scholarly journals Multiple Elastic Networks With Time Delays for Early Fault Detection and Prognostics

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129387-129396
Author(s):  
Dongliang Guo ◽  
Wen Yang ◽  
Fengbo Tao ◽  
Bing Song ◽  
Hui Liu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Time Delays ◽  
Elastic Networks ◽  
Early Fault Detection

FAULT DETECTION IN MULTIRATE SAMPLED-DATA SYSTEMS WITH TIME-DELAYS

2002 ◽  
Vol 35 (1) ◽  
pp. 311-316 ◽  
Author(s):  
P. Zhang ◽  
S.X. Ding ◽  
G.Z. Wang ◽  
D.H. Zhou
Keyword(s):  
Fault Detection ◽  
Time Delays ◽  
Data Systems ◽  
Sampled Data

scholarly journals Fault Detection for Wireless Networked Control Systems with Stochastic Uncertainties and Multiple Time Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Lina Rong ◽  
Chengda Yu ◽  
Pengfei Guo ◽  
Hui Gao
Keyword(s):  
Fault Detection ◽  
Control Systems ◽  
Time Delays ◽  
Networked Control Systems ◽  
Networked Control ◽  
Linear Matrix ◽  
Multiple Time ◽  
Multiple Time Delays ◽  
System States ◽  
Stochastic Uncertainties

The fault detection problem for a class of wireless networked control systems is investigated. A Bernoulli distributed parameter is introduced in modeling the system dynamics; moreover, multiple time delays arising in the communication are taken into account. The detection observer for tracking the system states is designed, which generates both the state errors and the output errors. By adopting the linear matrix inequality method, a sufficient condition for the stability of wireless networked control systems with stochastic uncertainties and multiple time delays is proposed, and the gain of the fault detection observer is obtained. Finally, an illustrated example is provided to show that the observer designed in this paper tracks the system states well when there is no fault in the systems; however, when fault happens, the observer residual signal rises rapidly and the fault can be quickly detected, which demonstrate the effectiveness of the theoretical results.

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.

Adaptive Stochastic Resonance Method for Early Fault Detection of Rotating Machinery Based on Alpha Stable Distribution

2014 ◽  
Vol 618 ◽  
pp. 458-462
Author(s):  
Gang Yu ◽  
Ye Chen
Keyword(s):  
Fault Detection ◽  
Stochastic Resonance ◽  
Stable Distribution ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Rotating Machinery ◽  
Sliding Windows ◽  
Alpha Stable Distribution ◽  
Early Fault Detection ◽  
The Impact

This paper proposes an adaptive stochastic resonance (SR) method based on alpha stable distribution for early fault detection of rotating machinery. By analyzing the SR characteristic of the impact signal based on sliding windows, SR can improve the signal to noise ratio and is suitable for early fault detection of rotating machinery. Alpha stable distribution is an effective tool for characterizing impact signals, therefore parameter alpha can be used as the evaluating parameter of SR. Through simulation study, the effectiveness of the proposed method has been verified.

Statistical models application for early fault detection in offshore oil wells

2020 ◽  
Vol 20 (2020) ◽  
pp. 105-106
Author(s):  
Antonio Orestes de Salvo Castro ◽  
Mayara de Jesus Rocha Santos ◽  
Albino Lopes D'Almeida ◽  
Geraldo de Souza Ferreira ◽  
Gilson Brito Alves Lima ◽  
...  
Keyword(s):  
Fault Detection ◽  
Statistical Models ◽  
Oil Wells ◽  
Early Fault Detection ◽  
Offshore Oil
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