This paper presents a comprehensive model for the inertia force field acting on a moving connecting rod. The derived formulas enable the accurate calculation of resultant inertia forces and their distribution on individual components for finite element analysis (FEA). The method applies to symmetrical and complex-shaped connecting rods, addressing challenges in modeling forces for asymmetrical designs. This work advances the precision of stress and vibration modeling in connecting rods, crucial for tribology and reliability studies. By improving the understanding of wear and failure mechanisms in reciprocating systems, it supports design optimization. The article presents the application of the proposed computational methods using three materials typically used for connecting rod construction: 42CrMo4, aluminum 2618, and Ti6Al4V. The presented results demonstrate how the material selection influences the total inertia force and the resulting stresses within the material. The numerical results are presented based on simulations conducted for two connecting rods of different sizes, operating at extremely different rotational speeds. The conducted analyses show that in the examined cases, rotational speed is the key factor influencing inertia stresses. The implementation, based on Open Source tools, allows a numerical analysis of inertia forces and stresses, with all the methods and models available in an open repository.

This research is devoted to the development of a method for helicopter turboshaft engine energy characteristics control by regulating the free turbine rotor speed and fuel consumption using neural network technologies. A mathematical model was created that links the main rotor and free turbine rotor speed parameters, based on which a relation with the engine output power was established. In this research, a differential equation was obtained that links fuel consumption, output power, and rotor speed, which makes it possible to monitor engine dynamics in various operating modes. A fuel consumption controller was developed based on a neuro-fuzzy network that processes input data, including the desired and current rotor speed, which allows real-time adjustments to improve the operational efficiency. In the research, based on the flight data analysis obtained during the Mi-8MTV helicopter with a TV3-117 turboshaft engine flight test, improved signal processing quality was obtained due to time sampling and adaptive quantisation methods (this is confirmed by assessing the homogeneity and representativeness of the training and test datasets). A comparative analysis of the developed and traditional controllers showed that the neuro-fuzzy network use reduces the transient fuel consumption process time by 8.92% while increasing the accuracy and F1 score by 18.28% and 21.32%, respectively.

This article proposes a mathematical model for protecting helicopter turboshaft engines from surges, starting with fuel metering supply and maintaining stable compressor operation. The model includes several stages: first, fuel is supplied according to a specified program; second, an unstable compressor operation signal is determined based on the gas temperature in front of the compressor turbine and the gas generator rotor speed derivatives ratio; at the third stage, when the ratios’ threshold value is exceeded, fuel supply is stopped, and the ignition system is turned on. Then, the fuel supply is restored with reduced consumption, and the rotor speed is corrected, followed by a return to regular operation. The neural network model implementing this method consists of several layers, including derivatives calculation, comparison with the threshold, and correction of fuel consumption and rotor speed. The input data for the neural network are the gas temperature in front of the compressor turbine and the rotor speed. A compressor instability signal is generated if the temperature and rotor speed derivatives ratio exceed the threshold value, which leads to fuel consumption adjustment and rotor speed regulation by 28…32%. The backpropagation algorithm with hyperparameter optimization via Bayesian optimization was used to train the network. The computational experiments result with the TV3-117 turboshaft engine on a semi-naturalistic simulation stand showed that the proposed model effectively prevents compressor surge by stabilizing pressure, vibration, and gas temperature and reduces rotor speed by 29.7% under start-up conditions. Neural network quality metrics such as accuracy (0.995), precision (0.989), recall (1.0), and F1-score (0.995) indicate high efficiency of the proposed method.

This article presents a vibration signal analysis method to diagnose helicopter turboshaft engine defects such as bearing imbalance and wear. The scientific novelty of the article lies in the development of a comprehensive approach to diagnosing helicopter turboshaft engine defects based on the vibration signals amplitude and frequency characteristics integral analysis combined with a neural network for probabilistic defect detection. Unlike existing methods, the proposed approach uses the energy criterion for the vibration characteristics. It averages the assessment of unique signal processing algorithms, which ensures reliable defect classification under flight vibration conditions. The method is based on representing vibration signals as a sum of harmonic oscillations supplemented by noise components, which helps to identify deviations from typical values. The developed method includes a state function in which the amplitudes and frequency characteristics from nominal parameters estimate deviations. When the critical threshold is exceeded, the function signals possible malfunctions. A multilayer neural network is used to classify defect types, providing high classification accuracy (from 0.985 to 0.994). Computer experiments on the developed seminaturalistic modeling stand confirm that the method can detect increased vibration levels, which is the potential failure indicator. Comparative analysis shows the proposed method’s accuracy and noise resistance superiority, emphasizing the importance of introducing modern technologies to improve aircraft operation reliability and safety.

This work is devoted to the issue of recognizing voice commands by the voice control system of a collaborative robot in an industrial environment. Such a robot will be designed to cooperate with a human during the assembly process. This method of communication significantly facilitates and speeds up work, increasing the efficiency of the assembly process and reducing the operator's workload. However, production halls and other places where assembly takes place are environments filled with sounds with a wide spectrum of intensity and frequency. Such an environment significantly impedes the functioning of the voice control system. This work examined the impact of sound pollution on the robot's command recognition.