Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

Version 1 : Received: 16 April 2021 / Approved: 19 April 2021 / Online: 19 April 2021 (14:36:56 CEST)

How to cite: Guo, R.; Zhang, G.; Zhang, Q.; Zhou, L.; Yu, H.; Li, M.; Lv, Y. An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique. Preprints 2021, 2021040499 (doi: 10.20944/preprints202104.0499.v1). Guo, R.; Zhang, G.; Zhang, Q.; Zhou, L.; Yu, H.; Li, M.; Lv, Y. An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique. Preprints 2021, 2021040499 (doi: 10.20944/preprints202104.0499.v1).

Abstract

The induced draft (ID) fan is important auxiliary equipment in the thermal power plant. It is of great significance to monitor the operation of the ID fan for safe and efficient production. In this paper, an adaptive warning model is proposed to detect early faults of ID fans. First, a non-parametric monitoring model is constructed to describe the normal operation states with the multivariate state estimation technique (MSET). Then, an early warning approach is presented to identify abnormal behaviors based on the results of the MSET model. As the performance of the MSET model is heavily influenced by the normal operation data in the historic memory matrix, an adaptive strategy is proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the model. The proposed method is applied to a 300 MW coal-fired power plant for early fault detection, and it is compared with the model without an update. Results show that the proposed method can detect the fault earlier and more accurately.

Subject Areas

fault detection; induced draft fan; multivariate state estimation technique (MSET); model update; power plant

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