Machines

Bearing faults are the most common type of failure in induction motors, given their long operating times and mechanical loads. Because induction motors in industrial environments operate under various load conditions, effective methods for diagnosing bearing faults across these conditions have become increasingly important. Here, different load conditions were implemented with a powder clutch and a tension controller, and vibration data were acquired under both normal and faulty bearing conditions. To ensure diagnostic accuracy while improving time efficiency, a model bank-based fault diagnosis classifier is proposed, which utilizes independent classifiers trained for each load condition. For comparison, a single model-based classifier trained on all load conditions is also implemented. Both approaches are validated with three classifiers: support vector machine (SVM), multilayer neural network (MNN), and random forest (RF), with three input types: raw time-series signals, six statistical features, and three t-test–selected statistical features. Experimental results reveal that the model bank-based fault diagnosis classifier utilizing three statistical features selected by t-test maintained 98–100% accuracy while reducing operating time compared with Method 1 by 60.0, 71.2, and 60.0% for SVM, MNN, and RF, respectively. These results confirm that the proposed Method 2 utilizing time-domain analysis provides reliable and time-efficient performance for bearing fault diagnosis under variable load conditions.

The influence of inflow angle on the stall characteristics of a vertical axial flow pump is investigated numerically by solving the unsteady Reynolds-averaged Navier–Stokes equations. The study predicts both performance parameters and internal flow structures under varying inflow conditions. It is found that as the deflection angle decreases, both the critical and deep stall points shift toward higher flow rates. For the −30° scheme, the design efficiency and design head decrease by 16.27% and increase by 19.59%, respectively, compared to the 0° scheme. As stall develops, an axisymmetric blockage region forms at the impeller inlet, which reduces axial velocity and increases the impeller’s angle of attack. Under design conditions, a smaller deflection angle exacerbates boundary layer separation near the blade leading edge, thereby weakening the local work capacity and intensifying turbulent dissipation. Furthermore, although a reduced deflection angle promotes an earlier onset of stall, it also leads to a decrease in the instability intensity of the stall flow field. These results reveal a critical trade-off: while a smaller deflection angle promotes an earlier stall onset, it effectively mitigates the intensity of stall instability, providing crucial guidance for optimizing the hydraulic design and operational stability of vertical axial flow pumps.

This article focuses on the design, development and optimization of a mechanical system with the aim of increasing the efficiency of the production process. The article describes the issues involved in the production of molds used for EPS (Expanded Polystyrene) and EPP (Expanded Polypropylene) materials, specifically the assembly of mold nozzles. Currently, the assembly of nozzles is performed manually, and the proposed solution aims to automate this process using software and robotics. The solution involves scanning the mounting holes and then modifying the mold model in Siemens NX, based on which a trajectory is generated in the virtual environment of RoboDK software. Communication between Siemens NX and RoboDK software is implemented via a Python algorithm using NXOpen and RoboDK API (Application Programming Interface) libraries. The proposed tool has flexible settings and is not dependent on a robotic arm or tool. The result is a prototype software tool for offline programming of automated assembly, which is adapted to different hole layouts, allowing its use in small-batch production in the future. The proposed tool has flexible settings and is not dependent on a specific robotic arm or tool. The solution was validated through comprehensive simulation testing in the RoboDK environment, demonstrating significant potential for time reduction and process optimization.