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Statistical Vibration-Based Fault Diagnosis Approach Applied To Brushless DC Motors

机译：基于统计振动的无刷直流电动机故障诊断方法

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This paper proposes a statistical fault diagnosis approach based on vibration analysis, applied to brushless DC motors. First, the model of an inverter-fed permanent magnet motor, simulated using Matlab /Simulink, is briefly presented. The proposed model enables the generation of electrical, magnetic and vibration signals under healthy and faulty behaviors. The presented work focuses mainly on the rotor demagnetization and eccentricity faults. Then, different indicators including time, frequency, space and space harmonic characteristics are extracted from vibrations for different cases. These features are analyzed with respect to fault type and severity to select the most discriminative ones. For fault detection, a statistical test is used to compare each of the selected indicators with a decision threshold to detect the occurrence of a fault in the motor. The values of different thresholds are calculated in order to achieve a given low false alarm rate(α).The test power (1 ?β) of each fault indicator is also evaluated for its corresponding threshold. The fault isolation is then realized using a fault signature table. Finally, the proposed approach is tested on two sets of noisy simulated data related to different machine conditions.
机译：提出了一种基于振动分析的统计故障诊断方法，应用于无刷直流电动机。首先，简要介绍了使用Matlab / Simulink仿真的逆变器供电的永磁电动机的模型。所提出的模型能够在健康和错误行为下生成电，磁和振动信号。提出的工作主要集中在转子退磁和偏心故障上。然后，针对不同情况从振动中提取出包括时间，频率，空间和空间谐波特性在内的不同指标。针对故障类型和严重性对这些特征进行分析，以选择最具区别性的特征。对于故障检测，统计测试用于将每个选定的指标与决策阈值进行比较，以检测电动机中是否发生了故障。计算不同阈值的值以实现给定的低误报警率（α）。还评估了每个故障指示器的测试功率（1ββ）以获取其相应阈值。然后使用故障签名表实现故障隔离。最后，在与不同机器条件相关的两组噪声模拟数据上测试了该方法。

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