Most of the published research studies for detecting induction motor broken bar faults (BBFs) use a time-frequency (t - f) decomposition tool to characterize the fault-related components. However, the identification and the assessment of these components in (t - f) domain require skilled user or powerful pattern recognition technique. Moreover, a relatively long starting duration is necessary. This article introduces an automated scheme to detect BBFs and distinguish fault severity in induction motors under startup conditions regardless of the user experience and even under short starting duration and in a noisy environment. This scheme is based on the analysis of the starting current using optimized Stockwell transform (ST). An active-set algorithm is applied to maximize the energy concentration of the left-side harmonic (LSH) component. Then, an adaptive time-frequency filter is applied to extract the LSH component from the (t - f) domain, where the energy of the right part of LSH (RLSH) is utilized as an effective index for BBFs detection and for discriminating BBFs severity. Both real experimental data and simulation-based tests on 0.746- and 11-kW motors are used to extensively verify the performance of the proposed scheme. The achieved results have ensured that the proposed scheme can achieve a high accuracy with the minimum data and shortest acquisition time in comparison with some recent methods in the literature.

中文翻译：

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使用优化斯托克韦尔变换和自适应时间频率滤波器进行感应电机启动条件下的断条故障检测


大多数已发表的检测感应电机断条故障 (BBF) 的研究都使用时频 (t - f) 分解工具来表征与故障相关的组件。然而，（t - f）域中这些组件的识别和评估需要熟练的用户或强大的模式识别技术。此外，需要相对较长的启动持续时间。本文介绍了一种自动化方案，用于在启动条件下检测 BBF 并区分感应电机的故障严重程度，无论用户体验如何，甚至在启动持续时间短和嘈杂的环境中也是如此。该方案基于使用优化斯托克韦尔变换（ST）对启动电流进行分析。应用活动集算法来最大化左侧谐波（LSH）分量的能量集中。然后，应用自适应时频滤波器从（t - f）域中提取LSH分量，其中LSH右侧部分的能量（RLSH）被用作BBF检测和判别BBF严重程度的有效指标。 0.746 和 11 kW 电机的真实实验数据和基于仿真的测试都用于广泛验证所提出方案的性能。与文献中的一些最新方法相比，所取得的结果确保了所提出的方案能够以最少的数据和最短的采集时间实现高精度。