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A new fault diagnosis algorithm for helical gears rotating at low speed using an optical encoder

机译:使用光学编码器的低速斜齿轮故障诊断新算法

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摘要

Helical gears are widely used in gearboxes due to its low noise and high load carrying capacity, but it is difficult to diagnose their early faults based on the signals produced by condition monitoring systems, particularly when the gears rotate at low speed. In this paper, a new concept of Root Mean Square (RMS) value calculation using angle domain signals within small angular ranges is proposed. With this concept, a new diagnosis algorithm based on the time pulses of an encoder is developed to overcome the difficulty of fault diagnosis for helical gears at low rotational speeds. In this proposed algorithm, both acceleration signals and encoder impulse signal are acquired at the same time. The sampling rate and data length in angular domain are determined based on the rotational speed and size of the gear. The vibration signals in angular domain are obtained by re-sampling the vibration signal of the gear in the time domain according to the encoder pulse signal. The fault features of the helical gear at low rotational speed are then obtained with reference to the RMS values in small angular ranges and the order tracking spectrum following the Angular Domain Synchronous Average processing (ADSA). The new algorithm is not only able to reduce the noise and improves the signal to noise ratio by the ADSA method, but also extracts the features of helical gear fault from the meshing position of the faulty gear teeth, hence overcoming the difficulty of fault diagnosis of helical gears rotating at low speed. The experimental results have shown that the new algorithm is more effective than traditional diagnosis methods. The paper concludes that the proposed helical gear fault diagnosis method based on time pulses of encoder algorithm provides a new means of helical gear fault detection and diagnosis. (C) 2016 Elsevier Ltd. All rights reserved.
机译:斜齿轮由于其低噪音和高承载能力而被广泛应用于齿轮箱中,但是很难根据状态监测系统产生的信号来诊断其早期故障,特别是当齿轮低速旋转时。在本文中,提出了使用小角度范围内的角域信号计算均方根(RMS)值的新概念。利用这一概念,开发了一种新的基于编码器时间脉冲的诊断算法,以克服低转速下斜齿轮故障诊断的困难。在该算法中,加速度信号和编码器脉冲信号是同时获取的。基于齿轮的转速和大小确定角域的采样率和数据长度。通过根据编码器脉冲信号在时域中对齿轮的振动信号重新采样来获得角域中的振动信号。然后,参考小角度范围内的RMS值和遵循角域同步平均处理(ADSA)的阶次跟踪频谱,获得了低转速下斜齿轮的故障特征。该新算法不仅可以通过ADSA方法降低噪声,提高信噪比,而且还可以从故障轮齿的啮合位置提取斜齿轮故障特征,克服了故障诊断的困难。斜齿轮低速旋转。实验结果表明,新算法比传统的诊断方法更有效。结论是,提出的基于编码器算法时间脉冲的斜齿轮故障诊断方法为斜齿轮故障的检测和诊断提供了一种新的手段。 (C)2016 Elsevier Ltd.保留所有权利。

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