Abstract
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.
Highlights
Traditional energy sources, such as liquefied petroleum gas, natural gas and coal, are non-renewable resources; it is crucial that new energy sources are found to replace them
In a convolutional neural network (CNN), each neuron in a feature map is sparsely connected to a small group of neurons in the previous layer, which is different from the connections in an artificial neural network (ANN)
The main contributions of this study are the development of a raw sensor signalto-gray matrix images conversion method, which changes the default image size of LeNet-5 from 32 × 32 pixels to 50 × 40 pixels, according to the length of the hydrogen sensor signal data
Summary
Traditional energy sources, such as liquefied petroleum gas, natural gas and coal, are non-renewable resources; it is crucial that new energy sources are found to replace them. A novel sensor data-driven fault diagnosis method is proposed based on CNN [34]. Compared to traditional ML methods, a CNN has achieved better results, but its application in gas sensor fault diagnosis is still in a developmental stage. Called multi-classifier system and committee-based learning, uses multiple weak classifiers to form a strong classifier, the classification results are obtained according to the majority voting principle Such a process makes it perform better for complex data and obtain better classification accuracy and generalization performance. A method for hydrogen sensor fault diagnosis using a CNN with RF (CNN-RF) is proposed to automatically capture features of the gas sensor signal and improve upon the performance of conventional methods. A novel model based on CNN-RF for hydrogen sensor fault diagnosis is introduced.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
