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

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4787
Author(s):  
Ruijun Guo ◽  
Guobin Zhang ◽  
Qian Zhang ◽  
Lei Zhou ◽  
Haicun Yu ◽  
...  
Keyword(s):  
Fault Detection ◽  
State Estimation ◽  
Power Plants ◽  
Similarity Index ◽  
Adaptive Strategy ◽  
Estimation Technique ◽  
Operating Characteristics ◽  
Model Accuracy ◽  
Auxiliary Equipment ◽  
Early Fault Detection

The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fired power plant to achieve the early fault detection of an ID fan. In addition, fault detection by using the model without an update was also compared. Results show that the update strategy can greatly improve the MSET model accuracy when predicting normal operations of the ID fan; accordingly, the fault can be detected more than 4 h earlier by using the strategy with the adaptive update when compared to the model without an update.

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

2021 ◽  
Author(s):  
Ruijun Guo ◽  
Guobin Zhang ◽  
Qian Zhang ◽  
Lei Zhou ◽  
Haicun Yu ◽  
...  
Keyword(s):  
Power Plant ◽  
Fault Detection ◽  
State Estimation ◽  
Thermal Power ◽  
Adaptive Strategy ◽  
Normal Operation ◽  
Efficient Production ◽  
Estimation Technique ◽  
Auxiliary Equipment ◽  
Early Fault Detection

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.

Development of an Early Fault Detection System for Nuclear Power Plants

1988 ◽  
Vol 21 (5) ◽  
pp. 93-98
Author(s):  
M. Lilja ◽  
T. Johansen ◽  
R.-E. Grini ◽  
Ø. Berg
Keyword(s):  
Fault Detection ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Detection System ◽  
Early Fault Detection

Early fault detection of bearings based on adaptive variational mode decomposition and local tangent space alignment

2019 ◽  
Vol 36 (2) ◽  
pp. 509-532 ◽  
Author(s):  
Ping Ma ◽  
Hongli Zhang ◽  
Wenhui Fan ◽  
Cong Wang
Keyword(s):  
Fault Detection ◽  
Tangent Space ◽  
Adaptive Strategy ◽  
Detection Accuracy ◽  
Variational Mode Decomposition ◽  
Decomposition Level ◽  
Content Type ◽  
Mode Decomposition ◽  
Early Fault Detection ◽  
Local Tangent

PurposeEarly fault detection of bearing plays an increasingly important role in the operation of rotating machinery. Based on the properties of early fault signal of bearing, this paper aims to describe a novel hybrid early fault detection method of bearings.Design/methodology/approachIn adaptive variational mode decomposition (AVMD), an adaptive strategy is proposed to select the optimal decomposition level K of variational mode decomposition. Then, a criterion based on envelope entropy is applied to select the optimal intrinsic mode functions (OIMF), which contains most useful fault information. Afterwards, local tangent space alignment (LTSA) is used to denoising of OIMF. The envelope spectrum of the OIMF is used to analyze the fault frequency, thereby detecting the fault. Experiments are conducted in a simulated signal and two experimental vibration signals of bearings to verify the effect of the new method.FindingsThe results show that the proposed method yields a good capability of detecting bearing fault at an early stage. The new method can extract more useful information and can reduce noise, which can provide better detection accuracy compared with the other two methods.Originality/valueAn adaptive strategy based on center frequency is proposed to select the optimal decomposition level of variational mode decomposition. Envelope entropy is used to fault feature selection. Combining the advantage of the AVMD-envelope entropy and LTSA, which suits the nature of the early fault signal. So, the proposed method has better detection accuracy, which provides a good alternative for early fault detection of bearings.

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