A low-cost fiber based displacement sensor for industrial applications

Saimon, Siti Mahfuza; Ngajikin, Nor Hafizah; Omar, Muhammad Shafiq; Ibrahim, Mohd Haniff; Noor, Muhammad Yusof Mohd; Abdullah, Ahmad Sharmi; Salim, Mohd Rashidi

doi:10.12928/telkomnika.v17i2.9754

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…The arrangement of the bundle POF is in the form of a circle as shown in figure 1(a). The transmitting fiber is located in the middle, surrounded by receiving fiber [35,36]. This arrangement allows more reflected light to be collected in the sensor head.…”

Section: Sensor Design and Operating Principlementioning

confidence: 99%

Transformer oil temperature sensing utilizing bundle plastic optical fiber sensor

Kadar,

Ngajikin,

Yaacob

et al. 2024

Eng. Res. Express

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A bundle plastic optical fiber (POF) that works based on an intensity modulation technique is experimentally demonstrated to sense the temperature of transformer oil. The sensor was developed using a bundle POF that is located perpendicular to an aluminum reflective film with an airgap cavity between these two elements. The simplicity of the architecture allows the development of an economical optical sensor system. To avoid interference effects by other substances in the oil, the sensor head is encapsulated with a metal protecting tube. The temperature measurement was realized in this study by monitoring the output light intensity in the visible light spectrum. For linearity range from 40oC to 75oC, the tested sensor exhibits a sensitivity of 0.0064oC-1, a linearity coefficient of 0.95 and a resolution of 1.56oC. These results demonstrate the suitability of the developed sensor for temperature oil monitoring in an electrical power transformer system.

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Section: Sensor Design and Operating Principlementioning

confidence: 99%

Transformer oil temperature sensing utilizing bundle plastic optical fiber sensor

Kadar,

Ngajikin,

Yaacob

et al. 2024

Eng. Res. Express

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Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review

Ghazali

Rahiman

2021

Shock and Vibration

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Untimely machinery breakdown will incur significant losses, especially to the manufacturing company as it affects the production rates. During operation, machines generate vibrations and there are unwanted vibrations that will disrupt the machine system, which results in faults such as imbalance, wear, and misalignment. Thus, vibration analysis has become an effective method to monitor the health and performance of the machine. The vibration signatures of the machines contain important information regarding the machine condition such as the source of failure and its severity. Operators are also provided with an early warning for scheduled maintenance. Numerous approaches for analyzing the vibration data of machinery have been proposed over the years, and each approach has its characteristics, advantages, and disadvantages. This manuscript presents a systematic review of up-to-date vibration analysis for machine monitoring and diagnosis. It involves data acquisition (instrument applied such as analyzer and sensors), feature extraction, and fault recognition techniques using artificial intelligence (AI). Several research questions (RQs) are aimed to be answered in this manuscript. A combination of time domain statistical features and deep learning approaches is expected to be widely applied in the future, where fault features can be automatically extracted from the raw vibration signals. The presence of various sensors and communication devices in the emerging smart machines will present a new and huge challenge in vibration monitoring and diagnosing.

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