12 issues per year
Current Issue: Issue 02, 2021
Established in 1986, the Chinese journal of Control and Decision is sponsored by Northeastern University and under the administration of the Ministry of Education of China. The journal is published monthly with a total of 256 pages in A4 size, and is distributed at home and abroad. For more than 30 years, "Control and Decision" has adhered to its mission and purpose, and has committed to gathering and disseminating outstanding academic achievements, inspiring scientific and technological innovations, and advancing the development of China's academic disciplines. The journal has published a great number of high-standard, original, and outstanding research achievements, both in theory and in practical applications, in the field of control and decision-making. With a good academic reputation in both academia and industry, high academic influence, and excellent level of publications, "Control and Decision" has attained top rankings in both core impact factors and comprehensive evaluation indicators. The journal has received many prestigious awards including "Top 100 Outstanding Academic Journals of China", "China's Most Influential Academic Journals”, and so on, and has been included in key national and international databases. In November 2019, the journal was selected for the “China Science and Technology Journal Excellence Action Plan”, a national project jointly implemented by the China Association for Science and Technology, the Ministry of Finance, the Ministry of Education, the Ministry of Science and Technology, the General Administration of Press and Publication, the Chinese Academy of Sciences and the Chinese Academy of Engineering. This will be yet another great motivation for “Control and Decision” to contribute relentlessly to the construction of the Chinese science and technology journals system of open innovation, cooperation, and world-class.
Editor in Chief: Filiwang, Guang-Hong Yang
Control and Decision,2021,Vol 36,No. 02
With the complexity of industrial systems and the increasing degree of intelligence, the normal operation of the system is seriously restricted by the reliability and safety of the system. Long-term work operation will increase the risk of system failure and reduce its safety and stability. In order to reduce the impact of system failure on product quality and production cost, the optimal maintenance decision problem of the system has gradually become a research hotspot. Analyzing the degraded state of the system facilitates can help make correct maintenance decisions for the system, extending system uptime and reducing the economic losses. For a system consisting of multiple components that are identical and degraded independently, a multi-state joint deterioration state space partition modeling for multi-component systems under discrete state modeling is established. According to the partition of the joint deterioration state space, the combinations of all maintenance requirements and their probability calculation formulas are given, a stationary probability model of the system state is established using Markov process theory. The correctness and effectiveness of the model are verified by numerical experiments.