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The most popular method for transporting fluids, and gases is through pipelines. For them to work correctly, regular inspection is necessary. Humans must enter potentially dangerous environments to inspect pipelines. As a result, pipeline robots came into existence. These robots aid in pipeline inspection, protecting numerous people from harm. Despite numerous improvements, pipeline robots still have several limitations. This paper presents the design and motion planning of a wheeled type pipeline inspection robot that can inspect pipelines having an inner diameter between 250 mm to 350 mm. The traditional wheeled robot design has three wheels fixed symmetrically at a 120° angle apart from each other. When maneuvering through a curved pipeline, this robot encounters motion singularity. The proposed robot fixes the wheels at different angles to address this issue, allowing the robot to stay in constant contact with the pipe's surface. Motion analysis is done for the proposed and existing robot design to study their behavior inside the pipeline. The result shows that the proposed robot avoids motion singularity and improves mobility inside pipelines. 3d printing technology aids in the development of the proposed robot. The experimental tests on the developed robot inside a 300 mm-diameter straight and curved pipeline show that the robot avoids motion singularity.

Solid-state welding that uses a non-consumable method is Friction Stir Welding (FSW). Because of the friction produced between the workpiece and the tool, the metal becomes plasticized and, with a particular feed rate and spinning speed, the tool is transverse in the desired direction. Present work was carried out with vibration data for the condition monitoring of the FSW weldment. The welding was done using an H13 hexagonal pin profile for two dissimilar metals (AA6063 and AA5052). The test was carried out with the process parameters such as 1600 rpm spindle speed, 1 mm/s feed rate, and a 5.9 mm penetrating depth. The requisite descriptive statistical characteristics have been extracted from the vibration data and feature selection through C4.5. The credal decision tree classifier (CDT) used to perform the classification of the faults like tunnel defect (TD), kissing bonds (KB), root sticking (RS), incomplete fusion (IF), flash (FL), weld root-flaw crack (WC), oxidation (OX) and lack of fill defects (LF) and the prediction accuracy was achieved to be 97.44% with the computational time of 0.46 s.

Over the past decades, developments and scientific breakthroughs in the field of robotics have shown the replacement of wheeled robots with legged robots, which are often inspired by the biological characteristics of legged animals. Many industries and urban-based applications promote quadruped robots because of their dexterous ability to efficiently handle multiple tasks in the working environment. Motivated from the recent works in the field of quadruped robots, this research aims to develop and investigate gaits for a 2 DoF mammal-inspired quadruped robot that incorporates 4 hip and 4 knee servo motors as its locomotion element. Forward and inverse kinematic techniques are used to determine the joint angle required for the locomotion and stability calculation are presented to determine the center of mass/center of gravity of the robot. Three types of gaits such as walk, trot, and pace are developed while keeping the center of mass inside the support polygon using a closed-loop control system. To minimize errors and improve the performance of the robot due to its non-linearity, a meta-heuristic algorithm has been developed and addressed in this work. The fitness function is derived based on the Euclidean distance between the target and robot’s current position and kinematic equations are used to obtain the relation between joints and coordinates. Based on the literature, particle swarm optimization (PSO) was found to be a promising algorithm for this problem and is developed using Python’s ‘Pyswarms’ package. Experimental studies are carried out quantitatively to determine the convergence characteristics of the control algorithm and to investigate the distance traveled by the robot for different target positions and gaits. Comparison between experimental and theoretical results prove the efficiency of the proposed algorithm and stability of the robot during various gait movements.

This paper presents a modular design of a wheeled wall-press type In-pipe Inspection Robot using 3D printing technology. The proposed robot (Kuzhali) consists of a prismatic joint to which the modular parts are attached. This mechanism ensures that the wheels are in contact with the pipe's internal surface. Finite Element Analysis (FEA) was performed on the custom-designed parts to see whether they can withstand the stress acting on them while passing through straight and curved pipelines. The analysis was carried out for curved pipes with a Factor of Safety of 1.5. To optimize the performance and durability of the robotic legs, the thickness of the components are modified. The design of the components was validated with an error of ± 5 %. The new design was fabricated and it was found that the proposed robot was able to move through straight, curved and L-branch pipes in the horizontal plane.

Water scarcity is a serious issue that has to be addressed in order to face the increasing water demands. Due to this issue, agricultural crops do not receive the required amount of water. So, it is necessary to have a proper technique to determine the water requirement for a particular crop. Evapotranspiration (ET), a process which is reliant on numerous climatic conditions, quantifies the loss of water from soil and crops through evaporation and transpiration processes respectively. Reference evapotranspiration ET is a concept of estimating ET from the reference surface which resembles an in-depth surface of green grass of stable height, actively growing, fully shading the surface with sufficient water. The amount of water required for a crop is thus determined by multiplying ETo with the crop coefficient (Kc) which depends on the growth stages and duration of a crop. So, evapotranspiration is considered to be one of the successful approaches to optimize the usage of water for crops. A literature survey is carried out on the popular methods of estimating ETo and their merits, demerits are discussed in this paper. Also, the impact of various climatic factors on ETo is presented. From the survey, it is known that ETo is estimated using conventional and non-conventional methods like Penman-Monteith, Blaney-Criddle, Hargreaves, ANN and WNN, regression and fuzzy logic. Humidity, temperature, wind speed, and solar radiation are the factors that have a major impact on estimating ETo. Generally, conventional methods are tedious since it requires experimental setups and more climatic data to determine ETo which are not available in many under developing countries. Thus, in this case, non-conventional methods are found to yield better results from the survey.

The focus of this work is to investigate the adhesion characteristics of a permanent magnet arrangement over ferromagnetic surfaces for wall climbing robot applications. The changes in wall thickness affect the adhesion characteristics of the robot, this in turn influences the payload and alters the operating conditions. The effect of varying the wall thickness on the adhesion strength of a wall-climbing robot is an area barely investigated and this is being explored in this work. A two-dimensional model of the adhesion mechanism and the ferromagnetic surface is developed and simulated in this study. The adhesion characteristics are studied for different thicknesses of the ferromagnetic surface with different grades of the magnet. Two different standoff distances which comprise the gap between the magnet and the surface to be inspected are investigated therein. Experimental studies are also carried out to measure the performance, and the results show a strong correlation with the simulation results. Simulation with experimental validation of magnetic adhesion presented will provide better insights into magnetic wall climbing systems.

In an expansive environment, due to continuous change of location of objects, tracking a moving object is a challenging task, in intelligent IoT service provisioned. Tracking of objects indoors using conventional methods like GPS can give out signals not as accurately as we get outside due to the signal attenuation, thus GPS gets poor results, moreover, the inclusion of other effective positioning modules increases the overall cost of production of the device. Nowadays, Wi-Fi networks are prevalent in most public buildings and do not require any additional infrastructure, so we have come with an idea of this is that we could propose an efficient and reliable Wi-Fi-real time tracking system with help access points and signals strength. We are using a combination of two approaches where the first one uses pre-recorded received signal strengths. From the access points, and data is transmitted from the user on the move. It is known as the fingerprinting method. The second approach is trilateration from the known access points coordinates from the user’s device to find the position. The combination of these steps improves the accuracy of the user position in an indoor environment. The experiments presented in this research helps us understand and provide the source of a foundation for the indoor/outdoor positioning.

In a wind turbine, blades are the most important component of wind capture in wind turbines as they easily become unreliable due to environmental conditions. This paper demonstrates the malfunction characterization of wind turbine blades by the use of vibration data via the credal decision tree (CDT). The defects on the blades are replicated to model the defects through machine learning. The extraction of functions (statistical functions) and the selection of the component (algorithm of decision tree J48) were employed to identify the best framework for defect classification. Using the credal decision tree, 82.67% of classification accuracy have been obtained with the Kappa statistic of 0.792 and mean absolute error of 0.0768.

The Internet of Things (IoT) is a continuing massive excitement for machine-to-machine communications to encourage individual devices to use the internet for data sharing. This work introduces the design and performance of continuous transformer monitoring and failure diagnostic and documents main distribution transformers indicators such as load current, transformer oil level, gas formation, and temperature. By using this project one must continuously take a look at it and it will reduce progress and maximize the stability, accuracy, and performance of this project. The sensors are used to track recurrent errors or voltage intake variations. The observed information is transmitted to the microcontroller and it tests parameter limitations that send it to the IoT web server using the IoT module. This data sharing means that the appropriate data are presented to the customer and before any significant errors occur according to the collected data the user will make useful choices.

In the ongoing processes of the thermal power plant, temperature and pressure regulation and control is important. The complete process involves two main sections, such as a temperature- and pressure sensor, which is regulated with a real-time control program and therefore employing a temperature- and pressure sensors. Within this research, a temperature sensor and pressure sensor centered on the MEMS is developed for the sensing and monitoring of temperature and pressure, along with the use of LabVIEW. This study is mainly used in the thermal plants where the high-frequency wave is being emitted. Since the wireless data transfer is being distracted by these frequencies noise this study with the help of CAN is been implemented for the information sharing and with the help of LabVIEW the process takes place in the industry can be controlled from a single monitoring room which reduces the manpower.