No abstract

Power engineering is complex, involving many participants, numerous management factors, and different stages. To improve the quality management level of power engineering and realize the standardization of power engineering quality management, power engineering construction quality management standardization (CQMS) is evaluated using the order relation analysis method (G1 method) and the cloud model method. Firstly, based on systematic thinking and practice in China, the CQMS framework is proposed, and the evaluation index system including organizational performance, management behaviour performance, and entity performance related to power engineering construction quality is established. Subsequently, the G1 method is used to determine the weight of each index, and the cloud model method is used to implement the evaluation process. The results are divided into five grades, representing the five grades of construction quality management. Finally, taking a practical multiterminal DC demonstration project in China as an example, the CQMS performance is described and evaluated. The evaluation results directly reflect the level and difference of CQMS and show that this evaluation method can provide a reference for CQMS in power engineering.

Accidents in process industry occur frequently with serious casualties and property losses. This paper builds an accident causation model of process industry based on system thinking by dividing the accident causation system into 4 subsystems and 22 factors. A combination of grey relational analysis and correspondence analysis is conducted to carry out a structured analysis of the collected data. The research contains three main parts: (1) Grey relational analysis is used to obtain the significance ranking of 22 cause factors in process industry, and three critical cause factors are identified as “Security inspection,” “Risk identification,” and “Security awareness.” (2) Through correspondence analysis, the correlations between three sets of variables are analyzed and the cause factors requiring focused attention are identified as “Electric spark,” “Temperature,” “Raw material control,” “Punching phenomenon,” “Equipment clogging,” and “Combustible gases.” (3) An intelligent monitoring scheme is developed for the critical factors of each subsystem, which aims to achieve real‐time monitoring and early warning by means of video surveillance and sensor placement for the human, equipment, and environment subsystems. The conclusions obtained from this study can be used to enhance the efficiency of safety management and reduce the probability of accident occurrence in the process industry.