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Generally, Brushless DC (BLDC) machines attract many industrialists due to their unique characteristics like better output, stabilized performance, and high torque to current ratio. BLDC drive has a long life, and they do not need maintenance; however, the drive has low starting torque and high cost. Thus, Non-stop monitoring and future prediction methods can reduce fault occurrence and improve system performance. In this paper, we have proposed the prognosis of BLDC drive faults using the Autoregressive Integrated Moving Average (ARIMA) Algorithm. Here, we consider the open circuit (OC) and short circuit (SC) faults in BLDC drive to prognosis by ARIMA technique. The ARIMA has a fixed structure, and it is particularly built for time series data. By data acquisition system, the drive parameters such as current, torque, and speed will be continuously obtained. Filtering out the high-frequency noise present in the data is the main principle of the ARIMA model. Matlab/Simulink platform is used to implement the process and analyze the results using prediction efficiency, the fault analysis in speed, flux, torque, current, and voltage.

The brushless direct current (BLDC) motor drive is gaining popularity due to its excellent controllability and high efficiency. This paper introduces a fault diagnosis method for open circuit (OC) and short circuit (SC) BLDC motor drives using a hybrid classifier with hybrid optimization. Features such as current, voltage, speed, and torque are considered as the training data. The features are extracted by discrete wavelet transform (DWT) and then employed to train the classifiers to distinguish between fault types and values of response parameters using the support vector machine and Naive Bayes classifier (SVM-NB). To further improve the performance of the system, hybrid chaotic particle swarm optimization (CPSO) algorithms and teaching-learning-based optimization (TLBO) are used. This method is capable of detecting and recognizing the type of faults in the BLDC motor. The developed approach is implemented on the MATLAB/SIMULINK for OC, SC, and no-fault conditions. These hybrid algorithms provide better performance compared to existing approaches with respect to sensitivity, accuracy, and specificity. This improved model achieves about 98.8% accuracy.

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