1,721,153 research outputs found
A New Technique on Vibration Optimization of Industrial Inclinometer for MEMS Accelerometer Without Sensor Fusion
Full text linkAccelerometer of the Microelectromechanical systems (MEMS) based inertial measurement units (IMUs) is key to inclination measurement in the industry 4.0. However, external vibration negatively impacts the precision of orientation angles during operation. Many inclinometer companies have demanded to develop a solution for vibration impact on accelerometer without other sensors' support because of economic problems. This article presents a new algorithm Orientation Axes Crossover Processing (OACP) on vibration optimization for MEMS accelerometer without sensor fusion. The proposed filter works on a principle based on the characteristics of vibration impact on whether the X-axis or Y-axis to optimally minimize the noise. A high accurate setup is built-up based on the Pan-Tilt Unit and a TUMAC vibrator for the verification of new filters, implemented into LSM9DS1 (3D accelerometer, 3D gyroscope). The new filter is able to work independently, and also fuse with the Low-pass filter or Kalman filter to enhance the dynamic response, only 0.163 seconds as maximum delay during vibration. The experimental results show that the proposed algorithm always accomplishes smaller variations than Low-pass filter, about 0.2 degrees in standard deviation. The compromise between vibration immunity and dynamic response is analyzed in detail to demonstrate the optimal performances of the concerned filters. The project was carried out at the 'Sensor System' in Italy which is an industrial company in the inclinometer field
SISTEMI DI MISURA VXI. Guida all’Uso, alla Progettazione ed alla Valutazione delle Prestazioni
No full textSISTEMI DI MISURA VXI. Guida all’Uso, alla Progettazione ed alla Valutazione delle Prestazioni
No full text
Yaw/Heading optimization by drift elimination on MEMS gyroscope
No full textThe main goal of the paper is to achieve a highly accurate measurement of yaw/heading without the support of the Global Positioning System (GPS) and magnetometer by using a practical model based on the principle "No Motion No Integration" (NMNI). The proposed technique removes the drift significantly to optimize the Micro-Electro-Mechanical System (MEMS) gyroscope for the yaw/heading estimation. A "Renovating Model" is added to the NMNI algorithm as a real-time detector for sensor motion state. The 'NMNI' can work effectively with an independent gyroscope or collaborate with other MEMS sensors via fusion algorithms such as Madgwick, Mahony, and Kalman to overcome the limitations of the Global Positioning System (GPS) in the indoor environment. Moreover, the two other critical factors: slope and rotation speed, were examined on sensor behavior to thoroughly verify each filter's pros and cons. The experiments were carried out using a low-cost platform equipped with MEMS as gyroscope, accelerometer, and magnetometer. A Pan Tilt Unit-C46 (PTU-C46) with high accurate positioning was used as a reference angle for both static and dynamic experiments. The results show the considerable advancement of yaw estimation by implementing the NMNI model into the gyroscope thanks to the effective drift removal. Moreover, the fusions between NMNI filter with Mahony and Madgwick accomplish high yaw measurement performance when the sensor on the high slope without magnetometer.(c) 2021 Elsevier B.V. All rights reserved
Yaw/Heading optimization by Machine learning model based on MEMS magnetometer under harsh conditions
No full textThe paper's main goal is to accomplish a high accuracy of yaw/heading by Machine Learning approach when the motion range of vehicle/device calibration is limited. The nonlinear Random Forest (RF) Regression with proper training has a high potential to deal with the magnetometer uncertainty before calibration and during iron distortion cases. The proposed solution solely requires the magnetometer without other sensor's support. A Pan Tilt Unit-C46 (PTU-C46) with high precise positioning was used as a reference heading value to label the corresponding magnetic features in the learning model. The proposed approach helps yaw estimation to be carried out under harsh conditions, which resolve many difficulties in orientation tracking since the magnetometer is susceptible to hard iron and soft iron in the environment. In addition, many mechanical devices work only within the specific range and waste their dynamic motion around two axes or more just for calibration. Thus, the research focuses on the level rotation calibration around Z-axis within the restricted range of motion for practical application. The experiment was carried out using a low-cost platform equipped with Micro-Electro-Mechanical System (MEMS) sensors as gyroscope, accelerometer, and magnetometer. The 9 Degree of Freedom (DoF) Madgwick was implemented into the Microcontroller to compare with the proposed model. The sensor fusion can track the yaw value after the level calibration despite various error conduction. The RF model accomplishes a superior result with more stability and more minor error. Under iron disturbance or calibration absence, the ML model still maintains the good tracking command with maximum Mean Square Error of about 0.3°, while the Madgwick is unsuccessful in heading measurement due to huge error in these circumstances
New Artificial Intelligence Approach to Inclination Measurement Based on MEMS Accelerometer
No full textThe article presents a research of angular orientation based on a microelectromechanical system (MEMS) accelerometer by using machine learning (ML) and deep learning (DL) model with architectures of deep neural networks (DNNs). In the industrial environment, artificial intelligence (AI) plays a crucial role in automation which is a potential solution for better performance of inclinometer. This article was carried out to apply this intelligent model on the inertial measurement unit to accomplish the angular position. The experiment shows that the ML model correctly learns the relationship between acceleration and tracking angles via polynomial regression with an R-square of 0.98. The employed DL model with four hidden layers of ten neurons achieves an accuracy of 99.99 % and almost a nonerror performance. The acceleration acquisitions were obtained from MEMS accelerometer LSM9DS1 at a frequency of 50 Hz via microcontroller STM32F401RE. The ML and DNN models were designed based on the platform Tensorflow with high processing accuracy. The Pan-Tilt Unit was used as the angle reference for static and dynamic tests. The traditional technique is used for comparison as well as verification of the proposed models. The DL model has better precision over the ML model due to its high structure level with updating weight and error optimization from the neural network structure. Meanwhile, ML shows more stable results in dynamic circumstances
Sensitivity of water meters to small leakage
Full text linkWater leakage beyond the meter at the household level is becoming an emerging problem in a world where water must be respected and saved. More than public awareness campaigns for citizens, automatic leakage detection could give in the future the best results. Domestic water consumption will be continuously monitored by smart meters able to distinguish between normal absorption and leakage. Nowadays, some research prototypes of smart water meters were designed for continuous monitoring aimed to collect measurements and send them to a central unit for developing statistics on consumptions and alarms. In this paper, the authors propose a battery-powered visual smart device that could be a good starting point to generate leakage alarms at the household level. After a brief description of state of the art, the paper at first faces the problem of the leakage detection dependence on meter sensitivity. Then, an image-based technique for automatic “null consumption detection” to be applied both to the register last digit and to a needle of water meters is tested on three different water meters. Finally, experimental results confirm that this image-based technique, allowing the automatic detection of Periods With Null Consumption, can be very useful for water leakage detection algorithms
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