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When studying porous materials, most acoustical and geometrical parameters can be affected by the presence of uncertainties, which can reduce the robustness of models and techniques using these parameters. Hence, there is a need to evaluate the effect of these uncertainties in the case of modeling acoustic problems. Among these evaluation methods, the Monte Carlo simulation is considered a benchmark for studying the propagation of uncertainties in theoretical models. In the present study, this method is applied to a theoretical model predicting the acoustic behavior of a porous material located in a duct element to evaluate the impact of each input error on the computation of the acoustic proprieties such as the reflection and transmission coefficients as well as the acoustic power attenuation and the transmission loss of the studied element. Two analyses are conducted; the first one leads to the evaluation of the impacts of error propagation of each acoustic parameter (resistivity, porosity, tortuosity, and viscous and thermal length) through the model using a Monte Carlo simulation. The second analysis presents the effect of propagating the uncertainties of all parameters together. After the simulation of the uncertainties, the 95% confidence intervals and the maximum and minimum errors of each parameter are computed. The obtained results showed that the resistivity and length of the porous material have a great influence on the acoustic outputs of the studied model (transmission and reflection coefficients, transmission loss, and acoustic power attenuation). At the same time, the other physical parameters have a small impact. In addition, the acoustic power attenuation is the acoustic quantity least impacted by the input uncertainties.

The success or failure of a drone mission depend on several elements, including the drone’s mass and payload, the surrounding environment, the battery’s state, and the propulsion system’s performance. Several studies have been undertaken addressing the propeller performance of this final component, particularly for fixed-wing or quad copter drones with ducted propellers. However, the shape of propeller blades used on tiny civil drones has not drawn as much attention. In this research work, the performance characteristics of a 10 × 4.7-inch curved tip propeller were explored, with the Advanced Precision Composite drone propeller serving as a design reference. This study seeks an alternative to ducted propellers when mass or size limitations prevent their use. The Computational Fluid Dynamics model was confirmed by comparing simulation results to the experimental propeller data source established by the University of Illinois at Urbana-Champaign, starting with a standard type propeller and using thrust coefficient as the key performance indicator. Moreover, the data analysis for the bended tip propeller replicating the well-established model, revealed the benefits as well as the drawbacks of such propellers on the mission profile and the battery lifespan of a quad copter Unmanned Air Vehicle.

Duct silencers provide effective noise reduction for heating, ventilation and air conditioning systems. These silencers can achieve an excellent sound attenuation through the attributes of their design. The reactive silencer works on the principle of high reflection of sound waves at low frequencies. On the other hand, the dissipative silencer works on the principle of sound absorption, which is very effective at high-frequencies. Combining these two kinds of silencers allowed covering the whole frequency range. In this paper, the effect of liner characteristics composed of a perforated plate backed by a porous material and geometry discontinuities on the acoustic power attenuation of lined ducts is evaluated. This objective is achieved by using a numerical model to compute the multimodal scattering matrix, thus allowing deducing the acoustic power attenuation. The numerical results are obtained for six configurations, including cases of narrowing and widening of a radius duct with sudden or progressive discontinuities. Numerical acoustic power attenuation shows the relative influence of the variation in the values of each parameter of the liner, and of each type of radius discontinuities of ducts.

The detection and location of pipeline leakage can be deduced from the time arrival leak signals measured by acoustic sensors placed at the pipe. Ongoing research in this field is primarily focused on refining techniques for accurately estimating the time delays. This enhancement predominantly revolves around the application of advanced signal processing methods. Additionally, researchers are actively immersed in the utilization of machine learning approaches on vibro-acoustic data files, to determine the presence or absence of leaks. Less attention has been given to evaluating the sensitivity, performance, and overall effectiveness of these sensors in leak detection; although acoustic methods have been successfully used for leak detection in metallic pipes, they are less effective in plastic pipes due to the high attenuation of leak noise signals. The primary thrust of this research centers on identifying sensors that not only possess sensitivity but also exhibit high efficiency. To accomplish this goal, we conducted an exhaustive evaluation of the performance of three distinct categories of acoustic sensors employed for detecting water leaks in plastic pipes: specifically, lead zirconate titanate (PZT) sensors, polyvinylidene fluoride (PVDF) sensors, and surface acoustic wave (SAW) sensors. Our evaluation encompassed the performance of PVDF and SAW sensors in leak detection, comparing them to PZT sensors under a variety of conditions, including different leak sizes, flow rates, and distances from the leak. The results showed that all three sensors, when they were placed in the same position, were able to detect water leaks in plastic pipes with different sensitivities. For small leaks (1 mm, 2 mm), the PVDF sensor showed the greatest sensitivity (0.4 dB/L/h, 0.33 dB/L/h), followed by the SAW sensor (0.16 dB/L/h, 0.14 dB/L/h), and finally the PZT (0.13 dB/L/h, 0.12 dB/L/h). Similarly, for larger leaks (4 mm, 10 mm), the PVDF sensor continued to show superior sensitivity (0.2 dB/L/h, 0.17 dB/L/h), followed by the SAW sensor (0.13 dB/L/h, 0.11), and finally the PZT sensor (0.12 dB/L/h, 0.1 dB/L/h), outperforming the PZT sensor. This suggests that SAW and PVDF sensors, have the potential to serve as valuable, cost-effective alternatives to traditional commercial leak noise transducers. The outcomes of this comparative study involving three acoustic sensors hold the potential to advance the development of robust and dependable systems for the detection of water leaks in plastic pipelines.

Vibration condition monitoring is a non-devastating technique that can be performed to detect tooth cracks propagating in gear systems. This paper proposes to apply a new methodology using time-domain analysis, frequency-domain analysis, and statistical process control charts (SPCC) for gear crack detection of a 10 DOF dynamic model of spiral bevel gear system (SBGS). The gear mesh stiffness effect used in the model has been studied analytically for different levels of crack faults. Adding Gaussian white noise is discussed as the first step to simulating the initial modeling signals of real-world conditions. Second, time-domain signal analysis was performed to identify periodic vibration pulses as failure components and calculate the statistical standard deviation (STD) feature as a fault-sensitive feature. Third, a fast Fourier transform (FFT) to time signals of the variable gear mesh stiffness was applied to determine the gear mesh frequency and sidebands to detect tooth cracks. Fourth, the SPCC was designed using the Shewhart X-bar chart and an exponentially weighted moving average (EWMA) chart based on the STD feature of the healthy gears. Finally, in the testing stage, the control charts are carried out with simulation signals under faulty conditions to detect the different levels of cracks. The results showed that the EWMA chart outperformed the time domain analysis, frequency domain analysis, and Shewhart X-bar chart in detecting all levels of cracks at an early stage.

Production systems must be able to adapt to increasingly frequent internal and external changes. Cyber-Physical Production Systems (CPPS), thanks to their potential capacity for self-reconfiguration, can cope with this need for adaptation. To implement the self-reconfiguration functionality in economical and safe conditions, CPPS must have appropriate tools and contextualized information. This information can be organized in the form of an architecture. In this paper, after the analysis of several holonic and nonholonic architectures, we propose a holonic architecture that allows for reliable and efficient reconfiguration. We call this architecture QHAR (Q-Holonic-based ARchitecture). QHAR is constructed based on the idea of a Q-holon, which has four dimensions (physical, cyber, human, and energy) and can exchange three flows (energy, data, and materials). It is a generic Holon that can represent any entity or actor of the supply chain. The QHAR is structured in three levels: centralized control level, decentralized control level, and execution level. QHAR implements the principle of an oligarchical control architecture by deploying both hierarchical and heterarchical control approaches. This ensures the overall system performance and reactivity to hazards. The proposed architecture is tested and validated on a case study.

In recent years, collaborative robots have become one of the main drivers of Industry 4.0. Compared to industrial robots, automated guided vehicles (AGVs) are more productive, flexible, versatile, and safer. They are used in the smart factory to transport goods. Today, many producers and developers of industrial robots have entered the AGV sector. However, they face several challenges in designing AGV systems, such as the complexity and discontinuity of the design process, as well as the difficulty of defining a decentralized system decision. In this paper, we propose a new integrated design methodology based on swarm robotics to address the challenges of functional, physical, and software integration. This methodology includes two phases: a top-down phase from requirements specification to functional and structural modeling using the systems modeling language (SysML); with a bottom-up phase for model integration and implementation in the robot operating system (ROS). A case study of an automated guided vehicle (AGV) system was chosen to validate our design methodology and illustrate its contributions to the efficient design of AGVs. The novelty of this proposed methodology is the combination of SysML and ROS to address traceability management between the different design levels of AGV systems, in order to achieve functional, physical and software integration.

Condition monitoring of rotating machinery plays an important role in reducing catastrophic failures and production losses in the 4.0 Industry. Vibration analysis has proven to be effective in diagnosing rotating machine failures. However, identifying bearing defects based on vibration analysis remains a difficult task, especially in non-stationary operation conditions. This work aims to automate the process of identifying bearing defects under variable operating speeds. Based on an order analysis technique, three frequency domain features: Spectrum peak Ratio Outer (SPRO), Spectrum peak Ratio Inner (SPRI), and Spectrum peak Ratio Rolling element (SPRR) are updated to perform with non-stationary signals. The updated features are extracted from vibration data of a real ball bearing system. They are retained to build a predictive multi-kernel support vector machine (MSVM) classification model. Therefore, the effectiveness of the proposed features is evaluated based on the performance of the constructed classifier. The updated features deployed have proven their effectiveness in identifying bearing: outer race, inner race, ball, and combined defects under variable speed conditions.

In this paper we study a generic model of a nonlinear quasiperiodic vibration energy harvester (VEH) based on electromagnetic transduction. The proposed device consists of multiple moving magnets guided by elastic beams and coupled by repulsive magnetic forces. A system of two degrees-of-freedom (DOFs) with tunable nonlinearity and mode localization is experimentally validated. The validated 2-DOFs harvester is optimized using a multiobjective optimization procedure to improve its harvested power and frequency bandwidth. An efficient criterion using the modal kinetic energy of the finite element model is proposed to quantify the energy localized in the structure perturbed zones. Afterward, this concept has been generalized to a 5-DOFs VEH with two perturbed DOFs oscillators and the optimal performances are derived using a multiobjective optimization. This proposed model enables a significant increase in the harvested power and frequency bandwidth by 101% and 79%, respectively, compared to that of the 2-DOFs device. Moreover, it has been shown that harvesting energy from two perturbed magnets among five provides almost the same amount of harvested energy and enhances the frequency bandwidth by 18% compared to those of the periodic system. Consequently, the harvester can be improved by reducing the transduction circuits number and the manufacturing cost.

Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the squared envelope is a relatively simple yet effective method to perform fault detection, fault identification and fault trending under constant and time-varying operating conditions. However, its performance is impeded by the presence of impulsive signal components attributed to impulsive noise or the presence of other damage modes in the machine. In this work, it is proposed that the synchronous median of the squared envelope should be used instead of the synchronous average of the squared envelope for gearbox fault diagnosis. It is shown on numerical and experimental datasets that the synchronous median is more robust to the presence of impulsive signal components and is therefore more reliable for estimating the condition of specific machine components.

Gearboxes usually run under fluctuating load conditions during service, however most of papers available in the literature describe models of gearboxes under stationary load conditions. Main task of published papers is fault modeling for their detection. Considering real situation from industry, the assumption of stationarity of load conditions cannot be longer kept. Vibration signals issued from monitoring in maintenance operations differ from mentioned models (due to load non-stationarity) and may be difficult to analyze which lead to erroneous diagnosis of the system. The objective of this paper is to study the influence of time varying load conditions on a gearbox dynamic behavior. To investigate this, a simple spur gear system without defects is modeled. It is subjected to a time varying load. The speed-torque characteristic of the driving motor is considered. The load variation induces speed variation, which causes a variation in the gearmesh stiffness period. Computer simulation shows deep amplitude modulations with sidebands that don't differ from those obtained when there is a defective tooth. In order to put in evidence the time varying load effects, Short Time Fourier Transform and then Smoothed Wigner-Ville distribution are used. Results show that the last one is well suited for the studied case.