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Performance degradation assessment and prediction of hydraulic servo systems has attracted increasing attention in recent years. This study proposes a performance degradation assessment and prediction method based on Mean Impact Value (MIV), Mahalanobis Distance (MD), and Elman neural network. First, a state observer based on Radial Basis Function (RBF) is designed to calculate the residual error between the actual and estimated outputs, and typical time-domain features, such as Root Mean Square (RMS), peak value and kurtosis, are extracted. Second, the MIV analysis based on BP neural network is applied to evaluate the sensitivity of each extracted feature, and the selected optimal features are employed to construct the Mahalanobis space for normal states. Third, the MD between the most recent state and the constructed space of normal state is calculated, which can be normalized into a confidence value so as to assess the performance. Finally, an Elman neural network is used to predict the degradation trend. The proposed method is proven to be effective by a simulation model with the commonly occurring faults. [ABSTRACT FROM AUTHOR]

The research confirms that the irreversible transformation to spinel-like structure during orthorhombic LiMnO2 cycle is a gradual phase transformation process. A model for the structure change of orthorhombic LiMnO2 during the electrochemical cycle is presented. The model successfully clarified the fact that the 4 V voltage plateau does not exist for the first discharge. Because the LiMnO2 has 124 mAh g–1 of the first charge capacity, there are many Li ions containing the octahedral sites. The tetrahedral sites left by Mn ions migrating would be occupied by lithium ions containing the octahedral sites with further cycle, which make the 4 V voltage plateau developing. According to the model suggestion, the different variants of spinel LixMn2O4 will develop the nanodomain. [ABSTRACT FROM AUTHOR]