557","https://openalex.org/W2170505850","https://openalex.org/W2240067561","https://openalex.org/W2319453305","https://openalex.org/W2911081702","https://openalex.org/W2950865323","https://openalex.org/W3009080359","https://openalex.org/W3024912007","https://openalex.org/W3089011435","https://openalex.org/W3133543405","https://openalex.org/W3153932301","https://openalex.org/W3208115705","https://openalex.org/W4292694365","https://openalex.org/W4321494862","https://openalex.org/W4321786089","https://openalex.org/W4366988523","https://openalex.org/W4385490601","https://openalex.org/W4386102234","https://openalex.org/W4388018712","https://openalex.org/W4392611940","https://openalex.org/W4398795533","https://openalex.org/W4400391447","https://openalex.org/W4400482035","https://openalex.org/W4401322707","https://openalex.org/W4404772818","https://openalex.org/W4406062843","https://openalex.org/W4412732281","https://openalex.org/W4414156569","https://openalex.org/W4417098962","https://openalex.org/W7118560413","https://openalex.org/W7140093327","https://openalex.org/W7151825361","https://openalex.org/W7171768624"],"related_works":[],"abstract_inverted_index":{"Structural":[0],"health":[1],"monitoring":[2,45],"(SHM)":[3],"systems":[4,336],"depend":[5],"on":[6,54,63,291,299],"reliable":[7],"sensor":[8,11,26,49,102,109,164,168,339],"measurements,":[9],"rendering":[10],"fault":[12,27,89,104,110,283,325],"diagnosis":[13,90],"essential":[14],"for":[15,87,184,216,226,281,319,330],"maintaining":[16],"data":[17,46],"quality":[18],"and":[19,51,66,73,103,113,178,190,206,237,258,272,294,343,346],"supporting":[20],"decision-making.":[21],"Deep":[22],"learning":[23],"enables":[24],"automated":[25],"diagnosis,":[28,326],"while":[29,69,265],"explainable":[30],"artificial":[31],"intelligence":[32],"(XAI)":[33],"improves":[34],"the":[35,100,161,167,171,207,217,227,243,262,300,309,320],"transparency":[36],"of":[37,159,209,261,303,333],"model":[38],"predictions.":[39],"In":[40],"parallel,":[41],"edge":[42,188,228],"computing":[43,304],"processes":[44],"closer":[47],"to":[48,129,277],"networks":[50],"reduces":[52],"dependence":[53],"centralized":[55,195,218],"systems.":[56],"However,":[57],"most":[58],"edge-based":[59,83,322],"SHM":[60,335],"studies":[61],"focus":[62],"prediction":[64,235,293],"performance":[65,236,274],"computational":[67,221],"feasibility,":[68],"agreement":[70,181],"between":[71,242],"predictions":[72],"explanations":[74],"remains":[75],"insufficiently":[76],"examined.":[77],"This":[78],"study":[79],"proposes":[80],"an":[81,185],"integrated":[82],"XAI-enabled":[84,323],"evaluation":[85,158],"methodology":[86],"multisensor":[88,131,324],"in":[91,170],"SHM.":[92],"Sensor":[93],"faults":[94],"are":[95,146,182,213,297],"formulated":[96],"as":[97],"classes":[98],"combining":[99],"faulty":[101],"type":[105],"(sensor-fault":[106],"classes),":[107],"enabling":[108],"detection,":[111],"isolation,":[112],"identification":[114],"within":[115,308],"a":[116,155,193,328],"single":[117],"classification":[118,273],"task.":[119],"An":[120],"established":[121],"gated":[122],"recurrent":[123],"unit-based":[124],"sequence-to-label":[125],"classifier":[126],"is":[127,154,223,275],"used":[128],"classify":[130],"time-series":[132],"windows":[133],"into":[134,148],"\u201cno-fault\u201d":[135],"or":[136],"\u201csensor-fault\u201d":[137],"classes.":[138,284],"Gradient":[139],"\u00d7":[140],"input":[141],"generates":[142],"time-step-level":[143],"attributions,":[144],"which":[145],"aggregated":[147],"sensor-level":[149,179,239],"attributions.":[150],"The":[151,230,285,314],"main":[152],"contribution":[153],"task-specific":[156],"quantitative":[157],"whether":[160],"dominant":[162],"attributed":[163],"agrees":[165],"with":[166,192,337],"encoded":[169],"predicted":[172],"class.":[173],"Prediction":[174],"performance,":[175],"explanation":[176,202,295],"faithfulness,":[177],"prediction\u2013explanation":[180,250],"evaluated":[183],"independently":[186],"trained":[187],"deployment":[189,196],"compared":[191],"reference":[194],"under":[197],"controlled":[198],"implementation":[199],"conditions.":[200,348],"Local":[201],"stability,":[203],"attribution-method":[204],"dependence,":[205],"influence":[208],"chronological":[210,278],"response":[211,279],"portions":[212,280],"additionally":[214],"examined":[215],"deployment,":[219],"whereas":[220],"feasibility":[222],"assessed":[224],"only":[225],"deployment.":[229],"results":[231],"show":[232],"broadly":[233],"comparable":[234],"aggregate":[238],"attribution":[240,270],"behavior":[241],"two":[244],"deployments,":[245],"although":[246],"neither":[247],"achieves":[248],"full":[249],"agreement.":[251],"Supplementary":[252],"analyses":[253],"indicate":[254],"moderate":[255],"local":[256],"faithfulness":[257],"high":[259],"stability":[260],"sensor-attribution":[263],"distributions,":[264],"dominant-sensor":[266],"selection":[267],"varies":[268],"among":[269],"methods":[271],"sensitive":[276],"specific":[282],"measured":[286],"mean":[287],"runtime":[288],"indicates":[289],"that,":[290],"average,":[292],"generation":[296],"completed":[298],"next":[301],"unit":[302],"(NUC)-based":[305],"edge-computing":[306],"node":[307],"selected":[310],"window":[311],"update":[312],"interval.":[313],"findings":[315],"provide":[316],"proof-of-concept":[317],"evidence":[318],"investigated":[321],"establishing":[327],"basis":[329],"further":[331],"development":[332],"decentralized":[334],"larger":[338],"networks,":[340],"different":[341],"structures,":[342],"varying":[344],"operating":[345],"environmental":[347]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}