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Smart data selection can quickly sieve valuable information from initial data. Doing so improves the efficiency of analyzing situations to aid in better decision-making. Past methods have mostly been based on expert experience, which may be subjective and inefficient when dealing with large, complex datasets. Recently, the system analysis method has been exploited to find the key data. However, few studies address the indirect effects and heterogeneity of time series data. In this study, a data selection method, the modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method based on the objective data grey relational analysis (GRA), is used to enhance the ability to analyze time-series data. GRA was first applied to assess the direct impact in the raw data indicators. Then, a modified DEMATEL was adopted to find the overall impact by including the indirect impact and data heterogeneity. We applied the method to analyze the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and perform the remaining useful life (RUL) prediction of aircraft engines. The results suggest that our method predicts well. Our work offers a nuanced approach of identifying key information in time series data and has potential applications. [ABSTRACT FROM AUTHOR]

• A novel neural network was proposed, which combined the advantages of two kinds of convolution. • A supervised feature selection strategy based on grey relation analysis was proposed. • Multi-sensor data were considered in the remaining useful life prediction of induction motors. • The proposed method was validated by remaining useful life prediction of induction motors. A novel remaining useful life prediction (RUL) method of induction motors based on hybrid dilated convolution networks (HDCN) and grey relation analysis (GRA) was proposed. To test the performance of the proposed method, the RUL prediction experiment was performed on a data set containing 8 motors. Firstly, the features of time domain, frequency domain, and entropy were extracted from the original signals. Secondly, GRA was used for feature selection, and a feature selection strategy was proposed. Finally, the data set after feature selection was imported into HDCN for training and testing. The results show that the predicted RUL and real RUL have the same trend and similar values. Seven comparison methods were designed and the same experiments were carried out. The results show that most of the root mean square error and mean absolute error of the proposed method are smaller than those of the other seven methods. [ABSTRACT FROM AUTHOR]

Predictive maintenance of lithium-ion batteries has been one of the popular research subjects in recent years. Lithium-ion batteries can be used as the energy supply for industrial equipment, such as automated guided vehicles and battery electric vehicles. Predictive maintenance plays an important role in the application of smart manufacturing. This mechanism can provide different levels of pre-diagnosis for machines or components. Remaining useful life (RUL) prediction is crucial for the implementation of predictive maintenance strategies. RUL refers to the estimated useful life remaining before the machine cannot operate after a certain period of operation. This study develops a hybrid data science model based on empirical mode decomposition (EMD), grey relational analysis (GRA), and deep recurrent neural networks (RNN) for the RUL prediction of lithium-ion batteries. The EMD and GRA methods are first adopted to extract the characteristics of time series data. Then, various deep RNNs, including vanilla RNN, gated recurrent unit, long short-term memory network (LSTM), and bidirectional LSTM, are established to forecast state of health (SOH) and the RUL of lithium-ion batteries. Bayesian optimization is also used to find the best hyperparameters of deep RNNs. Experimental results with the lithium-ion batteries data of NASA Ames Prognostics Data Repository show that the proposed hybrid data science model can accurately predict the SOH and RUL of lithium-ion batteries. The LSTM network has the optimal results. The proposed hybrid data science model with multiple artificial intelligence-based technologies also demonstrates critical digital-technology enablers for digital transformation of smart manufacturing and transportation. [ABSTRACT FROM AUTHOR]