Abnormal data acquisition system of mechanical operation based on block chain technology

In order to solve the problems of information sharing, tampering and leaking in mechanical operation data acquisition, an abnormal data acquisition system for mechanical operation based on block chain technology was designed. The system takes Beihang Chain as the prototype and designs the overall architecture of the system. Data acquisition module uses data acquisition card, CPU, programmable logic device, A/D conversion chip and other equipment to collect and process abnormal data in mechanical operation. The alarm module divides the abnormal data collected by the data acquisition module into five levels: P1-P5. After the implementation of the alarm module, the abnormal information and alarm information in the process of mechanical operation are transmitted to the abnormal data management module for storage. The system adopts the method of mechanical operation anomaly data acquisition based on sparse sampling, and adopts hierarchical clustering method to establish the data acquisition tree of mechanical operation anomaly. The block chain technology is used to design the process of storing and monitoring abnormal data of mechanical operation. The experimental results show that the system has high accuracy of fitting curve for abnormal data acquisition, low real-time energy consumption, and the minimum energy consumption is only 0.01/10-3 J.

[1]  Mariano Ruiz,et al.  FPGA-Based Solutions for Analog Data Acquisition and Processing Integrated in Area Detector Using FlexRIO Technology , 2018, IEEE Transactions on Nuclear Science.

[2]  Young Han Lee,et al.  Short T2 tissue imaging with the Pointwise Encoding Time reduction with Radial Acquisition (PETRA) sequence: the additional value of fat saturation and subtraction in the meniscus. , 2015, Magnetic resonance imaging.

[3]  Ullrich Pietsch,et al.  Multichannel FPGA-Based Data-Acquisition-System for Time-Resolved Synchrotron Radiation Experiments , 2017, IEEE Transactions on Nuclear Science.

[4]  Richard Sparling,et al.  Whole cell, label free protein quantitation with data independent acquisition: Quantitation at the MS2 level , 2015, Proteomics.

[5]  Jiandong Wang,et al.  Normal and Abnormal Data Segmentation Based on Variational Directions and Clustering Algorithms , 2017 .

[6]  V. G. Nikitaev,et al.  Study of the Effectiveness of Using Wavelet Analysis in Data-Acquisition Systems for Diagnosis of Acute Leukemias , 2015 .

[7]  C. Yang,et al.  A Real-Time Data Acquisition and Processing Framework Based on FlexRIO FPGA and ITER Fast Plant System Controller , 2016, IEEE Transactions on Nuclear Science.

[8]  Jenny Chen,et al.  An FPGA-Based Quench Detector and Data Acquisition System for Superconducting Insertion Devices , 2018, IEEE Transactions on Applied Superconductivity.

[9]  P.K. Pandey,et al.  A finite difference method for a numerical solution of elliptic boundary value problems , 2018, Applied Mathematics and Nonlinear Sciences.