FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3098362015> ?p ?o ?g. }

• W3098362015 endingPage "642" @default.
• W3098362015 startingPage "629" @default.
• W3098362015 abstract "Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is a fundamental aspect for diagnosing the faults which saves both time and costs and in fact it avoids perilous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article analyses various machine learning approaches used for fault diagnosis of rotating equipment. In addition to this, a detailed study of different machine learning strategies which are incorporated on various rotating equipment in the context of fault diagnosis is also carried out. Mainly, the benefits and advance patterns of deep neural network which are applied to multiple components for fault diagnosis are inspected in this study. Finally, different algorithms are proposed to propagate the quality of fault diagnosis and the conceivable research ideas of applying machine learning approaches on various rotating equipment are condensed in this article." @default.
• W3098362015 created "2020-11-23" @default.
• W3098362015 creator A5005689965 @default.
• W3098362015 creator A5062168748 @default.
• W3098362015 date "2020-11-05" @default.
• W3098362015 modified "2023-10-06" @default.
• W3098362015 title "Fault diagnosis of various rotating equipment using machine learning approaches – A review" @default.
• W3098362015 cites W1534477342 @default.
• W3098362015 cites W1804462556 @default.
• W3098362015 cites W1965961968 @default.
• W3098362015 cites W2074724805 @default.
• W3098362015 cites W2092721384 @default.
• W3098362015 cites W2184192902 @default.
• W3098362015 cites W2219903032 @default.
• W3098362015 cites W2226126847 @default.
• W3098362015 cites W2244486986 @default.
• W3098362015 cites W2287029277 @default.
• W3098362015 cites W2336462153 @default.
• W3098362015 cites W2485614840 @default.
• W3098362015 cites W2553518610 @default.
• W3098362015 cites W2581853886 @default.
• W3098362015 cites W2583356199 @default.
• W3098362015 cites W2584994008 @default.
• W3098362015 cites W2593479727 @default.
• W3098362015 cites W2601590138 @default.
• W3098362015 cites W2610288532 @default.
• W3098362015 cites W2612554669 @default.
• W3098362015 cites W2616480351 @default.
• W3098362015 cites W2622302044 @default.
• W3098362015 cites W2737897717 @default.
• W3098362015 cites W2762841298 @default.
• W3098362015 cites W2766181795 @default.
• W3098362015 cites W2767761606 @default.
• W3098362015 cites W2767890277 @default.
• W3098362015 cites W2768753204 @default.
• W3098362015 cites W2791694051 @default.
• W3098362015 cites W2792098970 @default.
• W3098362015 cites W2802128760 @default.
• W3098362015 cites W2802130013 @default.
• W3098362015 cites W2855211234 @default.
• W3098362015 cites W2887767982 @default.
• W3098362015 cites W2887782657 @default.
• W3098362015 cites W2896882406 @default.
• W3098362015 cites W2897735448 @default.
• W3098362015 cites W2910549927 @default.
• W3098362015 cites W2911416839 @default.
• W3098362015 cites W2911418581 @default.
• W3098362015 cites W2912974502 @default.
• W3098362015 cites W2913381754 @default.
• W3098362015 cites W2915514405 @default.
• W3098362015 cites W2916505092 @default.
• W3098362015 cites W2919341207 @default.
• W3098362015 cites W2920611841 @default.
• W3098362015 cites W2921717016 @default.
• W3098362015 cites W2929571257 @default.
• W3098362015 cites W2931672078 @default.
• W3098362015 cites W2939053413 @default.
• W3098362015 cites W2939978363 @default.
• W3098362015 cites W2944049984 @default.
• W3098362015 cites W2947274212 @default.
• W3098362015 cites W2948704225 @default.
• W3098362015 cites W2950085316 @default.
• W3098362015 cites W2953889835 @default.
• W3098362015 cites W2954198433 @default.
• W3098362015 cites W2965040480 @default.
• W3098362015 cites W2972670180 @default.
• W3098362015 cites W2985226198 @default.
• W3098362015 cites W2990297053 @default.
• W3098362015 cites W2990790501 @default.
• W3098362015 cites W2990922688 @default.
• W3098362015 cites W2993960051 @default.
• W3098362015 cites W2995874909 @default.
• W3098362015 cites W2996072840 @default.
• W3098362015 cites W2999206154 @default.
• W3098362015 cites W3005789239 @default.
• W3098362015 cites W3009303282 @default.
• W3098362015 cites W3009992872 @default.
• W3098362015 cites W3012113763 @default.
• W3098362015 cites W3016277968 @default.
• W3098362015 cites W3036988671 @default.
• W3098362015 cites W3038465967 @default.
• W3098362015 doi "https://doi.org/10.1177/0954408920971976" @default.
• W3098362015 hasPublicationYear "2020" @default.
• W3098362015 type Work @default.
• W3098362015 sameAs 3098362015 @default.
• W3098362015 citedByCount "27" @default.
• W3098362015 countsByYear W30983620152021 @default.
• W3098362015 countsByYear W30983620152022 @default.
• W3098362015 countsByYear W30983620152023 @default.
• W3098362015 crossrefType "journal-article" @default.
• W3098362015 hasAuthorship W3098362015A5005689965 @default.
• W3098362015 hasAuthorship W3098362015A5062168748 @default.
• W3098362015 hasConcept C111472728 @default.
• W3098362015 hasConcept C116834253 @default.
• W3098362015 hasConcept C119857082 @default.
• W3098362015 hasConcept C121332964 @default.
• W3098362015 hasConcept C127313418 @default.
• W3098362015 hasConcept C127413603 @default.

• next