A discrete hidden Markov model fault diagnosis strategy based on K-means clustering dedicated to PEM fuel cell systems of tramways

Liu, Jiawei; Li, Qi; Chen, Weirong; Cao, Taiqiang

doi:10.1016/j.ijhydene.2018.04.163

Cited by 82 publications

(27 citation statements)

References 48 publications

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“…On the premise of ensuring reliability, the higher the average field strength is, the lower the volume and weight cost of the corresponding capacitor is and the higher the level of the capacitor is; conversely, in order to ensure the safety and reliability of the capacitor, there is a method that requires strict control of the average field strength not greater than a certain value. is method only focuses on the impact of the average field strength and ignores the impact of the maximum field strength at the edge of the plate [13].…”

Section: Related Relationshipmentioning

confidence: 99%

Fault Diagnosis and Identification of Power Capacitor Based on Edge Cloud Computing and Deep Learning

Zhao

Zhang

Ren

2020

Mathematical Problems in Engineering

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Nowadays, power electronic technology is widely affecting people’s daily work and life. However, there are still many problems in the current power supply research. When the fault information of power transformer is not complete or there is some ambiguity or even the information is lost, it will largely lead to the conclusion and correct conclusion of fault diagnosis. In this case, the fuzzy theory is applied to the fault diagnosis of shunt capacitor, and the fuzzy fault diagnosis system of shunt capacitor is studied. At the same time, a map-based fault diagnosis system is proposed. In this paper, the cloud computing technology is introduced into the deep learning and compared with SVM and DBN algorithm. The research results of this paper show that the accuracy of fuzzy diagnosis results is 94%, 84%, 90%, 80%, 83%, and 70%, respectively, which shows that the model diagnosis reliability is relatively high. Among the three algorithms, MR-DBN overall detection rate is higher and the time-consuming is lower than the other two methods. The diagnostic accuracy and misjudgment rate of DBN are as follows: 96.33% and 3.90%. The diagnosis accuracy and misjudgment rate of SVM are as follows: 96.40% and 3.83%. The diagnostic accuracy and misjudgment rate of MR-DBN are, respectively, 99.52% and 0.57%. Compared with the other two methods, MR-DBN has the highest diagnostic accuracy and the lowest error rate, which to a large extent indicates that MR-DBN algorithm has higher diagnostic accuracy and has greater advantages and reliability in power supply diagnosis and identification. It not only improves the accuracy of power capacitor fault diagnosis and identification but also provides a new method for the application of power capacitor fault research and development.

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Section: Related Relationshipmentioning

confidence: 99%

Fault Diagnosis and Identification of Power Capacitor Based on Edge Cloud Computing and Deep Learning

Zhao

Zhang

Ren

2020

Mathematical Problems in Engineering

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“…An adaptive-neuro-fuzzy-inference-system-based maximum-power-point-tracking controller is designed [33] for a proton exchange membrane fuel cell system used in electric vehicle applications. Fault classification of PEMFCs that are used in trams is achieved by employing a hidden Markov model (HMM) approach (e.g., [25]) in [34] where K-means clustering (e.g., [24]) is used to eliminate singular data points as part of the preprocessing step before the HMM is applied; classification results produced by the HMM are shown to be better than those produced by the support vector machine method. Deep learning is applied [35] to sequence fault diagnosis of a PEMFC water management subsystem.…”

Section: Introductionmentioning

confidence: 99%

Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm

Chakraborty

2019

Energies

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Fuel cell stack configuration optimization is known to be a problem that, in addition to presenting engineering challenges, is computationally hard. This paper presents an improved computational heuristic for solving the problem. The problem addressed in this paper is one of constrained optimization, where the goal is to seek optimal (or near-optimal) values of (i) the number of proton exchange membrane fuel cells (PEMFCs) to be connected in series to form a group, (ii) the number of such groups to be connected in parallel, and (iii) the cell area, such that the PEMFC assembly delivers the rated voltage at the rated power while the cost of building the assembly is as low as possible. Simulation results show that the proposed method outperforms four of the best-known methods in the literature. The improvement in performance afforded by the proposed algorithm is validated with statistical tests of significance.

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“…In these studies, various information can be collected from fuel cell systems and used for fault diagnosis [ 1 , 2 ]. Based on information used in the analysis, these studies can be loosely divided into two categories, including techniques using fuel cell voltage only [ 3 , 4 , 5 , 6 ], and methodologies using multiple sensor data [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Compared to the fault diagnosis using fuel cell voltage, more studies have been performed using multiple sensors information [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Both single [ 7 ] and multiple PEM fuel cell faults [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] can be identified accurately with information from multiple sensors, using either model-based or data-driven approaches. Furthermore, in order to increase computational efficiency when dealing with data from multiple sensors, dimension reduction techniques such as principal component analysis have been selected to reduce the dimension of the original dataset, while retaining useful information [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Sensor Set Size on Polymer Electrolyte Membrane Fuel Cell Fault Diagnosis

Mao

Jackson

2018

Sensors

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This paper presents a comparative study on the performance of different sizes of sensor sets on polymer electrolyte membrane (PEM) fuel cell fault diagnosis. The effectiveness of three sizes of sensor sets, including fuel cell voltage only, all the available sensors, and selected optimal sensors in detecting and isolating fuel cell faults (e.g., cell flooding and membrane dehydration) are investigated using the test data from a PEM fuel cell system. Wavelet packet transform and kernel principal component analysis are employed to reduce the dimensions of the dataset and extract features for state classification. Results demonstrate that the selected optimal sensors can provide the best diagnostic performance, where different fuel cell faults can be detected and isolated with good quality.

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A discrete hidden Markov model fault diagnosis strategy based on K-means clustering dedicated to PEM fuel cell systems of tramways

Cited by 82 publications

References 48 publications

Fault Diagnosis and Identification of Power Capacitor Based on Edge Cloud Computing and Deep Learning

Fault Diagnosis and Identification of Power Capacitor Based on Edge Cloud Computing and Deep Learning

Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm

Effect of Sensor Set Size on Polymer Electrolyte Membrane Fuel Cell Fault Diagnosis

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