Hydrogen fuel cell diagnostics using random forest and enhanced feature selection

Lin, Rongheng; Pei, Zixiang; Ye, Zezhou; Guo, Chengcheng; Wu, Bo

doi:10.1016/j.ijhydene.2019.10.127

Cited by 37 publications

(18 citation statements)

References 27 publications

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“…Although many have used feature selection algorithms such as Principal Component Analysis (PCA) [ 65 , 66 ], KNN [ 67 , 68 ], NB [ 69 , 70 ], LR [ 71 , 72 ], but recent works predominantly use RF [ 73 , 74 , 75 , 76 , 77 ] and XGBoost [ 78 , 79 , 80 , 81 , 82 ]. In particular, the authors in [ 83 ] provide a detailed analysis of RF-based feature selection.…”

Section: Related Workmentioning

confidence: 99%

Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

Liu

Thapa

Shaver

et al. 2021

Sensors

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As Internet of Things (IoT) networks expand globally with an annual increase of active devices, providing better safeguards to threats is becoming more prominent. An intrusion detection system (IDS) is the most viable solution that mitigates the threats of cyberattacks. Given the many constraints of the ever-changing network environment of IoT devices, an effective yet lightweight IDS is required to detect cyber anomalies and categorize various cyberattacks. Additionally, most publicly available datasets used for research do not reflect the recent network behaviors, nor are they made from IoT networks. To address these issues, in this paper, we have the following contributions: (1) we create a dataset from IoT networks, namely, the Center for Cyber Defense (CCD) IoT Network Intrusion Dataset V1 (CCD-INID-V1); (2) we propose a hybrid lightweight form of IDS—an embedded model (EM) for feature selection and a convolutional neural network (CNN) for attack detection and classification. The proposed method has two models: (a) RCNN: Random Forest (RF) is combined with CNN and (b) XCNN: eXtreme Gradient Boosting (XGBoost) is combined with CNN. RF and XGBoost are the embedded models to reduce less impactful features. (3) We attempt anomaly (binary) classifications and attack-based (multiclass) classifications on CCD-INID-V1 and two other IoT datasets, the detection_of_IoT_botnet_attacks_N_BaIoT dataset (Balot) and the CIRA-CIC-DoHBrw-2020 dataset (DoH20), to explore the effectiveness of these learning-based security models. Using RCNN, we achieved an Area under the Receiver Characteristic Operator (ROC) Curve (AUC) score of 0.956 with a runtime of 32.28 s on CCD-INID-V1, 0.999 with a runtime of 71.46 s on Balot, and 0.986 with a runtime of 35.45 s on DoH20. Using XCNN, we achieved an AUC score of 0.998 with a runtime of 51.38 s for CCD-INID-V1, 0.999 with a runtime of 72.12 s for Balot, and 0.999 with a runtime of 72.91 s for DoH20. Compared to KNN, XCNN required 86.98% less computational time, and RCNN required 91.74% less computational time to achieve equal or better accurate anomaly detections. We find XCNN and RCNN are consistently efficient and handle scalability well; in particular, 1000 times faster than KNN when dealing with a relatively larger dataset-Balot. Finally, we highlight RCNN and XCNN’s ability to accurately detect anomalies with a significant reduction in computational time. This advantage grants flexibility for the IDS placement strategy. Our IDS can be placed at a central server as well as resource-constrained edge devices. Our lightweight IDS requires low train time and hence decreases reaction time to zero-day attacks.

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Section: Related Workmentioning

confidence: 99%

Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

Liu

Thapa

Shaver

et al. 2021

Sensors

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“…Through accurate fault diagnosis, relevant personnel can take corresponding measures to prevent more serious faults. At present, many scholars have done a lot of research on fault diagnosis methods of the PEMFC system [1,[3][4][5][6][7][8][9][10][11][12][13][14]. The proposed methods can be mainly categorized into two types [4]-model-based methods [3,[5][6][7][8][9][10] and data-driven methods [1,4,[11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…It can be seen from [1,[3][4][5][6][7][8][9][10][11][12][13][14] that the model-based fault diagnosis method can be used to diagnose and isolate the different faults for fuel cell systems. However, because of the complex structure of PEMFC systems, it is difficult to obtain the parameters of some key components or materials.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification

2021

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Data-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose faults through the state variable data collected during the operation of the PEMFC system. However, the state variable data collected from the PEMFC system during the stack switching between different operating points can easily cause false alarms, such that the practical value of the diagnosis system is reduced. To overcome this problem, a fault diagnosis method for PEMFC systems based on steady-state identification is proposed in this paper. The support vector data description (SVDD) and relevance vector machine (RVM) optimized by the artificial bee colony (ABC) are used for the steady-state identification and fault diagnosis. The density-based spatial clustering of applications with noise (DBSCAN) and linear least squares fitting (LLSF) are used to identify the abnormal data in datasets and estimate change rates of the system state variables respectively. The proposed method can automatically identify the state variable data collected from the PEMFC system during the stack switching between different operating points, so that the diagnosis accuracy can be improved and false alarms can be reduced. The proposed method has a certain practical value and can provide a reference for further study.

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“…Fault conditions are detected, and isolated using artificial neural networks [24], and fuzzy logic [25]. Principle component analysis [26] is also used to study faults in the literature. The degradation in fuel cells needs to be detected, whether it is due to fault conditions or aging.…”

Section: Introductionmentioning

confidence: 99%

Proton exchange membrane fuel cell fault and degradation detection using a coefficient of variance method

Barhate¹,

Mudhalwadkar²

2021

Journal of Energy Systems

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Proton exchange membrane fuel cell is a clean energy generator as it emits water as a by-product. The fuel cell has various applications in stationary power generation and transportation. However, there is a need to improve durability for transportation applications. Fuel cell durability is limited as its performance degrades over a period due to aging, and fault conditions. In this study, we have compared fuel cell performance by using a new cell, and an aged cell. Degradation due to aging is experimented with by using a membrane that was operated for more than 2000 hours. Fuel cell performance degrades around 90% due to aging. Moreover, experimentally faults were created to study the degradation of fuel cell performance. We created three faults in the fuel cell system: Water flooding, reactant gas starvation, and high operating temperature. Fuel cell performance observed more than 30% degradation during the fault conditions. Furthermore, the coefficient of variance technique is used to detect aging, and the fault condition.

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Hydrogen fuel cell diagnostics using random forest and enhanced feature selection

Cited by 37 publications

References 27 publications

Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification

Proton exchange membrane fuel cell fault and degradation detection using a coefficient of variance method

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