Failure mode and effects analysis (FMEA) has been proven to be an effective methodology to improve system design reliability. However, the standard approach reveals some weaknesses when applied to wind turbine systems. The conventional criticality assessment method has been criticized as having many limitations such as the weighting of severity and detection factors. In this paper, we aim to overcome these drawbacks and develop a hybrid cost-FMEA by integrating cost factors to assess the criticality, these costs vary from replacement costs to expected failure costs. Then, a quantitative comparative study is carried out to point out average failure rate, main cause of failure, expected failure costs and failure detection techniques. A special reliability analysis of gearbox and rotor-blades are presented.

This research aims to develop an integrated approach to construction project management by integrating digital technology into monitoring and surveillance operations. Through the use of drones and image processing software, data can be updated regularly and accurately about the progress at the construction site, allowing managers and decision makers to have a clear view of the current situation and make effective decisions based on accurate. In addition, this approach contributes to improving communication and coordination among project team members, as data and images can be easily and effectively shared, reducing opportunities for error and enhancing effective interaction among different parties. Using digital twin technologies, planning and forecasting processes can also be improved, as comprehensive analysis of digital data provides a deeper understanding of project dynamics, identifies potential risks, and enables appropriate preventive measures to be taken. In conclusion, the integration of digital twins and the use of drones in construction projects represent a significant step towards achieving smarter and more efficient management, and successfully achieving the defined goals with greater effectiveness.

The design of energy systems is very important in order to reduce operating costs and guarantee the reliability of a system. This paper proposes a new algorithm to solve the design problem of optimal multi-objective redundancy of series-parallel power systems. The chosen algorithm is based on the hybridization of two metaheuristics, which are the bat algorithm (BA) and the generalized evolutionary walk algorithm (GEWA), also called BAG (bat algorithm with generalized flight). The approach is combined with the Ushakov method, the universal moment generating function (UMGF), to evaluate the reliability of the multi-state series-parallel system. The multi-objective design aims to minimize the design cost, and to maximize the reliability and the performance of the electric power generation system from solar and gas generators by taking into account the reliability indices. Power subsystem devices are labeled according to their reliabilities, costs and performances. Reliability hangs on an operational system, and implies likewise satisfying customer demand, so it depends on the amassed batch curve. Two different design allocation problems, commonly found in power systems planning, are solved to show the performance of the algorithm. The first is a bi-objective formulation that corresponds to the minimization of system investment cost and maximization of system availability. In the second, the multi-objective formulation seeks to maximize system availability, minimize system investment cost, and maximize the capacity of the system.

In electrical power engineering, elements such as reliability analysis, modeling, and optimization for complex systems are of the utmost importance. Although there exist myriad studies regarding reliability optimization with conventional methods, researchers are still seeking to find more efficient and accurate methods to address the issue of the redundancy allocation problem. To that effect, an ideal power energy management approach is put forward for the operation of a hybrid microgrid system with different kinds of productions. In the present study, we suggest three algorithms in order to optimize the series-parallel power energy system: the Firefly (FA), Bat (BA), and Interior Search (ISA) algorithms. Moreover, the reliability estimate of the system is solved with the Ushakov algorithm (UMGF). The components may completely fail, which decreases their performance rate. Furthermore, the optimization results are achieved using objective functions that include the total cost of the system, emission gases (NO_X, SO₂, and CO₂) of the power production from fuel cells, diesel generators, and gas turbines, and take into consideration the dependability indices. Devices used in power subsystems are characterized based on their dependabilities, performances, capital costs, and maintenance costs. Reliability hinges on a functioning system, which naturally entails meeting customer demand; as a result, it is influenced by the accumulated batch curve. This method provides an idea with regards to the economic cost optimization of microgrid systems. Finally, we present the results of numeric simulations.