Hall Sensor Signals Research Articles

Design and Development of Sensing Unit for Wire Rope Tester

In this work, a sensing unit on a Printed Circuit Board (PCB) has been designed to house hall sensors. Hall sensors are used to capture leakage flux signals from magnetized wire ropes for Non-Destructive Testing (NDT) of wire ropes. Various parameters are optimized before designing PCB, such as: Lift-off, orientation of sensor, number of hall sensors. Hall sensor signals are captured using DAQ card of NI® and program is written in LABVIEW® software. System is designed for: reducing noise in acquired signal (using RC filter), decreasing the number of channels for data acquisition system, reducing system cost and maintain signal fidelity.

Enhancing the solar water pumping efficiency through Beta MPPT method-controlled drive

This paper presents an innovative approach to achieve efficient solar water pumping through the integration of a Photovoltaic (PV) array and a Brushless Direct Current (BLDC) motor water pumping system. The system incorporates a Voltage Source Converter (VSC) with six switches, utilized to facilitate commutation. The inherent solar radiation is harnessed by the PV array, capitalizing on its renewable nature to generate electricity. By dynamically adjusting the switching states of the six VSC switches, the speed of the BLDC motor is modulated in response to the varying levels of available solar radiation. The BLDC motor's hall sensor signals play a crucial for determining the rotor's position and they are employed to generate precise commutation signals. The control strategy integrates the Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithm, which initially governs the commutation signals. To enhance adaptability to rapidly changing solar irradiation conditions, the control strategy dynamically updates the commutation signals using the innovative Beta MPPT algorithm. To assess the efficiency of the proposed control strategy, a comprehensive comparison between the INC and Beta MPPT algorithms is conducted using MATLAB Simulink. The performance of the BLDC motor under these algorithms was evaluated in terms of its ability to optimize energy extraction. The graphical analysis of these algorithms, considering the temporal aspect, substantiates the identification of the superior MPPT algorithm for BLDC motor control in solar water pumping applications. This study contributes to the advancement of solar water pumping systems by introducing a novel control approach that combines PV array utilization, VSC-based commutation, and a dual-step MPPT algorithm. The results demonstrate the effectiveness of the Beta MPPT algorithm by enabling the system to respond promptly to fluctuating solar irradiation conditions, thereby enhancing the overall efficiency of the solar water pumping process.

Investigation and mitigation of unbalanced hall sensor signal faults in sensored brushless DC motor drives

Investigation and mitigation of unbalanced hall sensor signal faults in sensored brushless DC motor drives

Performance Analysis Of DSMST Converter FED BLDC Motor

A positive output voltage SEPIC-type DC-DC converter called DSMST (modified SEPIC topology) without a transformer for use in electric vehicles. The DSMST converter is derived from the traditional SEPIC converter with the addition of a switching cell. Furthermore, the output diode and inductors are interchanged for the continuous output current. In the realm of electric vehicles, the utilization of Brushless DC (BLDC) motor drives coupled with a DC-DC converter has become increasingly important. Specifically in the context of electric vehicles, the use of BLDC motors offers several technical advantages compared to other types of motors such as DC, induction, and switched reluctance motors. The DSMST converter is connected to the BLDC motor through a three-phase voltage source inverter (VSI) to regulate the voltage and current supplied to the BLDC motor, ensuring optimal performance. To regulate the speed of drive, a closed loop control system is developed using hall sensor signals. The proposed system is developed in the MATLAB/Simulink to check the applicability of electric vehicle configuration and the results shown that the proposed converter is well suitable for electric vehicle applications.

Torque estimation in three-phase induction motors under broken bars and voltage variations using airgap flux measurement and a machine learning model

This paper proposes a new approach for load torque estimation in squirrel cage induction motors using airgap flux measurement, by means of a Hall effect sensor installed between two stator slots of the machine. The rotor speed was estimated from the Hall sensor signal using a previous method designed by the authors, and the root mean square and mean value were also computed from the Hall signal to serve as inputs to a multi-layer perceptron model.The positioning of the Hall sensor inside the machine was also investigated during different experiments for torque prediction and considered as an additional input for the regression task. The present methodology does not require motor parameters for torque prediction. This research has been validated using some simulated and experimental results for different motor operational conditions. The performance metrics disclosed a good torque prediction for the machine fed not only by a rated voltage, but also by overvoltage/undervoltage power supply and with broken rotor bars, even for low loads. Both experimental and simulation results showed a mean absolute percentage error lower than 4% for a 7.5 kW induction motor.

Extended Operation of Brushless DC Motors Beyond 120° Under Maximum Torque Per Ampere Control

Hall-sensor-controlled brushless dc (BLDC) motors are widely used in many electromechanical applications due to their simplicity and good torque-speed characteristics. The conventional commutation methods include the 120° and 180° switching logics that can be derived directly from the Hall sensor signals. The 120° switching method is the most common due to its natural approximation of the maximum torque per Ampere (MTPA) operation, while the 180° switching method offers a higher available phase voltage for the same dc voltage. Both methods are typically used with additional pulse-width modulation to control the motors from a fixed dc source. Recently, attention has been given to the operation of BLDC motors with conduction angles between 120° and 180° (e.g., 150°, 160°, etc.), where some benefits may be gained. This paper proposes a new methodology that continuously extends the operation from 120° to 180°, while maintaining the MTPA property. Simulations and experimental results based on a typical industrial BLDC motor demonstrate the proposed control methodology and its benefits over the conventional alternative methods.

Fault-Diagnosis and Fault-Recovery System of Hall Sensors in Brushless DC Motor Based on Neural Networks.

This paper proposes a neural-network-based framework using Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) for detecting faults and recovering signals from Hall sensors in brushless DC motors. Hall sensors are critical components in determining the position and speed of motors, and faults in these sensors can disrupt their normal operation. Traditional fault-diagnosis methods, such as state-sensitive and transition-sensitive approaches, and fault-recovery methods, such as vector tracking observer, have been widely used in the industry but can be inflexible when applied to different models. The proposed fault diagnosis using the CNN-LSTM model was trained on the signal sequences of Hall sensors and can effectively distinguish between normal and faulty signals, achieving an accuracy of the fault-diagnosis system of around 99.3% for identifying the type of fault. Additionally, the proposed fault recovery using the CNN-LSTM model was trained on the signal sequences of Hall sensors and the output of the fault-detection system, achieving an efficiency of determining the position of the phase in the sequence of the Hall sensor signal at around 97%. This work has three main contributions: (1) a CNN-LSTM neural network structure is proposed to be implemented in both the fault-diagnosis and fault-recovery systems for efficient learning and feature extraction from the Hall sensor data. (2) The proposed fault-diagnosis system is equipped with a sensitive and accurate fault-diagnosis system that can achieve an accuracy exceeding 98%. (3) The proposed fault-recovery system is capable of recovering the position in the sequence states of the Hall sensors, achieving an accuracy of 95% or higher.

Voltage Difference Envelope to the Control Simulating of Connection Frequency of Seamless Shoring

Through the intelligent control of single chip microcomputer and Hall sensor detection technology and signal transmission technology, the frequency, voltage and voltage phase of the shore power and the ship power are detected to form the lowest point of voltage difference envelope, and the seamless connection of the shore power and the ship power is controlled. The load transfer control between the shore power and the ship power realizes the ideal synchronous control of grid-connected closing and load transfer by establishing the ratio control of frequency difference between the shore power and the ship power and power change during load transfer.

Hall sensor fault diagnosis and compensation in Brushless DC motor drive using artificial intelligence technique

SummaryElectronic commutator in Brushless DC motor (BLDC) drives decides the sequence of driving current through the stator windings based on the rotor position sensed by the three Hall position sensors. If any fault occurs in the Hall sensors, it will result in a faulty driving sequence of rotors and the motor will stall. This paper investigates an artificial intelligence‐based Fault Diagnosis and Compensation (FDC) system for correcting up to two position sensor failures in trapezoidal back EMF BLDC motor drives. The proposed fault‐tolerant system is a simple, fast, and efficient method to diagnose and reconstruct the commutation signals using the existing processor‐based commutator arrangements. An Artificial Intelligence algorithm is developed to estimate the correct switching sequence in the absence of up to two Hall sensor signals while the motor is running. This supervised learning network is advantageous as it is added as an additional control algorithm with the existing control algorithm for BLDC motor. The simulation and hardware results showcase the effective performance of the proposed algorithm for different Hall sensor fault conditions.

Wireless Flexible Magnetic Tactile Sensor with Super-Resolution in Large-Areas.

Tactile recognition is among the basic survival skills of human beings, and advances in tactile sensor technology have been adopted in various fields, bringing benefits such as outstanding performance in manipulating objects and general human-robot interactions. However, promoting enhanced perception of the existing tactile sensors is limited by their sensor array arrangement and wire-connected design. Here we present a wireless flexible magnetic tactile sensor (FMTS) consisting of a multidirection magnetized flexible film (perception module) and a contactless Hall sensor (signal receiving module). The flexible magnetic film is composed of NdFeB microparticles and soft silicone elastomer microparticles, and it transfers the unambiguous transduction of external force position and magnitude into magnetic signals. Benefiting from the specific magnetization arrangement and clustering algorithm, only one Hall sensor is needed in FMTS to perceive the magnitude and position of the contact spot simultaneously with super-resolution (2.1 mm average error) on a large area (3600 mm2), and the effective working distance is also greatly extended (∼30 mm), allowing for the full softness and adaptability to diverse conditions. We anticipate that this design will promote the development of soft tactile sensors and their integration into human-robot interaction and humanoid robot perception.

A Commutation Optimization Strategy for High-Speed Brushless DC Drives With Voltage Source Inverter

The control performance of Hall sensor-based high-speed brushless dc (BLdc) motor drives depends heavily on the accuracy of Hall sensor signals. Due to the existence of Hall signal errors, the system may operate under unbalanced conditions that will lead to asymmetric currents and high-torque ripple. For high-speed BLdc drive, using voltage source inverter (VSI) with the pulsewidth modulation (PWM) technique will also lead to PWM update delay that causes inaccurate commutation. In this article, two Hall signal errors, i.e., misaligned and uneven Hall signal errors, are investigated and a Hall signal balancing strategy is developed to detect Hall signal errors and regenerate balanced Hall signals. A novel PWM generation scheme is then proposed to eliminate the PWM update delay when using traditional VSI. Both simulation and experiment results have verified the effectiveness of the proposed strategy.

Application in Virtual System Modelling (VSM) of Sensored Pm BLDC Motor Drive Using Two Technologies of Processors PIC16F877Aand Arduino Uno R3

This paper presents the simulation of a 3-phase Permanent Magnet Brushless DC (PM BLDC) motor drive. For the studied drive system in this paper, pulse width modulation (PWM) control has been implemented for a 60-degree six-step trapezoidal PM BLDC motor drive. The used processor is Arduino and PIC16F877A, which is a common, flash-able, and low-cost microcontroller unit (MCU) with functions to perform commutation sequence, rotating direction control, speed control and reading Hall sensor signals, and calculating RPM and duty cycle of the PWM outputs signals depending on variable speed. The controlling technique uses sensored type in order to make this design suitable for low-speed and high-speed applications plus control simplicity. In this paper, The application of Proteus Virtual System Modelling (VSM) software as a real-time simulation tool is introduced to model the performance of a 3-phase Permanent Magnet Brushless DC motor drive before hardware implementation. Expected results can be monitored and analyzed throughout the virtual simulation of all components. The usage of Proteus VSM enables shorter product development time, thus reducing development costs for industrial applications.

Development of a cost‐effective circuit hardware architecture for brushless direct current motor driver

SummaryA dual‐duty digital pulse‐width modulation (DDPWM) technique‐based cost‐effective control hardware architecture for brushless DC (BLDC) motor drive is reported in this paper. DDPWM control technique involves reduced computational complexity, which is beneficial in on‐chip area and power dissipation reduction. Simple Hall sensor‐based speed calculation and commutation circuits were also incorporated in the hardware to reduce the chip area further. The edge detection‐based speed calculation circuit was designed to be tolerant of any external noise or glitch in the Hall sensor signal. The proposed hardware architecture was implemented on the field‐programmable gate array (FPGA) and application‐specific integrated circuit (ASIC) platform using TSMC 180‐nm technology library. The ability of the integrated circuit (IC) for resource utilization reduction was validated by comparing the FPGA‐implemented architecture with the existing literature. The FPGA‐implemented architecture was also examined in real‐time using an experimental prototype BLDC motor setup. The drive response with dynamic load and speed variations, speed control precision, and glitch tolerant speed calculation is reported in the paper. The ASIC implementation demonstrates that the developed architecture sampled at 50 MHz is highly effective in the gate count and power dissipation reduction compared to the standard PI controller‐based width modulated pulse generation hardware architecture.

FFNN BASED MPPT CONTROLLER FOR PEMFC POWERED NON-ISOLATED HIGH GAIN DC-DC CONVERTER WITH SPWM INVERTER FED BLDC MOTOR

A Non-Isolated High Gain DC-DC converter fed from 1.2kW PEMFC is designed to boost the fuel cell voltage. The Feed-forward Neural Network based MPPT controller algorithm is implemented to adjust the duty cycle of DC-DC converter in order make sure optimum power extraction from PEMFC whose result is compared with Fuzzy Logic Controller. The boosted DC voltage is inverteted to drive BLDC motor by three phase inverter. The inverter switches are triggered with switching pattern generated by SPWM technique according to the Hall sensor signals placed at stator of BLDC motor to find the position of rotor. The simulation anlysis has been carried out using MATLAB (R2017a)/Simulink platform and discussed the results.

Circuit Design for Noise Removal of Sine Wave Hall Sensor Signal

Circuit Design for Noise Removal of Sine Wave Hall Sensor Signal

Optimised approach of feature selection based on genetic and binary state transition algorithm in the classification of bearing fault in BLDC motor

This study represents an effective approach for detection and classification of bearing faults in brushless DC (BLDC) motors based on hall-sensor signal analysis. The envelope analysis and Hilbert–Huang transform are used to extract features from the time and frequency domains of each signal. A new feature selection technique is proposed based on the combination of the genetic algorithm strength and the advantage of the binary state transition algorithm. The genetic algorithm explores search space through cross-over operator while the binary state transition algorithm is based on four special transformation operators in the local exploitation capabilities. The artificial neural network and support vector machine are used as the classifier. Each model is separately analysed and compared, leading to a high possibility to distinguish the bearing faults.

Online Detection and Classification of Rotor and Load Defects in PMSMs Based on Hall Sensor Measurements

Online monitoring and diagnostics of permanent magnet synchronous motors (PMSMs) is becoming important with the increasing demand in PMSM applications. Most of the research effort focuses on motor current signature analysis (MCSA) as it can provide remote, online monitoring at low cost. However, all types of defects that produce asymmetries in the PMSM rotor or load produce identical rotor rotational frequency components. This is a serious limitation when applying MCSA since it can produce false indications and degrade the sensitivity of fault detection. The requirement of complex time-frequency analysis techniques is another limitation of MCSA for applications with speed variations. In this paper, the feasibility of using analog Hall sensor signals to complement MCSA for detection and classification of rotor- and load-related defects is investigated. It is shown that Hall sensors installed in machines for initial rotor position estimation can be used with minimal hardware modifications to detect and classify signatures produced by the rotor and load for cases where MCSA fails, even during transient conditions. Experimental testing performed on an interior permanent magnet synchronous motor (IPMSM) under eccentricity, local demagnetization, and load unbalance conditions shows that the reliability and sensitivity of fault detection in PMSM systems can be improved at low added cost.

Research on Hall Sensor Fault Diagnosis and Compensation Method to Improve Motor Control Reliability

In this paper, we study the fault detection algorithm and the fault compensation method in case of hall sensor fault of BLDC motor. In the case of a BLDC motor operating at high speed, the fault of the hall sensor is a very important factor that reduces the reliability of the control. If hall sensor fault occurs, ripple occurs in torque and current output value of BLDC motor. In addition, a phenomenon such as rapid braking of the motor may occur. Therefore, this paper assumes the fault of one or two hall sensors, and studies hall sensor fault detection algorithm and hall sensor fault compensation method. The peak-to-peak interval of the hall sensor signal is stored and the peak-to-peak interval is checked in real time to detect the fault immediately after the hall sensor fault. Unlike existing algorithms, it has higher reliability than existing fault detection algorithms, because it detects faults in real time. When a fault is detected, a signal of a normally operating hall sensor is input to the hall sensor fault compensation circuit. The output of the compensation circuit is a signal whose phase is delayed. The phase difference of each hall sensor signals is 120°. Therefore, the gain of the compensation circuit is appropriately selected to generate a signal for compensating for the fault of the hall sensor. Model and simulate motors, control circuits, and faulty hall sensors using MATLAB Simulink.

Wide-range Speed Control Scheme of BLDC Motor Based on the Hall Sensor Signal

Wide-range Speed Control Scheme of BLDC Motor Based on the Hall Sensor Signal

Optimal Design Strategy for Improved Operation of IPM BLDC Motors With Low-Resolution Hall Sensors

This study proposes an interior permanent magnet (IPM) brushless dc (BLDC) motor design strategy that utilizes BLDC control based on Hall sensor signals. The magnetic flux of IPM motors varies according to the rotor position and abnormal Hall sensor problems are related to magnetic flux. To find the cause of the abnormality in the Hall sensors, an analysis of the magnetic flux density at the Hall sensor position by finite element analysis is conducted. In addition, an IPM model with a notch structure is proposed to solve abnormal Hall sensor problems and its magnetic equivalent circuit (MEC) model is derived. Based on the MEC model, an optimal rotor design method is proposed and the final model is derived. However, the Hall sensor signal achieved from the optimal rotor is not perfect. To improve the accuracy of the BLDC motor control, a rotor position estimation method is proposed. Finally, experiments are performed to evaluate the performance of the proposed IPM-type BLDC motor and the Hall sensor compensation method.