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In the current economic situation of many companies, the need to reduce production time is a critical element. However, this cannot usually be carried out with a decrease in the quality of the final product. This article presents a possible solution for reducing the time needed for quality management. With the use of modern solutions such as optical measurement systems, quality control can be performed without additional stoppage time. In the case of single-point measurement with the Laser Doppler Vibrometer, the measurement can be performed quickly in a matter of milliseconds for each product. This article presents an example of such quality assurance measurements, with the use of fully non-contact methods, together with a proposed evaluation criterion for quality assessment. The proposed quality assurance algorithm allows the comparison of each of the products’ modal responses with the ideal template and stores this information in the cloud, e.g., in the company’s supervisory system. This makes the presented 3D Laser Vibrometry System an advanced instrumentation and data acquisition system which is the perfect application in the case of a factory quality management system based on the Industry 4.0 concept.

The Impulse Excitation Technique (IET) is one of the most useful testing methods for evaluating or calculating some material properties. This can be useful to evaluate and confirm that the material ordered is what was delivered. In the case of unknown materials, where their properties are required by simulation software, this is also a quick way to obtain mechanical properties and thus improve the simulation quality. The main drawback of the method is the requirement for a specialized sensor and acquisition system and a well-trained engineer to prepare the setup and analyze the results. The article evaluates the possibility of using a low-cost solution in the form of a mobile device microphone as a way to obtain data, which after the Fast Fourier Transform (FFT), allows to obtain frequency response graphs and use the IET method procedure to calculate the mechanical properties of the samples. The data obtained by the mobile device are compared with the data obtained by professional sensors and data acquisition systems. The results confirm that for typical homogenous materials, the mobile phone is a cheap and reliable alternative for fast, on-the-go material quality inspections and can be introduced even in small companies and on construction sites. Additionally, this kind of approach does not require specific knowledge of sensing technology, signal treatment, or data analysis and can be performed by any assigned employee, who can receive the quality check information immediately on-site. Additionally, the presented procedure allows data collection and transfer to the cloud for future references and additional information extraction. This element is fundamental for introducing sensing technologies under the Industry 4.0 concept.

A seismic swarm is a series of earthquakes that occur in a small area over a short period of time. A sequence of earthquakes of this magnitude is unusual in Switzerland, and it is impossible to anticipate how it may unfold in the future.The seismic activity of such an event usually fades after a few days or weeks. Significantly greater earthquakes are likely to occur during the next several days, with up to a chance of 5 to 10%. For these reasons, the underground research facilities need tools to provide data on the impact of these events on their experiments. The paper presents the techniques implemented at The European Organization for Nuclear Research (CERN) to allow the tracking and monitoring of these unusual events. Additionally, the real effect of such an unusual event is presented together with the statistical approach to monitoring and effect evaluation. Considering the collision energy of the beams at 14 TeV, the energy stored in the magnets at 10 GJ (2400 kg of TNT), and the energy carried by the two beams at 724 MJ (173 kg of TNT), prolonged exposure to vibration close to or above the set alarm levels may result in serious safety issues. The presented evaluation of earthquake swarm impact on underground facilities together with the approach for data evaluation can be used for the design of future detectors and accelerators. Additionally, it provides tools for facilities users to present the data in an easy to understand way. This includes the Future Circular Collider, whose purpose is to significantly expand the energy and intensity frontiers of planned particle colliders, with the goal of reaching collision energies of 100 TeV in the quest for novel physics. As a result, even greater standards for beam size and stability will be required.

In construction, ensuring the quality and compliance of materials with specified requirements is often challenging, especially at construction sites. Conventionally, this process necessitates transporting samples to well-equipped laboratories, incurring significant time and financial costs. This article proposes a novel approach through a cost-effective mobile test station, enabling on-site measurements and immediate evaluation results, regardless of the testing conditions. The foundation of our testing methodology lies in the Impulse Excitation Technique (IET), which capitalises on measuring the frequency response of samples while considering their mass and dimensions. By applying this technique, we can effectively determine crucial elastic properties, such as the Young Modulus and Poisson Ratio. These obtained values can then be cross-referenced with established material tables to verify the material’s compliance with the specified order. In this study, the developed universal and mobile test station demonstrated versatility by successfully evaluating three samples of typical construction materials, showing the method’s reliability on some real case measurements. The results substantiate its potential as a reliable mobile quality assurance station. Moreover, the station’s adaptability empowers its use on site, in laboratory settings, or even during material transportation when necessary. This innovation promises to revolutionise material quality assessment, streamlining the construction process and expediting decision making.

The article’s main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies.

In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing. Specifically, the task of fruit detection and counting in orchards represents a complex issue that is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide the timely and precise data necessary for these tasks. With the agricultural sector increasingly relying on technological advancements, the integration of innovative solutions is essential. This study presents a novel approach that combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs). The proposed approach demonstrates superior real-time capabilities in fruit detection and counting, utilizing a combination of AI techniques and multi-UAV systems. The core innovation of this approach is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and, ultimately, a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring the high-resolution and accurate detection of targeted objects during UAV operations. Its effectiveness is proven by experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate at 14.7% and a false negative rate at 18.3%, even under challenging weather conditions like cloudiness. Overall, the practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0.

In Central and Eastern Europe, a growing popularity of gas heaters as the main source of heat and domestic hot water can be observed. This is the result of new laws and strategies for funding that have been put in place to encourage households to stop using coal and replace it with cleaner energy sources. However, there is a growing concern that gas furnaces are prone to malfunction and can be a threat to occupants through CO (carbon monoxide) generation. To see how a faulty gas furnace with a clogged exhaust may affect a household, a series of multizone and computational fluid dynamics (CFD) simulations were carried out using the CONTAM software and CFD0 editor created by the National Institute of Standards and Technology (NIST). The simulations presented different placements of the furnace and ventilation outlet in an attached garage. The results showed how the placement influenced contaminant migration and occupant exposure to CO. It changed the amount of CO that infiltrated to the attached house and influenced occupant exposure. The results may be used by future users to minimize the risk of CO poisoning by using the proper natural ventilation methods together with optimal placement of the header in the household.

Due to rising energy costs, there is a trend to return to conventional heating systems powered by solid fuel. A rise in the combination of new and old energy sources is creating unintended dual-fuel heating systems. These systems combine an old solid-fuel furnace and a new gas furnace. Usually, the old furnace was meant to be replaced by the new one and their cooperation was never intended when installing the new heating system. The occupants decided to leave the old system in fear of a rise in prices of gas or electricity or temporary problems with their supply. The study focuses on such a system and its influence on indoor air quality and thermal comfort. A series of dynamic measurements with an IoT remote sensor array in a chosen household was conducted to evaluate the behaviour of the system as well as effects on the indoor environment. Sensors measured the CO2 concentration and thermal profile in a household when using a dual-fuel heating system consisting of an old wood furnace from the 1980s and a recently installed new gas furnace. The results showed that none of the heat sources posed a threat to the occupants. Contaminants were safely removed by the exhaust systems of the furnaces. The thermal comfort, however, was influenced more by the wood furnace where fluctuations in the temperature were noticed, especially during the night. The gas furnace maintained a stable temperature that was more suitable for the occupants.

Vibration analysis and monitoring are currently required in various fields of industry, from automotive and aeronautics to manufacturing and quality control, and from machining and maintenance to civil engineering [...]

The work is devoted to the helicopter turboshaft engines’ gas generator rotor R.P.M. neuro-fuzzy controller development, which improves control accuracy and increases the system’s stability to external disturbances and adaptability to changing operating conditions. Methods have been developed, including improvements to the automatic control system structural diagram which made it possible to obtain the system transfer function in the bandpass filter transfer function form. The work also improved the fuzzy rules base and the neuron activation function mathematical model, which significantly accelerated the neuro-fuzzy controller training process. The transfer function frequency and time characteristics analysis showed that the system effectively controlled the engine and reduced vibration. Methods for ensuring a guaranteed stability margin and the synthesis of an adaptive filter were studied, which made it possible to achieve the system’s high stability and reliability. The results showed that the developed controller provided high stability with amplitude and phase margins, effectively compensating for changes in external conditions. Experimental studies have demonstrated that the control quality improved by 2.31–2.42 times compared to previous neuro-fuzzy controllers and by 5.13–5.65 times compared to classic PID controllers. Control errors were reduced by 1.84–2.0 times and 5.28–5.97 times, respectively, confirming the developed neuro-fuzzy controller’s high efficiency and adaptability.