Abstract
Condition monitoring of industrial processes can minimize downtime and maintenance costs while enhancing the safety of operation of plants and increasing the quality of products. Multivariate statistical methods are widely used for condition monitoring in industrial plants due to the rapid growth and advancement in data acquisition technology. However, the effectiveness of these methodologies in real industrial processes has not been fully investigated. This paper proposes a CVA-based approach for process fault identification, system modelling and performance estimation. The effectiveness of the proposed method was tested using data acquired from an operational industrial centrifugal compressor. The results indicate that CVA can be effectively used to identify abnormal operating conditions and predict performance degradation after the appearance of faults.
| Original language | English |
|---|---|
| Pages (from-to) | 247-257 |
| Journal | Lecture Notes in Mechanical Engineering |
| DOIs | |
| Publication status | Published - 25 Nov 2017 |
| Externally published | Yes |
Keywords
- Fault detection
- Performance estimation
- Canonical variable analysis
- Condition monitoring
- System identification