Performance-based fault detection and fault-tolerant control for automatic control systems

Li, Linlin; Luo, Hao; Ding, Steven X.; Chen, Xiaolu; Peng, Kaixiang

doi:10.1016/j.automatica.2018.10.047

Cited by 134 publications

(65 citation statements)

References 16 publications

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“…Machine learning has been widely used in many industrial diagnosis fields. More and more attention has been paid to the fault diagnosis methods based on machine learning [9,10]. In machine learning, the boost algorithm combines weakly predictive models into a strongly predictive model, which is adjusted by increasing the weight of the error samples to improve the accuracy of the algorithm [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

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It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate.

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Section: Introductionmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

Self Cite

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“…It is applied in weakly stochastic environments using a learning of the YK-parameter Q , the effectiveness of the proposed method is demonstrated in simulation of a DC motor. In Li, Luo, Ding, Yang, and Peng (2019) YK parametrization is applied in the case of a non-linear faulty system. A fault detection scheme is investigated to estimate and detect the stability performance degradation.…”

Section: Applicationsmentioning

confidence: 99%

Advances in Youla-Kucera parametrization: A Review

Mahtout

Navas

Milanés

et al. 2020

Annual Reviews in Control

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Youla-Kucera (YK) parametrization was formulated decades ago for obtaining the set of controllers stabilizing a linear plant. This fundamental result of control theory has been used to develop theoretical tools solving many control problems ranging from stable controller switching, closed-loop identification, robust control, disturbance rejection, adaptive control to fault tolerant control. This paper collects the recent work and classifies them according to the use of YK parametrization, Dual YK parametrization or both, providing the latest advances with main applications in different control fields. A final discussion gives some insights on the future trends in the field.

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“…Due to the improvement of product quality and safety in modern industrial processes, industrial process monitoring has become a hot topic of research [1][2][3][4][5] . As the continuous development of modern data collection and storage technology, the types and quantities of industrial process data have been greatly improved, and industrial processes have entered the era of big data.…”

Section: Introductionmentioning

confidence: 99%

Plant-Wide Process Monitoring Strategy Based on Complex Network and Bayesian Inference-Based Multi-Block Principal Component Analysis

Peng

Tian

2020

IEEE Access

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In this work, a novel process monitoring method in a block-wised partitioning manor is proposed for plant-wide processes which can be partitioned into several sub-blocks and monitored parallelly. The focus of this method is to reduce the high complexity of global plant-wide process, while to improve the efficiency of local feature extraction. In this method, considering that not all process knowledge is available in the block division process strategy, a novel community discovery (CD) algorithms, based on the similarity of neighbor node weighted Louvain, is introduced into the framework of the multi-block Bayesian inference and principal component analysis (PCA) based plant-wide process monitoring scheme. Firstly, the complex network (CN) theory is used to establish the network topology structure for the global variables of the plant-wide process. Secondly, by analyzing the graph characteristic structure, considering the connection strength between nodes, a more reasonable sub-block division is conducted according to the improved Louvain algorithm. Then, PCA method is used to establish process monitoring model for each sub-block to obtain sub-block monitoring statistics. Finally, the total joint statistics is obtained through Bayesian inference for fault detection. The feasibility and effectiveness, in terms of the detection performance, of this method are demonstrated in a simulated plant-wide process by compared with other state-of-the-art PCA based monitoring methods.

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Performance-based fault detection and fault-tolerant control for automatic control systems

Cited by 134 publications

References 16 publications

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

Advances in Youla-Kucera parametrization: A Review

Plant-Wide Process Monitoring Strategy Based on Complex Network and Bayesian Inference-Based Multi-Block Principal Component Analysis

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