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Eddy current test (ECT) is one of the techniques for accurate testing as well as widely used in the aircraft and nuclear industry. Changes in thickness due to corrosion will affect the measurement of defects on pipes or plates. This will make an irrelevant decision as to whether the defect that occurs on the work piece is the thickness of the paint or corrosion. This problem can lead to the occurrence of leaking pipes, apart from the deterioration of the company’s productivity and most importantly endangering the safety of employees. Meanwhile, eddy current testing is one of the methods in NDT used to detect cracks or corrosion on the surface of carbon steel. In this work, GMR Eddy Current sensor is used to measure the corrosion on carbon steel pipes, and a permanent magnet is used to generate the magnetic field for corrosion detection. Also, this system is configured with an IoT Unmanned Aerial Vehicles (UAV) drone platform to facilitate access at different heights during the inspection process. The change of voltage amplitude at the GMR sensor on the test material proves the conditioning of the material either normal or corrosive. The result shows that when corrosion occurs on the pipe, the voltage amplitude increases up to 0.5% higher than normal. Finally, the metal loss is higher when the corrosion area is larger and at the same time it is directly proportional to the voltage amplitude

With fossil fuel sources diminishing and climate issues getting more and more serious each year, researchers around the world have been turning their attention to more sustainable and environmental friendly energy sources. While many new renewable energy sources emerge, solar remains as a popular and rapidly researched source. The efficiency and robustness of a solar energy conversion system is highly reliant on the continuous improvements and development in several aspects. Among others, is the monitoring and control system. The past few decades witness some rapid growth in the research and enhancements in embedded control and monitoring systems for photovoltaic solar systems. In this paper, a review on various developments of embedded monitoring and control systems for photovoltaic energy conversion systems is presented. The purpose of this article is to shed a better light on relevant techniques and methods. A number of research articles are reviewed and categorized based on the focus and mechanisms presented. A comprehensive study is carried out to reveal the improvement claims, outcomes, discoveries, and trade-offs of the techniques found in the literature, and a comprehensive conclusion is drawn at the end of the article.

Non-Destructing Testing (NDT) is crucial in the oil and gas industry to avoid fatal pipeline accidents and alarming property damage. Its general applications include the detection of pipeline defects in nuclear power plants, steam generator tubing, aircraft etc. Meanwhile, the Piezoelectric with Lamb Wave method testing system (PZ-LW), also known as Long Range Ultrasonic Testing (LRUT) is one of the methods in NDT used for crack detection on long-distance surface area. The current research proposed the ultrasonic testing probe design for the ideal gap between Piezo sensor resulting in reflection signal optimization for Piezoelectric sensor array in carbon steel pipe inspection. Response Surface Methodology (RSM) used the signal wave frequency excitation and the distance between the sensors array were optimized for appropriate multiple defect detection on 60 mm diameter carbon steel pipe sample. An Artificial Axial and Hole defect was utilized to test its efficiency. The analysis of the experimental results suggests the accuracy of PZ-LW system to amount to 98.55% when determining the defect location (hole defect), above 96.05% (axial defect measurement), while 98% and beyond (identification of defect shape). The inspection position is achievable until 311,399 m on free damaged pipe, 64,377 m (axial damage) and 61,267 m (hole crack defect damage). The reflection signal on PZ-LW inspection indicated good feedback signal amplitude with high distance detection in axial crack defect. The comparison of experimental and simulation results in Simulation of Non-Destructed Testing (SIMNDT) has successfully proven the consistency and accuracy of the proposed PZ-LW system for carbon steel pipe inspection.

The ever evolving complexity of real-world problems had become an impetus for the development of many new and efficient optimization algorithms. Meta-heuristics based on evolutionary computation and swarm intelligence are successful examples of nature-inspired optimization techniques. In this work, a new Dynamic Social Behavior (DSB) algorithm is proposed to solve global optimization problems. The DSB algorithm is based on the simulation of cooperative behavior of animal groups. In the proposed algorithm, individuals emulate the interaction of individuals based on biological laws of cooperative colony. This algorithm partially adopts the foraging strategy of animal groups and utilizes recruitment signal as a means of information transfer among individuals. In order to illustrate the proficiency and robustness of the proposed algorithm, it is compared with other well-known evolutionary algorithms. The comparison examines several series of widely used benchmark functions and an engineering problem on hyper beamforming optimization. The results testifies the superior performance of DSB compared with other state-of-the-art meta-heuristics.

Bending force and fatigue-induced crack is a generally assured trouble in industrial pipe line achieving their original design life. An amount of high-profile accidents has been covered in the past that involved bending and fatigue harm in structures. Such that incidences frequently encounter without prior warnings ascribable deficiency of proper crack monitoring method. Called for to discover and monitor the bending and fatigue crack, ultrasonic (UT) and acoustic emission (AE) technique, has been encountering growing concerns recently. This paper presenting the measurement and effect of bending test at carbon steel plate with different length and defect’s depth by using ultrasonic testing with considering the defect size at failure point. The zero-degree probe and 4MHz excitation frequency is used in UT measuring for condition of S55C carbon steel plate within before bending process and after bending process. From the result show that effect of bending force will increase the velocity (m/s), gain (dB), beam path (mm) and thickness plate (mm) based on the defect sizes. The increasing of four parameters can be explained namely 0.19% - 0.3% velocity, 17% - 20% gain, 10% - 12% beam path and 5.0% - 7.5% thickness of plate.

Eddy current testing (ECT) is an accurate, widely used and well-understood inspection technique, particularly in the aircraft and nuclear industries. The coating thickness or lift-off will influence the measurement of defect depth on pipes or plates. It will be an uncertain decision condition whether the defects on a workpiece are cracks or scratches. This problem can lead to the occurrence of pipe leakages, besides causing the degradation of a company&rsquo, s productivity and most importantly risking the safety of workers. In this paper, a novel eddy current testing error compensation technique based on Mamdani-type fuzzy coupled differential and absolute probes was proposed. The general descriptions of the proposed ECT technique include details of the system design, intelligent fuzzy logic design and Simulink block development design. The detailed description of the proposed probe selection, design and instrumentation of the error compensation of eddy current testing (ECECT) along with the absolute probe and differential probe relevant to the present research work are presented. The ECECT simulation and hardware design are proposed, using the fuzzy logic technique for the development of the new methodology. The depths of the defect coefficients of the probe&rsquo, s lift-off caused by the coating thickness were measured by using a designed setup. In this result, the ECECT gives an optimum correction for the lift-off, in which the reduction of error is only within 0.1% of its all-out value. Finally, the ECECT is used to measure lift-off in a range of approximately 1 mm to 5 mm, and the performance of the proposed method in non-linear cracks is assessed.

Increase of electricity demand and urbanization process has caused more power plants to be built to meet the demand of electricity. However, development of power plant will cause environmental issue for its surrounding. Necessary measures need to be taken to ensure social and environmental sustainability. Among the requirements in Malaysia, discharge of air pollution emission of a gas- or distillate-fired power plant has to comply with air pollution level as described in the Malaysian Ambient Air Quality Standards ((MAAQS) 2013 and the Environmental Quality (Clean Air) Regulations 2014. Pertaining to the environmental requirements, this paper is to investigate the ability of a regression based artificial intelligence tool, namely Extreme Learning Machine (ELM) in correlating multiple sources of big data sets and subsequently predicting the air pollution emission level from the chimney of a Combined Cycle Gas Turbine (CCGT) power plant. This emission data is later being used to ensure the clean air regulatory requirement is fulfilled. The big data sources that have been used in this work are meteorological data, terrain and land use data, historical emission data and power plant parameters particularly related to the point source emitter. With the correlation of multiple big data sources, Extreme Learning Machine (ELM) is then trained for the prediction of emission rate at certain targeted areas, which are classified as air sensitive receptors (ASR) surrounding the power plant. Nitrogen dioxide (NO2) is the key emission that has been studied in this paper due to its criticality towards environment. A standalone application program has been developed to employ ELM based big data analytics tool for the prediction of NO2 pollution emission. The output of ELM is analyzed to ensure the emission at ground level of ASR is maintained within allowable limit.