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Based on ensemble empirical mode decomposition (EEMD) and the support vector machine (SVM), an algorithm used in the sensor fault detection and classification is put forward in this paper. Using this method and through EEMD, the sensor signal is decomposed into several segments, including the original signals, several intrinsic mode functions (IMFs) and the residual signals. Moreover, as features of the sensor fault, their variance, mean, entropy and the slope of the original signal are calculated in accordance with the characteristics of different fault types and the inherent physical meanings of each IMF. Subsequently, the feature vectors are inputted into the SVM, which is used to classify the detection and identification of sensor faults. Finally, the simulation results of the fault diagnosis of a carbon dioxide sensor indicate that this method may not only be effectively applied to fault diagnosis of carbon dioxide sensors but also provides a reference for that of other sensors.

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This paper studies features with the characteristic of unknown probability distribution, and its application on fault diagnosis based on non-stationary monitoring signals, which mainly consider the uncertainty as the main factor in masking fault diagnosis of practical industrial system. Generally, the probability distribution of the signal feature is unknown and prior information of the trend term is lacking. For this reason, different feature extraction methods, such as time-domain, frequency-domain and time-frequency-domain methods, have always been used to extract features, and they can be used to generate a high-dimensional and nonlinear initial feature set. However, the features' probability distribution is still unknown and prior information of the trend term is still lacking. In order to solve this top problem, Restricted Boltzmann Machine (RBM), with the advantage of feature learning and selection for initial feature set, has been stacked layer by layer to realize a high-dimensional nonlinear mapping between non-stationary signal features and fault modes. Two fault diagnosis experiments on self-confirmation sensor and rolling bearing shown the robustness and effectiveness of this proposed method.

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