Fast Fault Diagnosis Method for Hall Sensors in Brushless DC Motor Drives

Zhang, Qian; Feng, Ming

doi:10.1109/tpel.2018.2844956

Cited by 44 publications

(20 citation statements)

References 19 publications

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“…Furthermore, only the papers that show experimental results and motor parameters are included in the comparison. In that respect, the authors in [19] ensure the FTC in the shortest possible time; nevertheless, the authors state that in case of excessive acceleration or deceleration, the algorithm needs to be turned off to avoid false alarms. The authors in [18] propose a non-invasive FTC approach by analyzing the motor line voltages, however, the presented design results in maximum electrical degrees to compensate for the fault.…”

Section: Comparison Of the Ftc Methodsmentioning

confidence: 99%

“…Since the method uses FFT and SED, therefore, the recommended scheme takes 113 ms to diagnose and identify the fault [18]. The authors in [19] explain fast fault diagnosis technique, where the Hall-effect sensor signals are sampled at high frequency and the sampling instants are considered as a fast counter. Therefore, whenever the counter exceeds a certain limit or falls short of the threshold values, that raises a fault flag.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Sensor Fault Detection Algorithm for Fault Tolerant Control of BLDC Motor

Aqil

Hur

2021

Electronics

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A direct redundancy-based fault tolerant control system (FTCS) is proposed for the operation of a brushless DC (BLDC) motor in the case of multiple sensor failures. The presented method expands on the previously published work that dealt with the failure of a single Hall-effect sensor. In this paper, a novel algorithm is developed along with the new experimental scheme and the FTCS can deal with the failure of up to two Hall-effect sensors. The fault tolerant control (FTC) algorithm is based on three conditions and is designed to deal with any scenario of the sensor faults. Simulation was performed and presented experimental results show effectiveness and validation of the method. The method can be implemented with ease, is fast and does not require high end processors.

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Section: Comparison Of the Ftc Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple Sensor Fault Detection Algorithm for Fault Tolerant Control of BLDC Motor

Aqil

Hur

2021

Electronics

View full text Add to dashboard Cite

show abstract

“…A Hall sensor has the obvious advantages of a low cost and simple structure [7]. Additionally, DC motors using Hall sensors have been widely used in commercial and industrial applications [8]. Therefore, this article uses the Hall signal, which is an electrical technology, to establish an identification model.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis for BLDC With Incorporating Accuracy and False Negative Rate in Feature Selection Optimization

Lee

Hung

2022

IEEE Access

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Early fault diagnosis is essential for the proper operation of rotating machines. This article proposes a fitness function in differential evolution (DE) that considers accuracy rate and false negative rate for optimization in brushless DC (BLDC) motor fault diagnosis. Feature selection based on a distance discriminant (FSDD) calculates the feature factors which base on the category separability of features after the Hilbert-Huang transform (HHT) which extracts the features of four different type signals from BLDC motor Hall sensor. The feature rank through DE to optimize before the features into the backpropagation neural network (BPNN) in order. By reducing the feature number of Hall signal and decreasing the complexity of neural network input, the combined method was proposed in this article can significantly reduce the calculation cost. Finally, the identification model obtained an accuracy rate of 98.98% and false negative rate of 13.66% when there were 18 features; besides, receiver operating characteristic (ROC) curve and probability curve have been evidenced the number of false negative is decreased. Moreover, the experiments have verified that the proposed method is effective in UCI data set.

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“…A Hall sensor has the obvious advantages of a low cost and simple structure [8]. Additionally, DC motors using Hall sensors have been widely used in commercial and industrial applications [9]. Therefore, this article uses the Hall signal, which is an electrical technology, to establish an identification system.…”

Section: Introductionmentioning

confidence: 99%

Feature Ranking and Differential Evolution for Feature Selection in Brushless DC Motor Fault Diagnosis

Lee

Hung

2021

Symmetry

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A fault diagnosis system with the ability to recognize many different faults obviously has a certain complexity. Therefore, improving the performance of similar systems has attracted much research interest. This article proposes a system of feature ranking and differential evolution for feature selection in BLDC fault diagnosis. First, this study used the Hilbert–Huang transform (HHT) to extract the features of four different types of brushless DC motor Hall signal. Second, we used feature selection based on a distance discriminant (FSDD) to calculate the feature factors which base on the category separability of features to select the features which have a positive correlation with the types. The features were entered sequentially into the two supervised classifiers: backpropagation neural network (BPNN) and linear discriminant analysis (LDA), and the identification results were then evaluated. The feature input for the classifier was derived from the FSDD, and then we optimized the feature rank using differential evolution (DE). Finally, the results were verified from the BLDC motor’s operating environment simulation with the same features by adding appropriate signal-to-noise ratio magnitudes. The identification system obtained an accuracy rate of 96% when there were 14 features. Additionally, the experimental results show that the proposed system has a robust anti-noise ability, and the accuracy rate is 92.04%, even when 20 dB of white Gaussian noise is added to the signal. Moreover, compared with the systems established from the discrete wavelet transform (DWT) and a variety of classifiers, our proposed system has a higher accuracy with fewer features.

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Fast Fault Diagnosis Method for Hall Sensors in Brushless DC Motor Drives

Cited by 44 publications

References 19 publications

Multiple Sensor Fault Detection Algorithm for Fault Tolerant Control of BLDC Motor

Multiple Sensor Fault Detection Algorithm for Fault Tolerant Control of BLDC Motor

Intelligent Fault Diagnosis for BLDC With Incorporating Accuracy and False Negative Rate in Feature Selection Optimization

Feature Ranking and Differential Evolution for Feature Selection in Brushless DC Motor Fault Diagnosis

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