Partial discharge (PD) diagnostics is a reliable technique for the health assessment of electrical motors. This paper presents a unique methodology for the failure risk assessment of the low voltage motors containing various insulation defects based on PD measurements. The severity of PD in the motors increases with the time during their operation due to electrical and environmental stresses. In this work, several artificial PD defects have been created in low voltage induction motors under laboratory conditions. Accordingly, the characteristic parameters of PD measured in the laboratory for the artificial defects are evaluated to identify their impact on the resulting degradation of the insulation. Furthermore, the classification of these defects has been carried out based on cumulative energy function using K-mean clustering algorithm followed by the estimation of insulation lifetime. In this regard, Weibull distribution has been employed to quantify the probability of failure and risk evaluation corresponding to the severity of PD defects. The proposed risk assessment framework may be utilized to support asset managers in scheduling the regular maintenance activities and assist them in decision making about the type of actions required to eliminate the latent threat.

局部放电（PD）诊断是一种用于电动机健康状况评估的可靠技术。本文提出了一种独特的方法，用于基于PD测量来评估包含各种绝缘缺陷的低压电动机的故障风险。由于电气和环境应力，电动机中PD的严重性会随着运行时间的延长而增加。在这项工作中，在实验室条件下的低压感应电动机中已经产生了一些人为的PD缺陷。因此，评估在实验室中为人工缺陷测得的局部放电的特征参数，以识别其对所导致的绝缘劣化的影响。此外，这些缺陷的分类是使用K均值聚类算法根据累积能量函数进行的，然后估算绝缘寿命。在这方面，已采用威布尔分布来量化与PD缺陷严重程度相对应的失效概率和风险评估。建议的风险评估框架可用于支持资产经理安排定期维护活动，并帮助他们制定消除潜在威胁所需的行动类型决策。