The headwater of Yellow River Basin (HYRB) is crucial for the water resources of the whole basin in Northwest China. Based on the semi-distributed hydrological model "Soil and Water Assessment Tool" (SWAT), the spatiotemporal change trends of blue water and green water resources in the HYRB were analyzed quantificationally. By using the Sequential Uncertainty Fitting program (SUFI-2), the model was calibrated at Tangnaihai hydrological station and uncertainty analysis was performed. The results showed that the total water resources decreased by 1.08 billion m 3 over the past five decades in the HYRB. Blue water and green water storage (soil water) presented the downtrend, while green water flow (actual evapotranspiration) increased between 1961 and 2010. The decrease in blue water resources were mainly attributed to the decrease in precipitation in the southwest parts of the study area while the increase in actual evapotranspiration and the decrease in soil water were the results of the uptrend of air temperature. In 1990s, an enormous transition occurred between the blue water (24.86 %) and green water flow (63.46 %). At seasonal scale, the largest down trend of blue water and uptrend of actual evapotranspiration all occurred in autumn. The decrease ratios of them were 88.3 and 83.1 % in inter-annual variation, respectively. The study can provided a scientific basis for integrated water resources management under the background of global climate change and human activity.

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Compressive sensing provides a new idea for machinery monitoring, which greatly reduces the burden on data transmission. After that, the compressed signal will be used for fault diagnosis by feature extraction and fault classification. However, traditional fault diagnosis heavily depends on the prior knowledge and requires a signal reconstruction which will cost great time consumption. For this problem, a deep belief network (DBN) is used here for fault detection directly on compressed signal. This is the first time DBN is combined with the compressive sensing. The PCA analysis shows that DBN has successfully separated different features. The DBN method which is tested on compressed gearbox signal, achieves 92.5 % accuracy for 25 % compressed signal. We compare the DBN on both compressed and reconstructed signal, and find that the DBN using compressed signal not only achieves better accuracies, but also costs less time when compression ratio is less than 0.35. Moreover, the results have been compared with other classification methods.

It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency of the data. Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data. This method’s essential contribution is combining compressive sensing technology with fault diagnosis. To achieve a better diagnosis effect, we can effectively improve the wireless transmission efficiency of the vibration signal. First, the dictionary is dynamically updated by learning the dictionary using singular value decomposition to produce the ideal sparse form. Second, a block sparse Bayesian learning boundary optimization approach is utilized to recover structured non-sparse signals rapidly. A detailed assessment index of the data compression effect is created. Finally, the experimental findings reveal that the approach provided in this study outperforms standard compression methods in terms of compression efficiency and accuracy and its ability to produce the desired fault diagnostic effect, proving the usefulness of the proposed method.

Equipment degradation state recognition and prognosis are considered two significant parts of a prognostics and health management (PHM) system that help to reduce downtime and decrease economic losses. In this paper, a sparse representation (SR) feature is proposed as a new degradation feature, and the hidden semi-Markov model (HSMM) is established. The new method offers three significant advantages over the traditional HSMM. (1) Since the degradation information is incomplete, a Gaussian mixture model (GMM) is used here for degradation data clustering and state division. (2) A new degradation feature based on the wavelet packet transform (WPT) and SR can better extract the structural information of the collected signal and reflect the degradation characteristics. (3) To conduct remaining useful life (RUL) predictions, an improved model is proposed, which adds a control variable that can dynamically adjust the state duration. The effectiveness of the proposed method is demonstrated using 8 groups of bearing data from the Center for Intelligent Maintenance Systems (IMS). The results show that the HSMM with the SR feature achieves the best recognition accuracy, of 85.28%. Moreover, the improved prediction model achieves a prediction accuracy of 86.11% on average for 8 bearings.

Vibration signal transmission plays a fundamental role in equipment prognostics and health management. However, long-term condition monitoring requires signal compression before transmission because of the high sampling frequency. In this paper, an efficient Bayesian compressive sensing algorithm is proposed. The contribution is explicitly decomposed into two components: a multitask scenario and a Laplace prior-based hierarchical model. This combination makes full use of the sparse promotion under Laplace priors and the correlation between sparse blocks to improve the efficiency. Moreover, a K-singular value decomposition (K-SVD) dictionary learning method is used to find the best sparse representation of the signal. Simulation results show that the Laplace prior-based reconstruction performs better than typical algorithms. The comparison between a fixed dictionary and learning dictionary also illustrates the advantage of the K-SVD method. Finally, a fault detection case of a reconstructed signal is analyzed. The effectiveness of the proposed method is validated by simulation and experimental tests.