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Extension of Linear Operating Range for Linear Variable Differential Transformer Using Its Inverse Transfer Characteristic
(2023-01-01)
Petchmaneelumka, Wandee
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Songsuwankit, Kanoknuch
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Rerkratn, Apinai
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Gullayanon, Rutchanee
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Riewruja, Vanchai
An analog circuit technique to realize an inverse transfer characteristic of a linear variable differential transformer (LVDT) is presented in this paper. Practically, the structure of the LVDT causes a narrow linear operating range compared with its full stroke range. However, a large linear operating range requires a huge structure for the LVDT, making it unsuitable for a small or compact measurement system. The proposed technique can be used in a commercial LVDT to extend the linear operating range to its full stroke range. The technique utilizes an inherent behavior of an operational transconductance amplifier (OTA) to emulate the LVDT transfer characteristic. The LVDT transfer characteristic generated by the OTA is used as a feedback path of the inverting amplifier formed by an operational amplifier (opamp) to realize the inverse transfer characteristic. The residual error due to the OTA behavior is very small and can be neglected without adversely affecting the performance of the proposed technique. All devices used in the proposed scheme are commercially available. The attractive features of the proposed technique are its simple configuration, small size, low cost, and high accuracy. The performance of the proposed technique is discussed in detail and confirmed by its experimental implementation. Measurement results demonstrate that the linear operating range of the commercial LVDT used in this study can be extended by a factor of more than 2.4, and a fullscale percentage error of about 0.068% was obtained.
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Finding Picking Point Using Straight Line Detection of Label Attachment Machine
(2022-01-01)
Prum, Lipheng
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Gullayanon, Rutchanee
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Morris, John
We developed a new technique for detecting the position of labels using a SCARA Robot with the end-effector vacuum pad. The technique first found the image edge with a Canny detector, then selected straight lines and their intersections. It assumed that the label was basically rectangular in shape and roughly aligned so that the key edges were close to horizontal and vertical directions. The slope of the horizontal edge defined the rotational of the label. Otherwise, reducing the image tilting and the distortion by using a perspective transform matrix and thereafter converting the image coordinate into the SCARA robot coordinate. Through the factory environment, the experiment is conducted using various captured image conditions under a lighting source. The performance of the system provides the acceptable accuracy all of the picking coordinates with positioning errors, which x-coordinate are 0.01 - 0.25 mm, y-coordinate are 0.01 - 0.32 mm and rotation angle are 0.05° - 3.94°. In trials, the SCARA robot was able to grip the label precisely in each test.
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Early fault detection based on ball bearing vibration analysis using multinomial logistic regression
(2017-03-25)
Kerdsiri, Thanadat
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Gullayanon, Rutchanee
Rotating machines play an important role in several applications such as transportation, industry and military. Maintaining continuous operations of these machines is vital to these applications. The reliability of these rotating machines depends on ball bearing health. Bearing can fail from many factors, e.g., impurity of lubricant, improper installation, etc. All of these defects can be detected by vibration analysis. Therefore, vibration signals are utilized to develop early fault detection of ball bearing to warn operators of any anomalies in the system. This paper presents a fault detection algorithm using a logistic regression method to assess the health state of the system. This regression utilizes the node energy of wavelet packet decomposition, kurtosis and crest factor as key features. The resulting signals are processed using the Savitzky-Golay smoothing algorithm before determining the time location where the defects occur with a confidence interval of 99%. The proposed method was tested with actual vibration data of bearing which were provided by the FEMTO-ST institute and showed that it is capable of accurately predicting defect time only 10 seconds after the actual defect occurred. The worst-case prediction of 970 seconds after defect occurs also observed during experiment. Although the system did not set off alarm for fault detection after 970 seconds, one can observe an increase in the fault probability trends in early state. Therefore, the proposed algorithm can be utilized for early fault detection in ball bearing applications.
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Robust tracking algorithm with designed marker for limited-power computer
(2016-03-23)
Siriteerakul, Teera
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Gullayanon, Rutchanee
Object tracking is an important part of many visual systems. The robot navigation task, for example, does not only require a robust and accurate tracking, but also need the tracking module to perform in real-time. This may not be a problem with hi-end robotic system with powerful computing unit. However, in the case of mobile robot where the computing and the battery power are limited, there are only a few tracking methods available. Thus, we propose an algorithm, coupled with a designed marker, for fast and stable tracking. The proposed algorithm utilizes robustness of Hough Circle Transform to detect candidate locations for the marker. Then, all the detected locations are filtered by known properties of the designed marker until we have the correct location. An experiment done on Raspberry PI shows that the marker can be detected robustly with sufficient speed.
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Character classification framework based on support vector machine and k-nearest neighbour schemes
(2016-02-01)
Siriteerakul, Teera
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Boonjing, Veera
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Gullayanon, Rutchanee
The problem of Thai character classification can be difficult because of the large number of characters and the similarity in the shape of many characters. While previous work combined different fonts to build their classifier, this paper proposes a framework based on support vector machine (SVM) and k-NN schemes to exploit characteristics of each font separately. In this framework, each font is used to train an SVM separately. With the trained SVMs, a vector of predicted values can be produced for any input image. Then a class label of the input image can be found by a k-NN based scheme. The proposed framework performs well with familiar fonts while providing an acceptable performance on unfamiliar fonts.
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Fast tracking algorithm for designed marker
(2016-01-01)
Siriteerakul, Teera
;
Gullayanon, Rutchanee
Robust and real-time object tracking is a vital part of many application including the navigation of mobile robot and unmanned aerial vehicle. In such environment, the computational power and battery are limited. Hence, most of the state of the art algorithms will not be able to display their full potential. Thus, we propose an improved tracking algorithm, with a designed marker, to perform robustly in real-time in complex environments. Our algorithm based on a simple binarization and finding candidate connected components. Then, with physical and geometric property of the designed marker, we can filter out all the other components until we only have the marker. This scheme has been tested on Raspberry PI, a limited-power computing unit, and demonstrate sufficient robustness and speed.
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Flipping an engineering mathematics classroom for a large undergraduate class
(2015-03-17)
Gullayanon, Rutchanee
Although flipping classroom is not a new idea in education, implementing flipped class is somewhat a new trend. Flipping classrooms involve students in using their textbooks, online materials, and other useful resources before coming to class. Consequently, students can spend valuable class meetings on problem-solving activities instead of lectures. Positive feedback from utilizing the flipped classroom techniques has been reported in several papers. This article presents an attempt to implement the flipped classroom for a large introductory engineering class in multivariable calculus at King Mongkut's Institute of Technology Ladkrabang. The results from implementing the flipped classroom both quantitatively and qualitatively showed a higher performance and more positive feedback when compared to those obtained from this class taught by the same instructor using the traditional classroom format in the previous year.
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Fluorochemical concentration and distribution analysis using portable XRF instrument in carpet industry
(2013-01-01)
Gullayanon, Rutchanee
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Michaels, Thomas E.
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Rudat, Martin A.
This article introduces the use of a portable X-ray fluorescence (XRF) instrument as a non-destructive tool to rapidly determine fluorochemical concentration on carpet fibers. Most portable XRF instruments are not capable of detecting the primary element in fluorochemicals, fluorine (F) directly. However, fluorochemicals may be combined with chemical taggants, which are measurable. When properly calibrated, the taggant concentrations are a proxy for the F level on carpet fibers, which in turn represents fluorochemical concentration. Results in this research showed that an accuracy of 150±50ppm of F can be achieved using a portable XRF instrument. This level and accuracy range is specified by manufacturers as an acceptable fluorochemical concentration on carpet fibers. Therefore, a portable XRF instrument can be used in place of the certified chemical burn test, thereby reducing the response time to obtain test results from hours for the chemical burn test to less than 5min when using the portable XRF technology. This online measurement system for fluorochemical concentration can also be used as a practical means of determining fluorochemical distribution profiles across carpet samples. © 2013 John Wiley & Sons, Ltd.

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