Eddy current testing plays an important role in numerous industries, particularly in material coating, nuclear and oil and gas. However, the eddy current testing technique still needs to focus on the details of probe structure and its application. This paper presents an overview of eddy current testing technique and the probe structure design factors that affect the accuracy of crack detection. The first part focuses on the development of different types of eddy current testing probes and their advantages and disadvantages. A review of previous studies that examined testing samples, eddy current testing probe structures and a review of factors contributing to eddy current signals is also presented. The second part mainly comprised an in-depth discussion of the lift-off effect with particular consideration of ensuring that defects are correctly measured, and the eddy current testing probes are optimized. Finally, a comprehensive review of previous studies on the application of intelligent eddy current testing crack detection in non destructive eddy current testing is presented.

Eddy current testing (ECT) is one of the non-destructive evaluation techniques widely used, especially in oil and gas industries. It characterized noisy data to the less-than-perfect detection and as an indication of serious false alarm problem. However, not many researchers have described in detail the intelligent ECT crack detection system. This paper introduces a review of ECT technique and factors that affect the signal fundamental according to the hardware and software development. First, describe the magnetic excitation resources including the sinusoidal and pulse exciting signal. Second, outlines explanation about the ECT probe. The explanations are more about the probes development for air core probe and giant magnetoresistance probe. Third, there is discussion on ECT circuit that used including ECT system, ECT rotating magnetic field and application measurement for optimal control parameters. The defect in characterizations and measurement are discussed on the fourth part of this paper. The fourth part discusses the ECT lift-off compensation including the lift-off and application of intelligent technique in ECT. The limitations of lift-off for coil probe and compensation techniques also discussed in this part. Finally, a comprehensive review of previous studies on the application of intelligent ECT crack detection in nondestructive ECT is presented.

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’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’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.

Non-destructive evaluation (NDE) plays an important role in many industrial fields, such as detecting cracking in steam generator tubing in nuclear power plants and aircraft. This paper investigates on the effect of the depth of the defect, width of the defect, and the type of the material on the eddy current signal which is modeled by an adaptive neuro-fuzzy inference system (ANFIS). A total of 60 samples of artificial defects are located 20 mm parallel to the length of the block in each of the three types of material. A weld probe was used to inspect the block. The ANFIS model has three neurons in the input layer and one neuron in the output layer as the eddy current signal. The used design of experiments (DOE) software indicates that the model equations, which contain only linear and two-factor interaction terms, were developed to predict the percentage signal. This signal was validated through the use of the unseen data. The predicted results on the depth and width of defect significantly influenced the percentage of the signal (p < 0.0001) at the 95% confidence level. The ANFIS model proves that the deviation of the eddy current testing measurement was influenced by the width and depth of the defect less than the conductivity of the materials.

<span lang="EN-GB">Solar energy has become one of the most studied topic in the field of renewable energy. In this paper, an artificial intelligent approach is proposed for the optimization of a photovoltaic solar energy harvesting system. An Electromagnetism-Like Mechanism Algorithm (EM) has been developed to search for the hourly optimum tilt angles for photovoltaic panels. In order to investigate the effect of the search step size on the efficiency and overall accuracy of the algorithm, the EM has also been modified into several variants with different search step size settings. Experimental findings show that EM with bigger search lengths has the advantage of reaching a near optimum tilt angle in earlier iterations but less accurate. EM with smaller step lengths, on the other hand, can hit a relatively more optimum tilt angle in the process. During the peak of the power generation at noon, EM with smaller search stes found an optimum tilt angle which yielded additional 3.17W of power compared to a fixed panel. We thus conclude that the proposed EM performs well in optimizing the tilt angle of a photovoltaic solar energy harvesting system.</span>

In power energy distribution networks, switchgear is considered critical equipment. This is because the act of monitoring the operation and condition of switchgear, as well as performing the required corrective maintenance on any potentially problematic equipment, is critical. A single event may harm thousands of customers over time and pose a significant risk to operational staff. Many considerations must be put in place before using outages to switch down the system since they may raise maintenance costs and disrupt the power supply to users. As a result, proper interpretation of switchgear status evaluations is critical for the early identification of possible faults. Existing ultrasound condition-based monitoring (CBM) diagnostic testing techniques require the tester to manually interpret test data. This study aims to review the status of the recent development of CBM techniques with faults in switchgear. The current trend in electrification will be toward the safety and sustainability of power grid assets, which involves an evaluation of the electrical systems’ and components’ health and grids for medium-voltage distribution. This work provides a current state-of-the-art evaluation of deep learning (DL)-based smart diagnostics that were used to identify partial discharges and localize them. DL techniques are discussed and categorized, with special attention given to those sophisticated in the last five years. The key features of each method, such as fundamental approach and accuracy, are outlined and compared in depth. The benefits and drawbacks of various DL algorithms are also examined. The technological constraints that hinder sophisticated PD diagnostics from being implemented in companies are also recognized. Lastly, various remedies are suggested, as well as future research prospects.

Ultrasonic testing or commonly known as UT is one of the non-destructive testing technique and widely used in oil and gas industrial inspection. This technique mostly used in defect or crack identification of the pipeline and also used for flaw detection/evaluation, dimensional measurements, and material characterization. This paper presents the effect of heat treatment for S55C carbon steel in attenuation measurement by using ultrasonic testing including annealing, tempering, and quenching process.  Seawater and oil are used as a medium of quenching process. The fixed excitation frequency at 4 MHz is used and 0 degrees with double crystal is implemented in this measurement. The thicknesses of blocks used are as the sample from 30mm until 80mm. The result shows that the measurement of material attenuation will be decreased after annealing, tempering and quenching process from 40% until 99% compared to before the heat treatment process. The highest attenuation decreasing can be seen on the sample block with the 30mm thickness in the heat treatment process.

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