Matches in SemOpenAlex for { <https://semopenalex.org/work/W2975595448> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W2975595448 abstract "Multivariate sensor data collected from manufacturing and process industries represents actual operational behavior and can be used for predictive maintenance of the plants. Anomaly detection and diagnosis, that forms an integral part of predictive maintenance, in industrial systems is however challenging due to their complex behavior, interactions among sensors, corrective actions of control systems and variability in anomalous behavior. While several statistical techniques for anomaly detection have been in use for a long time, these are not particularly suited for temporal (or contextual) anomalies that are characteristic of multivariate time series sensor data. On the other hand, several machine learning and deep learning techniques for anomaly detection gained significant interest in the recent years. Further, anomaly diagnosis that involves localization of the faults did not receive much attention. In this work, we compare the anomaly detection and diagnosis capabilities, in semi-supervised mode, of several statistical, machine learning and deep learning techniques on two systems viz. the interacting quadruple tank system and the continuous stirred tank reactor (CSTR) system both of which are representative of the complexity of large industrial systems. The techniques studied include principal component analysis (PCA), Mahalanobis distance (MD), one-class support vector machine (OCSVM), isolation forest, elliptic envelope, dense auto-encoder and long short term memory auto-encoder (LSTM AE). The study revealed that MD and LSTM-AE have the highest anomaly detection capability, followed closely by PCA and OCSVM. The above techniques also exhibited good diagnosis capability. The study indicates that statistical techniques in spite of their simplicity could be as powerful as machine learning and deep learning techniques, and may be considered for anomaly detection and diagnosis in manufacturing systems." @default.
- W2975595448 created "2019-10-03" @default.
- W2975595448 creator A5003429494 @default.
- W2975595448 creator A5009160097 @default.
- W2975595448 creator A5025036071 @default.
- W2975595448 creator A5027833891 @default.
- W2975595448 creator A5049610853 @default.
- W2975595448 creator A5083395137 @default.
- W2975595448 date "2019-09-22" @default.
- W2975595448 modified "2023-10-14" @default.
- W2975595448 title "Anomaly Detection and Diagnosis In Manufacturing Systems" @default.
- W2975595448 cites W2027178166 @default.
- W2975595448 cites W2056239473 @default.
- W2975595448 cites W2057130336 @default.
- W2975595448 cites W2106849258 @default.
- W2975595448 cites W2141741499 @default.
- W2975595448 cites W2150436340 @default.
- W2975595448 cites W2947369839 @default.
- W2975595448 cites W834036986 @default.
- W2975595448 doi "https://doi.org/10.36001/phmconf.2019.v11i1.815" @default.
- W2975595448 hasPublicationYear "2019" @default.
- W2975595448 type Work @default.
- W2975595448 sameAs 2975595448 @default.
- W2975595448 citedByCount "8" @default.
- W2975595448 countsByYear W29755954482021 @default.
- W2975595448 countsByYear W29755954482022 @default.
- W2975595448 countsByYear W29755954482023 @default.
- W2975595448 crossrefType "journal-article" @default.
- W2975595448 hasAuthorship W2975595448A5003429494 @default.
- W2975595448 hasAuthorship W2975595448A5009160097 @default.
- W2975595448 hasAuthorship W2975595448A5025036071 @default.
- W2975595448 hasAuthorship W2975595448A5027833891 @default.
- W2975595448 hasAuthorship W2975595448A5049610853 @default.
- W2975595448 hasAuthorship W2975595448A5083395137 @default.
- W2975595448 hasBestOaLocation W29755954481 @default.
- W2975595448 hasConcept C119857082 @default.
- W2975595448 hasConcept C121332964 @default.
- W2975595448 hasConcept C12267149 @default.
- W2975595448 hasConcept C124101348 @default.
- W2975595448 hasConcept C12997251 @default.
- W2975595448 hasConcept C153180895 @default.
- W2975595448 hasConcept C154945302 @default.
- W2975595448 hasConcept C1921717 @default.
- W2975595448 hasConcept C26873012 @default.
- W2975595448 hasConcept C41008148 @default.
- W2975595448 hasConcept C739882 @default.
- W2975595448 hasConceptScore W2975595448C119857082 @default.
- W2975595448 hasConceptScore W2975595448C121332964 @default.
- W2975595448 hasConceptScore W2975595448C12267149 @default.
- W2975595448 hasConceptScore W2975595448C124101348 @default.
- W2975595448 hasConceptScore W2975595448C12997251 @default.
- W2975595448 hasConceptScore W2975595448C153180895 @default.
- W2975595448 hasConceptScore W2975595448C154945302 @default.
- W2975595448 hasConceptScore W2975595448C1921717 @default.
- W2975595448 hasConceptScore W2975595448C26873012 @default.
- W2975595448 hasConceptScore W2975595448C41008148 @default.
- W2975595448 hasConceptScore W2975595448C739882 @default.
- W2975595448 hasIssue "1" @default.
- W2975595448 hasLocation W29755954481 @default.
- W2975595448 hasOpenAccess W2975595448 @default.
- W2975595448 hasPrimaryLocation W29755954481 @default.
- W2975595448 hasRelatedWork W1550515741 @default.
- W2975595448 hasRelatedWork W1965502310 @default.
- W2975595448 hasRelatedWork W2041399278 @default.
- W2975595448 hasRelatedWork W2056016498 @default.
- W2975595448 hasRelatedWork W2136184105 @default.
- W2975595448 hasRelatedWork W2160451891 @default.
- W2975595448 hasRelatedWork W2336974148 @default.
- W2975595448 hasRelatedWork W3013515612 @default.
- W2975595448 hasRelatedWork W2187500075 @default.
- W2975595448 hasRelatedWork W2345184372 @default.
- W2975595448 hasVolume "11" @default.
- W2975595448 isParatext "false" @default.
- W2975595448 isRetracted "false" @default.
- W2975595448 magId "2975595448" @default.
- W2975595448 workType "article" @default.