Measuring vibration velocity is one of the most common techniques to estimate the condition of industrial machines. At a constant operating point, as the vibration velocity value increases, the machine’s condition worsens. However, there are no precise thresholds that indicate the condition of a machine at different operating points. Also, the axial piston pump, which is the subject of the article, is a device that generates stronger vibrations by design and cannot be enclosed in general vibration norms. Due to different use cases and work regimes of axial piston pumps, the need to determine whether the device is working correctly for a broad spectra of operating points emerges. This article aims to present and compare different methods for vibration velocity prediction for axial piston pumps with use of neural networks including dense networks, variants of recurrent neural networks, and ensemble methods. The result of this research consists of models that have performance metrics that clearly indicate whether the monitored pump has malfunctioned or not across a wide variety of operating points, working conditions, and in case of reassembling. A detailed analysis of the influence of available measured variables on the performance of models is also provided. The conclusion is that the application of commercial implementation of developed models is reasonable in the context of both performance quality and costs of sensors needed to provide the necessary data.

The effect of the novel controlled thermomechanical treatment, including torsion components in the elastic strain range during the isothermal holding on the microstructure and mechanical properties of the high-carbon nanobainitic steel, was investigated. TEM observations of the thermo-mechanically treated steel revealed bainitic ferrite laths with an average size of 68 ± 40 nm and films of retained austenite with an average size of 34 ± 17 nm, along with the blocky morphology of retained austenite in sub-micron scale. The XRD synchrotron diffraction allows estimating the amount of retained austenite at 43.1 ± 1.2% volume fraction with a carbon concentration of 1.17 ± 0.09 wt.%. Furthermore, the deconvolution of (200) Fe-γ reflections corresponding to two different low-carbon and high-carbon retained austenite peaks and, simultaneously, the blocky and film-like retained austenite was performed. In addition, the Nishiyama–Wassermann (N–W) crystallographic orientation relationship between bainitic ferrite and retained austenite was described as dominant using the misorientation distribution function (MDF). The crystallographic texture results indicated that the main growth of bainitic ferrite plates occurred after removing external stress during isothermal holding. The tensile tests and hardness measurements showed a high tensile strength achieved mainly by nano-metric bainitic ferrite plates and a high dislocation density. The high level of elongation is most likely attained due to a high amount of retained austenite in steel and both TRIP and TWIP effects during tensile deformation.

This article aims to present the real-world implementation of an anomaly detection system of a hydraulic power unit. Implementation involved the Internet of Things approach. A detailed description of the system architecture is provided. The complete path from sensors through PLC and the edge computer to the cloud is presented. Some technical information about hydraulic power units is also given. This article involves the description of several model-at-scale deployment techniques. In addition, the approach to the synthesis of anomaly and novelty detection models was described. Anomaly detection of data acquired from the hydraulic power unit was carried out using two approaches, statistical and black-box, involving the One Class SVM model. The costs of cloud resources and services that were generated in the project are presented. Since the article describes a commercial implementation, the results have been presented as far as the formal and business conditions allow.

Hydraulic systems are widely used in drive and control systems of multiple devices. Various types of control valves are used to determine the appropriate direction of the working fluid movement. This article provides flow analysis of a two-way hydraulic control valve in a cartridge housing. The flow rate through the valve is controlled by a spring and an electromagnet. Design of a poppet ensures tightness in both directions in the cut-off position. Accordingly, there is no need to use additional shut-off valves. However, the fluid stream entering the valve is divided into several jets, due to the cartridge design. This makes the flow complex and difficult to be modelled. Therefore, the advanced ANSYS/Fluent software was used to carry out the analysis. The conducted tests allowed the influence of selected geometrical and physical parameters on the valve characteristics to be examined.

Herein, we have reported a facile and green synthesis approach of Ag NP decorated reduced graphene oxide (RGO) through an in situ self-assembly method in the presence of l-methionine (l-Met) as reducing and stabilizing agent. The electronic properties, crystal structure, and morphology of the as-synthesized RGO–Ag nanocomposite were investigated by UV-Visible (UV-Vis) spectroscopy, Fourier transform-infrared (FTIR), X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) techniques. UV-Vis and FTIR show the effective reduction of GO and the formation of Ag NPs using l-Met. FESEM, TEM, and XRD analysis show the successful impregnation of Ag NPs into RGO with a 23 nm average crystallite size. The RGO–Ag nanocomposite with NaBH4 shows a fast-catalytic reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AMP). The enhanced catalytic activity of RGO–Ag nanocomposites can be attributed to the synergistic effect of improved adsorption capacity and the absence of agglomeration of Ag nanoparticles. Moreover, RGO–Ag showed strong antibacterial activity against B. subtilis and E. coli.