Fault detection and assessment for solid oxide fuel cell system gas supply unit based on novel principal component analysis

Wu, Xiaolong; Xu, Yuanwu; Zhao, Dongqi; Li, Zehua; Zhong, Xiaoliang; Chen, Meng-ting; Jiang, Jianhua; Deng, Zongquan; Fu, Xiaowei; Li, Xi

doi:10.1016/j.jpowsour.2019.226864

Cited by 18 publications

(8 citation statements)

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“…Costamagna et al [7] adapted the supervised classifier to the new conditions through domain adaptive statistics technology, solved the fault diagnosis under unexpected non-design operating conditions, and reduced the decrease in diagnostic performance. Wu et al [8] proposed a statistical method based on principal component analysis (PCA) and an exponentially weighted moving average control chart for SOFC system gas supply fault detection. Wu et al [9] developed a predictive controller to solve the thermal power surge problem and improved the energy conversion efficiency of the SOFC system.…”

Section: Of 13mentioning

confidence: 99%

Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven

Liu

2020

Energies

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The solid oxide fuel cell (SOFC) is a new energy technology that has the advantages of low emissions and high efficiency. However, oscillation and propagation often occur during the power generation of the system, which causes system performance degradation and reduced service life. To determine the root cause of multi-loop oscillation in an SOFC system, a data-driven diagnostic method is proposed in this paper. In our method, kernel principal component analysis (KPCA) and transfer entropy were applied to the system oscillation fault location. First, based on the KPCA method and the Oscillation Significance Index (OSI) of the system process variable, the process variables that were most affected by the oscillations were selected. Then, transfer entropy was used to quantitatively analyze the causal relationship between the oscillation variables and the oscillation propagation path, which determined the root cause of the oscillation. Finally, Granger causality (GC) analysis was used to verify the correctness of our method. The experimental results show that the proposed method can accurately and effectively locate the root cause of the SOFC system’s oscillation.

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Section: Of 13mentioning

confidence: 99%

Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven

Liu

2020

Energies

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“…ESR_a (mO) and ESR_b (mO) were measured using a type A circuit and type B circuit respectively. The parameters MCap, MESR_a, and MESR_b were derived using (14) and (15). Table 9.…”

Section: Data Detection Using Pca-based Indicators In Manufacturingmentioning

confidence: 99%

“…Moreover, PCA is considered as a novel technique for fault detection and diagnosis. [12][13][14] Wu et al 15 proposed a statistical method based on PCA for fault detection and the assessment of the gas supply in solid oxide fuel cell systems. They collected data for a real system and used the aforementioned method to conduct fault detection.…”

Section: Related Workmentioning

confidence: 99%

Dynamic weight-based learning method for data detection in manufacturing

Lin

2020

Advances in Mechanical Engineering

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In this study, a dynamic weight-based method combined with principal component analysis (PCA) was developed for the first time for detecting measurement data in manufacturing. This weight-based learning technique can learn and train the measurement data sequence to isolate incorrect data sources for achieving high accuracy when detecting various types of data. Research has revealed that unsuitable image or data features might cause poor performance in industrial inspections. In contrast to the previous inspection methods, the weight-based learning method proposed in this study employs a dynamic learning algorithm for effectively and adaptively selecting optimal principle components to the support vector machine (SVM) algorithm and then establishes indicators. Finally, these PCA-based indicators act as substitutes for massive amounts of data in data processing and can be applied to timely detect data when the data contain redundant and incorrect inputs in a sequence. The experimental results indicate that the proposed method, which combines dynamic weight-based feature extraction with PCA, can provide useful indicators for detecting various types of manufacturing data and exhibited satisfactory performance in the data detection.

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“…Although the sensor-set size can be optimized, usability and availability are also requested to be verified. The performance prediction and PHM capability are always the validation indexes [19,20]. Typical PHM procedure is illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Sensor development and optimization for a proton exchange membrane fuel cell system in automotive applications

Wang

Morando

Gaillard

et al. 2021

Journal of Power Sources

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Fault detection and assessment for solid oxide fuel cell system gas supply unit based on novel principal component analysis

Cited by 18 publications

References 50 publications

Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven

Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven

Dynamic weight-based learning method for data detection in manufacturing

Sensor development and optimization for a proton exchange membrane fuel cell system in automotive applications

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