FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4376603885> ?p ?o ?g. }

• W4376603885 abstract "Abstract Accurate detection of possible machine failure allows manufacturers to identify potential fault situations in processes to avoid downtimes caused by unexpected tool wear or unacceptable workpiece quality. This paper aims to report the study of more than 20 fault detection models using Machine Learning (ML), Deep Learning (DL), and Deep Hybrid Learning (DHL). Predicting how the system could fail based on certain features or system settings (input variables) can help avoid future breakdowns and minimize downtime. The effectiveness of the proposed algorithms was experimented with a synthetic predictive maintenance dataset published by the School of Engineering of the University of Applied Sciences in Berlin, Germany. The fidelity of these algorithms was evaluated using performance measurement values such as accuracy, precision, recall, and the F-Score. Final results demonstrated that Deep Forest and Gradient Boosting algorithms had shown very high levels of average accuracy (exceeded 90%). Additionally, the Multinominal Logistic Regression and Long Short Term Memory based algorithms have shown satisfactory average accuracy (above 80%). Further analysis of models suggests that some models outperformed others. The research concluded that, through various ML, DL, and DHL algorithms, operational data analytics, and health monitoring system, engineers could optimize maintenance and reduce reliability risks." @default.
• W4376603885 created "2023-05-17" @default.
• W4376603885 creator A5004288882 @default.
• W4376603885 creator A5023319274 @default.
• W4376603885 creator A5035402206 @default.
• W4376603885 creator A5067889801 @default.
• W4376603885 date "2023-05-15" @default.
• W4376603885 modified "2023-10-11" @default.
• W4376603885 title "Using Machine Learning and Deep Learning Algorithms for Downtime Minimization in Manufacturing Systems: An Early Failure Detection Diagnostic Service" @default.
• W4376603885 cites W1409921370 @default.
• W4376603885 cites W1607420047 @default.
• W4376603885 cites W1678356000 @default.
• W4376603885 cites W1981000344 @default.
• W4376603885 cites W1982275278 @default.
• W4376603885 cites W1983291981 @default.
• W4376603885 cites W1988790447 @default.
• W4376603885 cites W2005783021 @default.
• W4376603885 cites W2022304150 @default.
• W4376603885 cites W2024046085 @default.
• W4376603885 cites W2028501442 @default.
• W4376603885 cites W2033201429 @default.
• W4376603885 cites W2042634436 @default.
• W4376603885 cites W2049279861 @default.
• W4376603885 cites W2055166298 @default.
• W4376603885 cites W2057024201 @default.
• W4376603885 cites W2064675550 @default.
• W4376603885 cites W2068424723 @default.
• W4376603885 cites W2089367555 @default.
• W4376603885 cites W2092229687 @default.
• W4376603885 cites W2140494000 @default.
• W4376603885 cites W2143253416 @default.
• W4376603885 cites W2153476503 @default.
• W4376603885 cites W2165556152 @default.
• W4376603885 cites W2166999901 @default.
• W4376603885 cites W2168949990 @default.
• W4376603885 cites W2195660543 @default.
• W4376603885 cites W2196723875 @default.
• W4376603885 cites W2222570178 @default.
• W4376603885 cites W2516153342 @default.
• W4376603885 cites W2539167253 @default.
• W4376603885 cites W2593519057 @default.
• W4376603885 cites W2681460984 @default.
• W4376603885 cites W2726977525 @default.
• W4376603885 cites W2740570963 @default.
• W4376603885 cites W2766043968 @default.
• W4376603885 cites W2771486828 @default.
• W4376603885 cites W2772084711 @default.
• W4376603885 cites W2775884586 @default.
• W4376603885 cites W2786624683 @default.
• W4376603885 cites W2789263499 @default.
• W4376603885 cites W2797844224 @default.
• W4376603885 cites W2799972844 @default.
• W4376603885 cites W2805330622 @default.
• W4376603885 cites W2810032703 @default.
• W4376603885 cites W2885232896 @default.
• W4376603885 cites W2893916405 @default.
• W4376603885 cites W2898426468 @default.
• W4376603885 cites W2899231837 @default.
• W4376603885 cites W2899922896 @default.
• W4376603885 cites W2900470244 @default.
• W4376603885 cites W2901389654 @default.
• W4376603885 cites W2910630325 @default.
• W4376603885 cites W2911713721 @default.
• W4376603885 cites W2911964244 @default.
• W4376603885 cites W2914019150 @default.
• W4376603885 cites W2916370975 @default.
• W4376603885 cites W2947583263 @default.
• W4376603885 cites W2982521432 @default.
• W4376603885 cites W3007602269 @default.
• W4376603885 cites W3007733503 @default.
• W4376603885 cites W3013163308 @default.
• W4376603885 cites W3014859868 @default.
• W4376603885 cites W3025333243 @default.
• W4376603885 cites W3045935064 @default.
• W4376603885 cites W3102476541 @default.
• W4376603885 cites W3108307025 @default.
• W4376603885 cites W3113082919 @default.
• W4376603885 cites W3130825936 @default.
• W4376603885 cites W3133798627 @default.
• W4376603885 cites W3156680434 @default.
• W4376603885 cites W3162074813 @default.
• W4376603885 cites W3170992401 @default.
• W4376603885 cites W3174959864 @default.
• W4376603885 cites W3196366242 @default.
• W4376603885 cites W3198542605 @default.
• W4376603885 cites W3200110661 @default.
• W4376603885 cites W3201941059 @default.
• W4376603885 cites W3209243216 @default.
• W4376603885 cites W3216464446 @default.
• W4376603885 cites W3217432596 @default.
• W4376603885 cites W4205460871 @default.
• W4376603885 cites W4205668793 @default.
• W4376603885 cites W4206133575 @default.
• W4376603885 cites W4210871516 @default.
• W4376603885 cites W4220877954 @default.
• W4376603885 cites W4230474071 @default.
• W4376603885 cites W4234451427 @default.
• W4376603885 cites W4249490807 @default.
• W4376603885 cites W4281394637 @default.
• W4376603885 cites W4285299373 @default.

• next