The research compares the performance of a pair of machine learning toolkits PCA-ANN and LDA-ANN in optimising the fault diagnosis performance of a turbo machinery, a wind turbine, where bearings have become one of the most unreliable parts in mechanical systems. Our dataset, a 50-days record of a vibration signal acquired on a high-speed shaft of a 2 MW wind turbine, enabled us to obtain 15 statistical parameters in the time domain and resort to Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to reduce the dimensionality without losing much of the variance (97.6 percent) (PCA) or, at the same time, without diminishing the possibilities to separate the data in different classes (maximize the separability between classes) (LDA). Biometric performance of these reduced features was subsequently trained using a 10 neurons hidden layer in an ANN (PCA-ANN tested with accuracy = 80% loss = 0.059 and LDA-ANN tested with accuracy = 86.7%, loss = 0.031). LDA-ANN has proven to be better, requiring less time to execute (24.36 s vs. 28.39 s) and report fewer errors, which further establishes the strength of LDA in supervised fault detection. These findings present strong evidence of the effectiveness of a combination of statistical feature extraction, dimension reduction, and ANN-based classification in predictive maintenance, and at the same time, they indicate the potential to be advanced using additional datasets or hybrid modeling models.

Hybrid renewable energy systems (HRESs) are increasingly used because they integrate diverse energy resources whose availability can complement one another over the day–night cycle. However, the high cost of system components, particularly batteries, and the dependence of energy generation on weather conditions can undermine their economic viability. Researchers, therefore, seek to optimize HRES configurations to achieve a balance between reliability and lifecycle cost by performing techno-economic studies using different techniques. One of the industries that started relying more on renewable energy is the mobile telecommunication industry, especially in the remote stations where the expansion of the grid is expensive and the renewable resources are abundant. This study introduces the scope of hybrid renewable energy system optimization and applies a study case to a remote telecommunication station where the existing energy system was installed without prior techno-economic assessment. Using mathematical models, single and multi-objective (lexicographic) optimizations are conducted using the particle swarm optimization algorithm and a deterministic exhaustive search numerical algorithm, to minimize the lifecycle cost of the system, maintain reliability for a full continuity of service, and reduce the dependency to the diesel generator to contribute to the decarbonization objective of the world. The computational results show a significant reduction in the cost of the system from USD 525,512.83 for the existing system to approximately USD 197,879.03 (reduction of 62.3% of the actual cost), and the operating hours of the diesel generator from 1401 h/year to 770 h/year. More details are presented in the Discussion section.

In modern times, many manufacturing processes and laboratory experiments are becoming increasingly intricate as they rely on a multitude of variables that are hard to intuitively manipulate. To make sense of and model such complex phenomena, the design of experiments methodology is one of the most effective approaches. In the current report, the Surface Response Method (RSM) was adopted to assess how different drilling mechanical parameters affect the Rate of Penetration (ROP). The main goal is to explore the behavior of ROP when various mechanical drilling parameters, including weight on the bit (WOB), rotational speed of the bit (RPM), and cutting angle (β), are altered. The investigation covers two types of limestone rocks, and the gathered data is statistically analyzed using ANOVA to develop a mathematical model that explains the relationship between the different parameters and the rate of advancement, using the Response Surface Method (RSM). The results demonstrate that the WOB factor has the most significant influence on ROP development for both rock types, compared to other factors. Applying an optimization method is likely to control the optimal parameters for the drill's rational operation regime. The model developed in this study accurately fits the data and is therefore valuable in predicting the response. The technical reason that prompted us to conduct this scientific research is the excessive and continuous alteration of mechanical parameters in the drilling machine WOB (80,120,160 kgf), RPM (118, 135, 152 rpm), β (3°, 8°, 45°), aimed at maintaining an advanced drilling rate and ensuring the safety of its components. Therefore, it is aimed to reduce this continuous alteration while achieving the same or better results. Each parameter can be limited to a single level to achieve the same or better results. For the first rock A, the parameters are (WOB = 80.632, RPM = 118.959rpm, β = 23.016°) and for the second rock B, the parameters are (WOB = 136.418kgf, RPM = 147.911rpm, β = 40.911°).