Mining dump trucks have the advantages of high load-bearing weight and high efficiency, thus the trucks are mainly used in large construction sites such as opencast mining and transport infrastructure construction. Hydro-pneumatic suspensions are widely used in large construction vehicles due to their non-linear characteristics of stiffness and damping, which can better adapt to changes in external load excitation. In order to solve the problem that the suspension and tire components are easy to be damaged due to the high stiffness of the single-chamber hydro-pneumatic suspension (SCHPS) under the loading impact condition, a two-stage pressure hydro-pneumatic suspension (TPHPS) system is designed to meet the needs of the new type mining dump truck. Firstly, the structure and working principle of the two-stage pressure hydro-pneumatic suspension are analyzed. Secondly, the inelastic force mathematical model and damping force model of the TPHPS system are established. Finally, the TPHPS system model of the mining dump truck is established by using the AMEsim software. Through simulation and experiment, the suspension cylinder pressure, dynamic deflection, acceleration and power spectral density of the SCHPS system and the TPHPS system in the process of mineral loading are compared and analyzed, and the damping characteristics of the suspension systems are further obtained. The research results can provide reference for the design of hydro pneumatic suspension system of new mining dump trucks.

In the industry, induction motors operate in difficult environments. Monitoring the performance of motors in such circumstances is significant which can provide a reliable system. This paper intends to develop a new model for fault diagnosis based on the knowledge of transfer learning using the ImageNet dataset. The development of this framework provides a novel technique for the diagnosis of single and multiple induction motor faults. A transfer learning network based on a VGG-19 convolutional neural network (CNN) is implemented, which achieves a high amount of accuracy with minimum training loss compared to the exciting traditional learning methods. Thermal images with different induction motor conditions are captured and applied as inputs to investigate the reliability of the proposed model. The implementation of this task is to use VGG19 (CNN) based pre-trained network with a dense-connected classifier to predict the true class. The use of a transfer learning network allows for the attributes to be automatically extracted and associated with the decision-making part with a quick and faster training time. The experimental results confirm that the proposed model is promising and successfully able to classify the induction motor faults with high classification accuracy of 99.8%. Furthermore, this model can be further used for other applications based on related topics.

In the construction industry, excavators play a major role in any project. A transition from manual to autonomous operation of them can save a significant amount of costs and time.
The main contribution of this study is that a prototype of a flying excavation platform using a drone is presented, which flies autonomously and carries out excavation operations while executing a planned mission through the Ardupilot platform. For the excavation, the platform can exert a force of up to 42N with its integrated excavating arm to dig and lift the soil in the flight operation. This development is the first attempt in the recent drone and construction industry, which allows excavations can be done while in flight. A sensing algorithm has also been developed by using a 3D depth camera that facilitates fully autonomous excavations by detecting the depth of the soil to dig.

The Elastic Emission Machining (EEM) is a finishing process for the surface of ceramic parts. In the EEM, the ceramic part is submerged into a liquid interface that contains abrasive particles, and then a spindle rotates a spheric tool rapidly, forcing the abrasive particles into contact with the ceramic part surface. Due to the fact that it is a finishing process, the part that goes through EEM have a high aggregated value from previous machining process. Thus, with monitoring of this process, failures that would cause the parts to be discarded can be detected. One of the most preeminent non-intrusive methods of machining processes monitoring is the digital processing of in-situ acquired acoustic emission (AE) signals. In recent published papers, a low-cost piezoelectric transducer has shown great results as an alternative to traditional AE sensors when applied in monitoring of other machining processes such as grinding and dressing. Among the methods of evaluating sensor’s response, the Pencil Lead Break (PLB) method has shown to be effective when applied to ceramic parts. The presents work aims to evaluate the liquid interface influence on the low-cost piezoelectric transducer response by means of the PLB method for EEM monitoring. The results obtained shows that there is great influence on the signal obtained when the piezoelectric transducer is in contact with the liquid interface. The results also shows that the influence is more preeminent on certain frequency values.

This work is devoted to the optimization of the trajectory of a robotic system for aliquoting biosamples, consisting of serial and parallel manipulators. Optimization consists of two stages. At the first stage, optimization constraints associated with the workspace, taking into account the ranges of permissible values ​​of the angles of the drive rotational joints, the link interference and singularities. The workspace in the space of input and output coordinates is represented as a partially ordered set of integers. At the second stage, restrictions are formed related to the objects that are in the workspace during the aliquoting process, such as the body of the robotic system, test tubes and racks. The condition for excluding collisions of the manipulator with other objects is provided by geometric decomposition of objects and exclusion of areas corresponding to external objects from the set describing the workspace of the manipulator. Optimization is performed in the space of input coordinates. The objective function is proportional to the duration of movement along the trajectory. The possibility of evolutionary algorithms application for solving this problem is analyzed. An assessment of their performance is given. Optimization and export of the resulting trajectory are implemented in software, which allows you to verify the optimization results on a virtual model. The simulation results are presented.

This work is devoted to the analysis of the application of object detection algorithms for recognizing the level of blood in a test tube. Based on the initial data and data received from the sensory system, pattern recognition is performed and decisions are made to complete the task. It is necessary to control the level of the liquid being determined in the test tube to solve the technological problems of biomaterial aliquoting. In this case, it should be taken into account that the liquid has two fractions that differ in color: a blood clot and serum. The glare of the tube surface and lighting features should also be taken into account. These factors greatly complicate the operation of the vision system. Software in Python language have been developed that implement the ability of a vision system to recognize the level of blood in test tubes and visualize the recognized blood level in several ways: contour selection, dot marking, color filtering. The developed methods are supposed to be applied for biosamples aliquoting using a delta robot in a multirobotic system, which will increase the productivity of ongoing biomedical research through the application of new technical solutions and principles of intelligent robotics. Visualized results of the work of the programs under consideration are presented and an assessment of the quality of recognition implementation is given.

In this paper, a full analysis of the voltage vectors (VVs) in the Direct Torque Control (DTC) algorithm is presented to determine the accurate effect on both the torque and the flux throughout the entire sector with the change of the speed and the load. The analytical analysis showed that the application of specific VVs results in false switching states in a specific range of the sector with the change of speed, load, or both. More specifically, some switching states are not fully defined. These wrong states are called Uncontrollable Angles (UCAs). The authors proposed a robust and effective scheme that ensures the elimination of the (UCAs) over the entire speed range. Through the analysis, it was found that the basic scheme suffered from UCAs of large values ​​at medium and high speeds. Accordingly, a special strategy for medium and high speeds with 18 Sub-Sectors (SSs) was proposed while maintaining the basic 6 sectors strategy for low speed. The transition speed between the two strategies was determined to ensure the absence of UCAs. Furthermore, to verify the effectiveness of this method, MATLAB/ Simulink results of the drive system were accomplished and validated.