Identification of Mechanical Fault of Induction Motor by Combining Lyapunov Exponent and Random Forest Algorithm

Yan Liu, Jincheng Geng, Yuchen Li, Yiming He
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Abstract

In this paper, a scheme by combining Lyapunov exponent and random forest algorithm for mechanical fault identification of induction motor is proposed. During the implementation of the scheme, the severity of pedestal looseness is identified. First, operating states of motors are simulated in an experimental platform. Vibration signals are measured and the evolution of signal waveform are revealed. Subsequently, for vibration signals of different input frequencies or different number of pedestal loose bolts, Lyapunov exponential spectrums are calculated by BBA algorithm. Features extracted from Lyapunov exponential spectrums, such as largest Lyapunov exponent and Kolmogorov entropy, are utilized to demonstrate the chaotic characteristics of signals. At last, extracted features are fed into random forest model for the fault classification. The feasibility of this scheme is validated by accuracy of fault recognition greater than 90%. Several groups of experimental results indicate that proposed scheme has high effectiveness and generalization performance.
李雅普诺夫指数与随机森林算法相结合的感应电机机械故障识别
提出了一种将李雅普诺夫指数与随机森林算法相结合的异步电动机机械故障识别方案。在方案实施过程中,确定了底座松动的严重程度。首先,在实验平台上模拟了电机的运行状态。对振动信号进行了测量,揭示了信号波形的演变规律。随后,对于不同输入频率或不同基座松动螺栓个数的振动信号,采用BBA算法计算Lyapunov指数谱。利用从Lyapunov指数谱中提取的特征,如最大Lyapunov指数和Kolmogorov熵,来展示信号的混沌特性。最后,将提取的特征输入到随机森林模型中进行故障分类。通过故障识别准确率大于90%,验证了该方案的可行性。多组实验结果表明,该方案具有较高的有效性和泛化性能。
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