Vibration Measurement Using a Low-Cost MEMS Accelerometer Kit for the Education

Author(s):  
Syed Faizan Shah ◽  
Skylab Paulas Bhore
2019 ◽  
Vol 9 (02) ◽  
pp. 63-67
Author(s):  
Indra Feriadi ◽  
Fajar Aswin ◽  
M Iqbal Nugraha

Vibration measurement technology using conventional sensors such as piezoelectric (PZT) Accelerometer is still expensive. Currently, many low-cost vibration measuring devices have been developed by using Micro Electro Mechanical System (MEMS) technology. This study aims to analyze the results of vibration measurement system MEMS Accelerometer ADXL345 with PZT Accelerometer. This research applies design and develop approach with comparative data analysis technique, that is comparing data of result of measurement of MEMS Accelerometer ADXL345 to PZT Accelerometer Vibroport80. The construction comprises the ADXL345 sensor connected to the Arduino Mega 2560 microcontroller operated by Widows operating system and programming language Arduino IDE 1.08. Testing of measurements at Bearing speeds of 500, 1000, and 1500 RPM with length of time measurements at 5, 10, and 20 seconds respectively. The analysis of the test results shows that the MEMS Accelerometer ADXL345 of vibration measurement system can measure, process and display vibration measurement data larger 3% than PZT Accelerometer and can provide the best measurement accuracy at 20 seconds measurement length of time.


2020 ◽  
Vol 23 (7) ◽  
pp. 25-33
Author(s):  
Luciane Agnoletti dos Santos Pedotti ◽  
Ricardo Mazza Zago ◽  
Mateus Giesbrecht ◽  
Fabiano Fruett

2015 ◽  
Vol 105 (3) ◽  
pp. 1314-1323 ◽  
Author(s):  
Angela I. Chung ◽  
Elizabeth S. Cochran ◽  
Anna E. Kaiser ◽  
Carl M. Christensen ◽  
Battalgazi Yildirim ◽  
...  

The aim of this paper is to develop a fault diagnosis algorithm by vibrational analysis for an industrial gear hobbing machine. Gear Hobbing is the most dominant and profitable process for manufacturing high quality gears. In order to sustain the market competition gear manufacturers, need to produce high quality gears with minimum possible cost. However, catastrophic failures do occur in gear hobbing process which causes unexpected machine down time and revenue loss. These failures can be avoided by using condition monitoring approaches. In the proposed approach vibration data during different faults such as lubrication error, excessive feed rate, loose bearing error is collected from an industrial gear hobbing machine using three axis MEMS accelerometer. The collected data is analyzed and classified with spectral kurtosis and Dynamic Time Warping algorithm. The efficiency of the proposed approach is 90 percent as determined by experimental results. The proposed approach can provide a low-cost solution for predictive maintenance for gear hobbing industries..


2018 ◽  
Vol 1148 ◽  
pp. 103-108 ◽  
Author(s):  
N.V.S. Shankar ◽  
A. Gopi Chand ◽  
K. Hanumantha Rao ◽  
K. Prem Sai

During machining any material, vibrations play a major role in deciding the life of the cutting tool as well as machine tool. The magnitude acceleration of vibrations is directly proportional to the cutting forces. In other words, if we are able to measure the acceleration experienced by the tool during machining, we can get a sense of force. There are many commercially available, pre-calibrated accelerometer sensors available off the shelf. In the current work, an attempt has been made to measure vibrations using ADXL335 accelerometer. This accelerometer is interfaced to computer using Arduino. The measured values are then used to optimize the machining process. Experiments are performed on Brass. During machining, it is better to have lower acceleration values. Thus, the first objective of the work is to minimize the vibrations. Surface roughness is another major factor which criterion “lower is the better” applies. In order to optimize the values, a series of experiments are conducted with three factors, namely, tool type (2 levels), Depth of cut (3 levels) and Feed are considered (3 levels). Mixed level optimization is performed using Taguchi analysis with L18 orthogonal array. Detailed discussion of the parameters shall be given in the article.


2019 ◽  
Vol 894 ◽  
pp. 1-8
Author(s):  
Khanh Duong Quang ◽  
Huong Vuong Thi ◽  
Anh Luu Van

Multi-axial mechanical systems commonly encounter the problem of vibration while attempting to drive machining systems at high speed. Many effective methods based on feed-forward and feedback control have been proposed and applied for vibration reduction. In order to design controllers all methods require the exact knowledge of system parameters: vibration frequency and damping ratio. In recent years, low-cost Micro Electro Mechanical Systems (MEMS) accelerometers have been used for many applications in industry. This paper presents the advantage of low cost MEMS accelerometer to identify vibration parameters of mechanical systems in comparison to conventional expensive devices.


2012 ◽  
Vol 201-202 ◽  
pp. 608-612
Author(s):  
Tie Liu Wang ◽  
Yang Dong ◽  
Jing Shen ◽  
Kui Leng ◽  
Hao Meng

Abstract: This article introduces a new digital inclinometer . It is designed by the combination of MEMS sensors and LPC1114 which is popular with low-power, low-cost 32-bit cortex-M0 embedded microprocessor on the market. Kalman fitter the Digital signal output of MEMS accelerometer and gyroscope . Then calculate the inclination angle in degrees. Finally, transfers data to a remote server. The instrument is low cost, fast signal processing speed, and solar green energy. It has a small size, light weight, high accuracy and high precision. Then, it can be widely used in the tower tilting real-time monitoring of electricity, buildings, bridges and gravity reference system.


2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gabriele Rescio ◽  
Alessandro Leone ◽  
Pietro Siciliano

Falling is one of the main causes of trauma, disability, and death among older people. Inertial sensors-based devices are able to detect falls in controlled environments. Often this kind of solution presents poor performances in real conditions. The aim of this work is the development of a computationally low-cost algorithm for feature extraction and the implementation of a machine-learning scheme for people fall detection, by using a triaxial MEMS wearable wireless accelerometer. The proposed approach allows to generalize the detection of fall events in several practical conditions. It appears invariant to the age, weight, height of people, and to the relative positioning area (even in the upper part of the waist), overcoming the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end user. In order to limit the workload, the specific study on posture analysis has been avoided, and a polynomial kernel function is used while maintaining high performances in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an one-class support vector machine classifier in a stand-alone PC.


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