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A Low-Cost Non-Intrusive Method for In-Field Motor Speed Measurement Based on a Smartphone
Induction motors are broadly used as drivers of a large variety of industrial equipment. A proper measurement of the motor rotation speed is essential to monitor the performance of most industrial drives. As an example, the measurement of rotor speed is a simple and broadly used industrial method to estimate the motor’s efficiency or mechanical load. In this work, a new low-cost non-intrusive method for in-field motor speed measurement, based on the spectral analysis of the motor audible noise, is proposed. The motor noise is acquired using a smartphone and processed by a MATLAB-based routine, which determines the rotation speed by identifying the rotor shaft mechanical frequency from the harmonic spectrum of the noise signal. This work intends to test the hypothesis that the emitted motor noise, like mechanical vibrations, contains a frequency component due to the rotation speed which, to the authors’ knowledge, has thus far been disregarded for the purpose of speed measurement. The experimental results of a variety of tests, from no load to full load, including the use of a frequency converter, found that relative errors on the speed estimation were always lower than 0.151%. These findings proved the versatility, robustness, and accuracy of the proposed method.
Journal Article
Measuring speed
When a bird flies by a window or a car zooms by on the highway, we often wonder how fast the two objects traveled. This book explains the concept of speed to readers while showing them accessible ways to measure it for themselves. Learning the amount of distance traveled, and how quickly that distance was covered, is a great way to implement a variety of STEM topics while practicing this essential mathematical skill.
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Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone
Induction motors are key pieces of equipment in today’s society, powering a variety of industrial drives and home appliances. The induction motor speed is often used to monitor the performance of all kinds of industrial drives. For example, in the industrial field, the motor speed is very often used to determine the efficiency and mechanical load of motors. In this work, a new simple, low-cost, and nonintrusive procedure is proposed for infield measurement of induction motors speed, which is based on the spectral analysis of the vibration signal of the motors. The motor vibration signal is first acquired using the accelerometers integrated into a basic phone. The acquired signal is then treated by a MATLAB-based algorithm, which can determine the motor speed by identifying the mechanical frequency of the rotor shaft from the harmonic content of the vibration signal. In this way, it is shown that the mechanical frequency corresponding to the speed of rotation of the motors can be acquired by means of the embedded accelerometers of a common smartphone, avoiding the acquisition and installation of external accelerometers. To the authors’ knowledge, this could be the first time that a smartphone has been proposed as a practical means of measuring the speed of a motor by analysing its vibration. Experimental results from an extensive set of tests, including the supply of the motor from a frequency converter, show that the speed can always be measured with a relative error of less than 0.15%.
Journal Article
Wind Speed Measurement by an Inexpensive and Lightweight Thermal Anemometer on a Small UAV
Profiling wind information when using a small unmanned aerial vehicle (sUAV) is vital for atmospheric profiling and monitoring attitude during flight. Wind speed on an sUAV can be measured directly using ultrasonic anemometers or by calculating its attitude control information. The former method requires a relatively large payload for an onboard ultrasonic anemometer, while the latter requires real-time flight log data access, which depends on the UAV manufacturers. This study proposes the feasibility of a small thermal anemometer to measure wind speeds inexpensively using a small commercial quadcopter (DJI Mavic2: M2). A laboratory experiment demonstrated that the horizontal wind speed bias increased linearly with ascending sUAV speed. A smoke experiment during hovering revealed the downward wind bias (1.2 m s−1) at a 12-cm height above the M2 body. Field experiments in the ice-covered ocean demonstrated that the corrected wind speed agreed closely with the shipboard wind data observed by a calibrated ultrasonic anemometer. A dual-mount system comprising thermal anemometers was proposed to measure wind speed and direction.
Journal Article
“Hearing” Wind Speed: Ground Wind Measurement Using Deep Learning From Surveillance Audio
This study presents a novel method for measuring ground wind speed (WS) using audio data collected from surveillance cameras. The continuous wavelet transform is employed to model wind sounds and capture the dynamic variations over time. A deep‐learning model integrating attention‐enhanced Convolutional Neural Network and Bidirectional Gated Recurrent Unit architectures is developed to extract WS features from the time‐frequency domain of the surveillance audio. For model training, a surveillance audio‐based WS data set is constructed. Extensive experiments demonstrate that the proposed model achieves a WS level prediction accuracy of 84.56% for a self‐constructed data set and 82.25% in real‐world tests. Additionally, the model yielded root mean square error values of 1.84 m/s and 1.49 m/s for two typhoon events. Although challenges remain in improving low‐speed wind measurement accuracy, this approach highlights the potential of a high‐resolution, low‐cost, urban wind observation network using surveillance cameras, significantly enhancing the granularity of urban ground wind observations.
Journal Article
Research on Centrifugal Pump Speed Measurement Based on Vibration Measurement
Traditional rotational speed measurement methods, such as invasive sensors and visual recognition technologies, are often constrained by physical wear and environmental limitations. This paper introduces a non-invasive rotational speed measurement approach based on vibration signal frequency spectrum analysis. The proposed method utilizes the Zoom-FFT algorithm to process vibration signals collected during pump operation, enabling the precise identification of the dominant frequency and its correlation with the pump shaft frequency for accurate speed calculation. The experimental results obtained from a centrifugal pump under varying operating conditions demonstrate the following: At a constant rotational speed, flow variations have a minimal impact on the measurement accuracy, with errors ≤0.04%. Under constant flow conditions, the speed calculation accuracy achieves an error rate of 0.27% across different speeds. Compared to traditional methods, the proposed approach exhibits superior reliability and accuracy. This non-invasive method minimizes physical wear and reduces dependency on environmental factors, offering an effective solution for mechanical equipment monitoring and fault diagnosis.
Journal Article
A Design of FPGA-Based Neural Network PID Controller for Motion Control System
In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. In the design of the controller, it is divided into several sub-modules according to the modular design idea. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The main state machine module generates enable signals that control the sequential execution of each sub-module. The error backpropagation and weight update module completes the update of the weights of each layer of the network. The peripheral modules of the control system are divided into two main parts. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority.
Journal Article