So far, numerous studies have been published on the selection of appropriate maintenance tactics based on some factors affecting them such as time, cost, and risk. This paper aims to develop the TRIZ contradiction matrix by explaining the dimensions and components of each of the following Reactive maintenance tactics. The related findings of previous studies were analyzed by adopting “Rousseau and Sandoski” seven-step method to identify and extract the relationships between TRIZ principles and Reactive maintenance tactics. Thereafter, 5 Reactive maintenance tactics were replaced TRIZ’s 40 principles in the TRIZ contradiction matrix. Finally, the ANP method were used to extract and prioritize the appropriate Reactive maintenance tactics. The proposed matrix in this research was used in the desalination section of one of the oil companies to select on the appropriate Reactive maintenance tactics. The results of this research is useful for managers and maintenance specialists of units in making decisions to provide appropriate Reactive maintenance tactics for the desired equipment.

Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.

In recent years, we have been able to observe a dynamic development of MCDA (multi-criteria decision analysis) methods, which have become widely used in various sectors, including construction. These methods are characterised by simplicity and one of their key advantages is their simple modelling of non-linear dependencies within decision problems and their analysis under the conditions of incomplete, uncertain and hard-to-measure information. The universality and simple use of these methods does not, however, free the decision-maker from the necessity to adopt the proper approach to modelling and analysing specific decision problems. To highlight the fact that it is the character of the problem that should determine the selection of the method of analysing it and not the other way around, the authors assessed the AHP (Analytic Hierarchy Process) and the ANP (Analytic Network Process) method in terms of verifying the impact of the different decision model structures on analysis outcomes and analysed their sensitivity to input data changes. This analysis was based on the example of selecting a telecommunications tower footing reinforcement alternative. The findings confirmed the significant impact of decision model structure on the ranking of the analysed alternatives.