The Practical Byzantine Fault Tolerant (PBFT) consensus algorithm has many advantages, which makes PBFT utilized widely. Nonetheless, PBFT is not suitable for large-scale node scenarios due to its high communication complexity and it also has an apparent disadvantage of inadequate fault tolerance. The typically derived PBFT algorithms focus on reducing communication complexity at the cost of diminished system security or fault tolerance. In this paper, Dual-Primary-Node derived Practical Byzantine Fault Tolerance (DPNPBFT) is proposed to achieve the best balance of the above three performances. First, DPNPBFT selects dual master nodes based on the idea of power separation. The two master nodes check balance and supervise each other to avoid excessive centralization as a single master node system. It also reduces the communication complexity of the replica node, which only communicates with the master node. Furthermore, we designed the architecture of DPNPBFT to get a practical 49% fault tolerance rate, and it is close to the current mainstream Proof of Work and Proof of Stake algorithms. Experimental results demonstrate that DPNPBFT has O(N) level communication complexity and excellent anti-host node malicious performance. The Transactions Per Second of DPNPBFT is stable at 1700. It proves DPNPBFT has the best performance balance and excellent comprehensive performance for large-scale Internet of Things application scenarios.

PurposeThe purpose of this paper is to provide a shorter time cost, high-accuracy fault diagnosis method for water pumps. Water pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing attention. Considering the time-consuming empirical mode decomposition (EMD) method and the more efficient classification provided by the convolutional neural network (CNN) method, a novel classification method based on incomplete empirical mode decomposition (IEMD) and dual-input dual-channel convolutional neural network (DDCNN) composite data is proposed and applied to the fault diagnosis of water pumps.Design/methodology/approachThis paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient (MFCC) and a neural network model of DDCNN. First, the sound signal is decomposed by IEMD to get numerous intrinsic mode functions (IMFs) and a residual (RES). Several IMFs and one RES are then extracted by MFCC features. Ultimately, the obtained features are split into two channels (IMFs one channel; RES one channel) and input into DDCNN.FindingsThe Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII dataset) is used to verify the practicability of the method. Experimental results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the diagnosis. Compared with EMD, 51.52% of data preprocessing time, 67.25% of network training time and 63.7% of test time are saved and also improve accuracy.Research limitations/implicationsThis method can achieve higher accuracy in fault diagnosis with a shorter time cost. Therefore, the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research importance.Originality/valueThis method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.

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We used the principle of hyperlink analysis method to mine the website data according to the indicators of the hyperlink analysis. We selected Taobao.com as an object of study. The evaluation indicators of network marketing effect were page views, sales quantity, sales, the number of adding store to bookmark . According to our research, we find Taobao.com stores can use data mining tool to obtain the very good marketing effect.

Failure diagnosis is of great significance for the timely detection of the safety hazard of the equipment and the guarantee of the normal operation of the production. In fault diagnosis, the way based on the processing of sound signal has the advantages of strong fault sensitivity, easy acquisition, and noncontact measurement, and the way of using neural network provides a more efficient and generally applicable method for fault diagnosis efficiency. For the poor diagnostic accuracy of traditional methods, which requires manual extraction of features and poor general applicability of the model, in this paper, we propose a mechanical failure diagnosis method based on acoustic signals and CNNs. The sound signals were first sampled and features extracted by MFCC, then the data were split into training and test sets in a 6:4 ratio and input to the convolutional neural network. After adjusting the parameters for the comparison experiment, the final experimental model was able to achieve 97.05% test accuracy over 20 training test iterations.