﻿ 基于轴承座振动的船舶推进轴系不对中故障特征提取方法
 舰船科学技术  2022, Vol. 44 Issue (15): 113-118    DOI: 10.3404/j.issn.1672-7649.2022.15.023 PDF

Fault feature extraction method of ship propulsion shafting misalignment based on bearing seat vibration
QIU Shi-hao, LI Guo-bin, XING Peng-fei, GAO Hong-lin, LU Li-xun
Marine Engineering College, Dalian Maritime University, Dalian 116026, China
Abstract: In order to extract the misalignment fault characteristics of ship shafting from the vibration of bearing seating, a detection and extraction method based on stochastic resonance and harmonic wavelet packet was proposed. The alignment experiments were carried out on the ship shafting test bench, the shafting misalignment fault signal was detected and extracted from the collected bearing seating vibration, and the law of the misalignment fault signal with the alignment state was analyzed. The results show that the misalignment vibration response of double frequency exists in the bearing seating vibration under the straight alignment state. As the elevation of the stern bearing increases, the misalignment of the shafting gradually deteriorates, and the amplitude of the extracted characteristic vibration gradually increases. Therefore, based on stochastic resonance and harmonic wavelet packet, the misalignment fault features of ship shafting can be extracted from the vibration of bearing seating.
Key words: ship propulsion shafting     misalignment fault     bearing seating vibration     detection     extraction
0 引　言

1 方　法

1.1 检测

 $\frac{{{\rm{d}}x}}{{{\rm{d}}t}} = - U'(x) + s(t) + n(t)。$ (1)

 $SNR = 10\lg \frac{{S({f_0})}}{{N({f_0})}}。$ (2)

 $\begin{split} & {v_i^d = wv_i^{d - 1} + {c_1}{r_1}(pbest_i^d - x_i^d)}+ \\ & { {c_2}{r_2}(gbes{t^d} - x_i^d)} ，\end{split}$ (3)
 $x_i^{d + 1} = x_i^d + v_i^d。$ (4)

1.2 提取

1）根据最高分析频率为fh和分解层数j，由式（5）和式（6）确定分析带宽B和不对中特征振动信号所在分析频带的上、下限mn

 $B = {2^{ - j}}{f_h}，$ (5)
 $\begin{split} & {m = sB,n = (s + 1)B} ，\\ & {s = 0,1,2, \cdots ,{2^{j - 1}}} 。\end{split}$ (6)

2）计算分析频带的谐波小波的频域值。

 $\begin{array}{l} {h}_{m,n}[(n-m)\omega ]=\\ \Bigg\{\begin{array}{ll}1/[(n-m)2 \text{π} ]，& 2\text{π} m \leqslant \omega < 2 \text{π} n，\\ 0，&{\rm{ others}}。\end{array}\end{array}$ (7)

3）对振动信号f(t)进行快速傅里叶变换，求得其频域离散值f(w)。

4）由式（8）计算不对中特征振动信号所在分析频带的谐波小波变换的频域值，进行频域特征分析。

 $Q(m,n,w) = f(\omega ){h_{m,n}}[(n - m)\omega ]。$ (8)

5）对上一步得到的频域值进行逆快速傅里叶变换，得到谐波小波变换后不对中特征振动信号所在频段的时域信号，进行时域特征分析。

2 不对中状态下轴承座振动分析 2.1 实验 2.1.1 实验条件

 图 1 实验台结构示意图 Fig. 1 Schematic diagram of the experimental platform structure
2.1.2 实验方法

2.2 结果与讨论 2.2.1 特征振动信号检测

 图 2 不同校中状态下尾后轴承处振动信号的时域波形与频谱 Fig. 2 Time-domain waveform and frequency spectrum of the vibration signal at the stern rear bearing in different alignment conditions

 图 3 不同校中状态下随机共振输出信号时域波形与频谱 Fig. 3 Time-domain waveform and frequency spectrum of stochastic resonance output signal under different alignment conditions

2.2.2 特征振动信号的提取及分析

 图 4 不同校中状态下不对中特征振动信号的时域波形与频谱 Fig. 4 Time-domain waveform and frequency spectrum of the characteristic vibration signal of misalignment under different alignment conditions

 图 5 不同校中状态下特征振动信号有效值变化趋势 Fig. 5 The change trend of the effective value of the characteristic vibration signal under different alignment conditions

3 结　语

1）应用基于粒子群算法优化的随机共振可实现轴承座振动中的船舶轴系不对中故障特征的检测。

2）应用谐波小波包可对检测到的特征振动信号进行提取，其有效值的变化能够反映校中状态的改变。

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