Real-time sensor fault detection in Tokamak using different machine learning algorithms

Mohapatra, Debashish; Subudhi, Bidyadhar; Daniel, Raju

doi:10.1016/j.fusengdes.2019.111401

Cited by 27 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…value of polynomial kernel is 2) (Hu et al, 2005), KNN (Yang et al, 2016b), and RF (Mohapatra et al, 2020). All the experiments repeated 30 times, respectively, and all the results are presented in Table 4.…”

Section: Conclusion and Future Researchesmentioning

confidence: 99%

“…Recently, traditional machine learning (ML) methods have been widely used for fault diagnoses, such as the extreme learning machine (ELM) (Song et al, 2019), empirical mode decomposition , support vector machines (SVM) (Hu et al, 2005), KNN (Yang et al, 2016b), non-negative matrix factorization (Yang et al, 2016a), gray forecasting , learning vector quantization (LVQ) (Bassiuny et al, 2007), random forest (RF) (Mohapatra et al, 2020), and kernel principal component analysis (KPCA) (Navi et al, 2018). These methods can effectively extract fault features to a certain extent, but there are some limitations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Hydrogen Sensor Fault Diagnosis Method Based on Transfer Learning With LeNet-5

et al. 2021

View full text Add to dashboard Cite

The fault safety monitoring of hydrogen sensors is very important for their practical application. The precondition of traditional machine learning methods for sensor fault diagnosis is that enough fault data with the same distribution and feature space under the same working environment must exist. Widely used fault diagnosis methods are not suitable for real working environments because they are easily complicated by environmental conditions such as temperature, humidity, shock, and vibration. Under the influence of such complex conditions, the acquisition of sensor fault data is limited. In order to improve fault diagnosis accuracy under complex environmental conditions, a novel method of transfer learning (TL) with LeNet-5 is proposed in this paper. Firstly, LeNet-5 is applied to learn the features of the data-rich datasets of gas sensor faults in a normal environment and to adjust the parameters accordingly. The parameters of the LeNet-5 are transferred from the task in the normal environment to a task in a complex environment by using the TL method. Then, the migrated LeNet-5 is used for the fault diagnosis of gas sensors with a small amount of fault data in a complex environment. Finally, a prototype hydrogen sensor array is designed and implemented for experimental verification. The gas sensor fault diagnosis accuracy of the traditional LeNet-5 was 88.48 ± 1.04%, while the fault diagnosis accuracy of TL with LeNet-5 was 92.49 ± 1.28%. The experimental results show that the method adopted presents an excellent solution for the fault diagnosis of a hydrogen sensor using a small quantity of fault data obtained under complex environmental conditions.

show abstract

Section: Conclusion and Future Researchesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Hydrogen Sensor Fault Diagnosis Method Based on Transfer Learning With LeNet-5

et al. 2021

View full text Add to dashboard Cite

show abstract

“…For instance, the authors in [ 13 ] proposed a context-aware intrusion detection system utilizing machine learning for a smart factory, and the study in [ 14 ] used an ensemble machine learning approach for disease diagnosis based on wearable sensors. The authors in [ 15 ] suggested an ensemble learning-based algorithm named Random Forest (RF) for real-time fault detection in magnetic position sensors.…”

Section: Related Workmentioning

confidence: 99%

CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Saeed

Lee

Jan

et al. 2021

Sensors

View full text Add to dashboard Cite

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

show abstract

“…Compared to other ML methods, RF has the characteristics of low complexity, fast computing speed, high accuracy rate, insensitive to parameters, no need for feature normalization, less over fitting, etc. [37], [38]. Importantly, RF is more robust with respect to noise [37].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is more suitable to use RF when respect to a large number of data with reasonable features, especially under noisy environment. The literature [38], [39] has demonstrated that sensors fault diagnosis based on RF is feasible.…”

Section: Introductionmentioning

confidence: 99%

A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis

et al. 2020

View full text Add to dashboard Cite

Hydrogen is considered to be a hazardous substance. Hydrogen sensors can be used to detect the concentration of hydrogen and provide an ideal monitoring means for the safe use of hydrogen energy. Hydrogen sensors need to be highly reliable, so fault identification and diagnosis for gas sensors are of vital practical significance. However, traditional machine learning methods for fault diagnosis are based on features extracted by experts, prior knowledge requirements and the sensitivity of system changes. In this study, a new convolutional neural network (CNN) using the random forest (RF) classifier is proposed for hydrogen sensor fault diagnosis. First, the 1-D time-domain data of fault signals are converted into 2-D gray matrix images; this process does not require noise suppression and no signal information is lost. Secondly, the features of the gray matrix images are automatically extracted by using a CNN, which does not rely on expert experience. Dropout and zero-padding are used to optimize the structure of the CNN and reduce overfitting. Random forest, which is robust and has strong generalization ability, is introduced for the classification of gas sensor signal modes, in order to obtain the final diagnostic results. Finally, we design and implement a prototype hydrogen sensor array for experimental verification. The accuracy of fault diagnosis in hydrogen sensors is 100% under noisy environment with the proposed method, which is superior of CNN without RF and other methods. The results show that the proposed CNN with RF method provides a good solution for hydrogen sensor fault diagnosis. INDEX TERMS Fault diagnosis, hydrogen sensor, convolutional neural network, random forest, feature extraction.

show abstract

Real-time sensor fault detection in Tokamak using different machine learning algorithms

Cited by 27 publications

References 8 publications

A New Hydrogen Sensor Fault Diagnosis Method Based on Transfer Learning With LeNet-5

A New Hydrogen Sensor Fault Diagnosis Method Based on Transfer Learning With LeNet-5

CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis

Contact Info

Product

Resources

About