Multivariate statistical signal processing technique for fault detection and diagnostics
| Autor: | O. Glockler, J. Eklund, B.R. Upadhyaya |
|---|---|
| Rok vydání: | 1990 |
| Předmět: |
Multivariate statistics
Signal processing Engineering business.industry Applied Mathematics Process (computing) Pattern recognition Fault detection and isolation Computer Science Applications Multidimensional signal processing Autoregressive model Control and Systems Engineering Frequency domain Electronic engineering Artificial intelligence Electrical and Electronic Engineering business Instrumentation Digital signal processing |
| Zdroj: | ISA Transactions. 29:79-95 |
| ISSN: | 0019-0578 |
| DOI: | 10.1016/0019-0578(90)90045-m |
| Popis: | The normal fluctuation of wideband signals in process industry systems exhibits behavior that is characteristic of process dynamics, sensor dynamics, vibration of components, and product quality. A baseline statistical signature behavior can be established by a systematic processing of multivariate signals and determining the cause and effect relationship among the process variables characterizing a subsystem. Both theoretical and computational basis for processing a set of signals using the multivariate autoregression (MAR) modeling has been developed and applied to establish frequency domain statistical signatures for an aluminum rolling mill. A systematic procedure is developed to interpret the causal relationships for the detection and isolation of process anomalies and sensor maloperation. This digital signal processing technique and its implementation have clearly demonstrated the applicability of this method of characterizing and monitoring complex industrial processes. |
| Databáze: | OpenAIRE |
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