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- W2531300144 abstract "On-line process monitoring schemes are designed to give early warnings of process faults. In the artificialintelligence and machine learning fields, reliable approaches have been utilized, such as kernel-based nonlineartechniques. This work presents a kernel-based empirical monitoring scheme with a small sample problem. Themeasurement data of normal operations are easy to collect, whereas special events or faults data are difficult to collect.In such situations, noise filtering techniques can be helpful in enhancing the process monitoring performance. Thiscan be achieved by the preprocessing of raw process data and eliminating unwanted variations of data. In this work,the performance of several monitoring schemes was demonstrated using three-dimensional batch process data. Theresults showed that the monitoring performance was improved significantly in terms of the detection success rate." @default.
- W2531300144 created "2016-10-21" @default.
- W2531300144 creator A5035008413 @default.
- W2531300144 date "2016-07-31" @default.
- W2531300144 modified "2023-09-25" @default.
- W2531300144 title "Empirical Process Monitoring Via On-line Analysis of Complex Process Measurement Data" @default.
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- W2531300144 doi "https://doi.org/10.5762/kais.2016.17.7.374" @default.
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