Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.

The Synergy of Living and Water Wall in Indoor Environment—Case Study in City of Brno, Czech Republic

The indoor environment that surrounds us and the elements in it affect not only our mood but also the air quality. Vegetation elements are currently more popular, especially for their aesthetic value but also because of the fact that they affect the physical parameters of the indoor environment such as temperature and humidity. Water elements are a similar example. The presented paper combines these two elements to achieve the best possible level of thermal comfort. Experimental verification of the influence of the living wall on air temperature and humidity took place during the heating season in the city of Brno in the space of the university, while three scenarios were created: the effect of the living wall in a semi-open space, an enclosed space, and a space with a water wall with regulated water temperature. The potential of the water wall is determined based on experimental verification in laboratory conditions. The results show that the synergy of the living and water wall in the indoor space may eliminate the risk of too-low humidity during the heating season.