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.

The present article introduces a numerical and analytical study of ternary hybrid nanoparticle (Cu, Fe ₃ O ₄ , and SiO ₂ ) with base fluid of Polymer. Heat Transfer on MHD Jeffery–Hamel flow with multiple effects is taken into account. Innovative results in the current study are obtained by utilizing the combined effect of nonlinear thermal radiation and the stretchable/shrinkable walls. The modelled partial differential equations underwent a transformation to nonlinear ordinary differential equations using similarity transformation. Subsequently, an analytical solution was obtained by employing the ADM method. To validate the accuracy of the results, a comparison was made with outcomes from the HAM‐based Mathematica package and the Runge–Kutta–Fehlberg fourth‐fifth order approach featuring shooting technique. The effect of diverse parameters on dimensionless velocity, temperature profiles, skin friction coefficient and Nusselt number has been investigated and interpreted through the tabular and graphical results. When nanoparticles are present in the base fluid, the rate of heat transmission is upsurged for both convergent‐divergent channels. In addition, the Nusselt number boosts with the augment of nanoparticle volume fraction. Results obtained also reveal that the nanofluid temperature upsurges in the stretching zone ( C > 0) and drops in the shrinking zone ( C < 0).