Research on the safety assessment of the brushless DC motor based on the gray model

Jinquan Xuan,Xiaohong Wang,Dawei Lu,Lizhi Wang

doi:10.1177/1687814017695438

Abstract

The brushless DC motor experiences operating safety problems due to the deterioration of its components following long-term operations, which are easily overlooked. To resolve these problems, failure mode, effects, and criticality analysis is utilized to characterize potential hazards in the motors. Hilbert–Huang transform is then employed to obtain the frequency-domain energy values of the vibration signals, which is defined as characteristic values that represent the performance degradation state. Second, gray model is selected to analyze the frequency-domain energy values and establish differential equations to predict the future vibration status, thereby achieving the vibration-based fault prediction. Furthermore, a gray safety assessment model is proposed to implement the safety assessment for the motor. The fault prediction and gray safety assessment are carried out based on historical data obtained from the brushless DC motor vibration experiment. The accuracy level of the gray model predictions is classified as Wonderful, thereby demonstrating the efficiency of gray model for the fault prediction. In addition, as low as reasonably practicable law is chosen to classify risk levels and formulate safety strategies in accordance with the results of safety assessment. Finally, the proposed safety effects of the methods and strategies are evaluated for microscopic and macroscopic levels.

Highlights

The brushless DC (BLDC) motor is widely applied in various industrial fields such as in oil/water pump driving motors, robots, and electrical vehicles due to its attractive features, namely, its energy efficiency, high torque, high rotational speed, and low noise level.[1,2,3] a growing number of applications rely on BLDC motors, which has increased concerns over its reliability and safety
Sr this study proposed the severity ratio (Sr), which is the relative extent of severity for accidents in various systems
A comparison analysis was conducted to highlight the Gray model (GM) attributes for small sample sizes using the support vector machine (SVM) and the back propagation artificial neural network (BP-ANN), which are commonly used in the vibration-based fault prediction technique.[11,35,36]

Summary

Introduction

The brushless DC (BLDC) motor is widely applied in various industrial fields such as in oil/water pump driving motors, robots, and electrical vehicles due to its attractive features, namely, its energy efficiency, high torque, high rotational speed, and low noise level.[1,2,3] a growing number of applications rely on BLDC motors, which has increased concerns over its reliability and safety. This study proposed GM-based methods to obtain quick and precise fault predictions and safety assessments for the BLDC motor with a small sample size. The degradation characteristic parameters of the BLDC motor were obtained following vibration signals processing using the above-mentioned methods. A comparison analysis was conducted to highlight the GM attributes for small sample sizes using the support vector machine (SVM) and the back propagation artificial neural network (BP-ANN), which are commonly used in the vibration-based fault prediction technique.[11,35,36] The assessment results of the three methods were obtained based on the same prediction means and experimental data (Table 7).

Methods

Findings

Conclusion