• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.195-201
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• 2016

Eddy current testing (ECT) is commonly applied for the inspection of automated production lines of metallic products, because it has a high inspection speed and a reasonable price. When ECT is applied for the inspection of a metallic object having an uneven target surface, such as the spline gear of a spline shaft, it is difficult to distinguish between the original signal obtained from the sensor and the signal generated by a defect because of the relatively large surface signals having similar frequency distributions. To facilitate the detection of defect signals from the spline gear, implementation of high-order filters is essential, so that the fault signals can be distinguished from the surrounding noise signals, and simultaneously, the pass-band of the filter can be adjusted according to the status of each production line and the object to be inspected. We will examine the infinite impulse filters (IIR filters) available for implementing an advanced filter for ECT, and attempt to detect the flaw signals through optimization of system design parameters for detecting the signals at the system level.

• Fire Science and Engineering
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• v.23 no.3
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• pp.40-47
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• 2009

Nowadays the diversity and large-capacity of electric appliances are strong effect on electrical fires augment in an alarming way. But, as the inactive response characteristics of the existing RCD (Residual Current protective Device) used on low voltage power distribution system, so control of overload and electric short circuit faults, major causes of electrical fires, are not enough. Therefore, this paper is confirmed the unreliability of the existing RCD by electrical fault simulator and is proposed a Electrical Fire Disaster Prevention Device (EFDPD) by using a high precision current sensor (namely, reed switch) for the prevention of electrical disasters in low voltage power distribution system caused by overload or electric short circuit faults. The sensitive reed switch in the proposed EFDPD exactly detects the increased magnetic flux with the overload or the short current caused by a number of electrical faults, and the following, the EFDPD has double protection function which operates self circuit breaker or rapidly cuts off the existing RCD. The proposed EFDPD is confirmed the excellent characteristics in response velocity and accuracy in comparison with the conventional circuit breaker through various operation performance analysis. The proposed EFDPD can also prevent electrical disaster, like as electrical fires, which resulted from the malfunction and inactive response characteristics of the existing RCD.

• Smart Structures and Systems
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• v.29 no.5
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• pp.661-675
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• 2022

In this paper, an electric vehicle drives with efficient control and low cost hardware using four quadrant DC converter with Permanent Magnet Direct Current (PMDC) motor fed by DC boost converter is presented. The main idea of this work is to improve the energy efficiency of the conversion chain of an electric vehicle by inserting a boost converter between the battery and the four quadrant-DC motor chopper assembly. Consequently, this method makes it possible to maintain the amplification gain of the 4 quadrant chopper constant regardless of the battery voltage drop and even in the presence of a fault in the battery. One of the most important control problems is control under heavy uncertainty conditions. The higher order sliding mode control technique is introduced for the adjustment of DC bus voltage and mechanical motor speed. To implement the proposed approach in the automotive field, experimental tests were carried out. The performances obtained show the usefulness of this system for a better energy management of an electric vehicle and an ideal control under different operating conditions and constraints, mostly at nominal operation, in the presence of a load torque, when reversing the direction of rotation of the motor speed and even in case of battery chamber failure. The whole system has been tested experimentally and its performance has been analyzed.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.5
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• pp.423-430
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• 2009

Recently as the inactive response characteristics of the existing RCD used on low voltage power distribution system, so control of overload and electric short circuit faults, major causes of electrical fires, are not enough. Therefore, this paper confirms the unreliability of the existing RCD by electrical fault simulator and proposes a EFDPD by using semiconductor switching devices and a high precision current sensor (namely, reed switch) for the prevention of electrical disasters in low voltage power distribution system caused by overload or electric short circuit faults. The sensitive reed switch in the proposed EFDPD exactly detects the increased magnetic flux with the overload or the short current caused by a number of electrical faults, and the following, the self circuit breaker in EFDPD rapidly cuts off the system. The proposed EFDPD confirms the excellent characteristics in response velocity and accuracy in comparison with the conventional circuit breaker through various operation performance analysis. The proposed EFDPD can also prevent electrical disasters, like as electrical fires, which resulted from the malfunction and inactive response characteristics of the existing RCD.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.659-666
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• 2006

Servo-motors are used as key components of automated system by performing precise motion control as accurate positioning and accurate speed regulation in response to the commands from computers and sensors. Especially, the linear brushless servo-motors have numerous advantages over the rotary servo motors which have connection with the friction induced transfer mechanism such as ball screws, timing belts, rack/pinion. This paper proposes an estimation method of unknown motor system parameters using the informations from the sinusoidal driving type linear brushless DC motor dynamics and outputs. The estimated parameters can be used to tune the controller gain and a disturbance observer. In order to meet this purpose high performance Digital Signal Processor, TMS320F240, designed originally for implementation of a Field Oriented Control(FOC) technology is adopted as a controller of the liner BLDC servo motor. Having A/D converters, PWM generators, rich I/O port internally, this servo motor application specific DSP play an important role in servo motor controller. This linear BLDC servo motor system also contains IPM(Intelligent Power Module) driver and hail sensor type current sensor module, photocoupler module for isolation of gate signals and fault signals.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.