Adaptive Early Warning Method Based on Similar Proportion and Probability Model
| Autor: | Wei Dai, Meihua Shi, Weifang Zhang, Yazhou Li, Tingting Huang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
adaptive threshold Computer science Kernel density estimation 02 engineering and technology fault early warning lcsh:Technology Constant false alarm rate lcsh:Chemistry ALARM 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Range (statistics) General Materials Science lcsh:QH301-705.5 Instrumentation Independence (probability theory) Fluid Flow and Transfer Processes Warning system lcsh:T Process Chemistry and Technology 020208 electrical & electronic engineering General Engineering similar proportion Division (mathematics) lcsh:QC1-999 Computer Science Applications Reliability engineering lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 State (computer science) kernel density estimation lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences Volume 10 Issue 12 Applied Sciences, Vol 10, Iss 4278, p 4278 (2020) |
| ISSN: | 2076-3417 |
| Popis: | This paper presents a multi-state adaptive early warning method for mechanical equipment and proposes an adaptive dynamic update model of the equipment alarm threshold based on a similar proportion and state probability model. Based on the similarity of historical equipment, the initial thresholds of different health states of equipment can be determined. The equipment status is divided into four categories and analyzed, which can better represent its status and provide more detailed and reasonable guidance. The obtained dynamic alarm lines at all levels can regulate the operation range of equipment in the different health states. Compared to the traditional method of a fixed threshold, this method can effectively reduce the number of false alarms and attains a higher prediction accuracy, which demonstrates its effectiveness and superiority. Finally, the method was verified by means of lifetime data of a rolling bearings. The results show that the model improves the timely detection of the abnormal state of the equipment, greatly reduces the false alarm rate, and even overcomes the limitation of independence between the fixed threshold method and equipment state. Moreover, multi-state division can accurately diagnose the current equipment state, which should be considered in maintenance decision-making. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|