摘要
针对齿轮传动噪声信号复杂,且啮合产生的噪声往往被外界噪声所掩盖而不利于噪声分析的问题,提出了一种基于集合经验模态分解(EEMD)算法、时域同步平均(TSA)和BP神经网络结合的齿轮缺陷检测方法。首先运用集合经验模态分解算法将原始噪声信号分解,以齿轮啮频及其倍频为参考从中提取有用信号,并作时域同步平均进一步去噪;然后,计算去噪以后的特征,并选取不同缺陷状态下差异明显的特征,构建为一组特征向量;最后,将特征向量输入到BP神经网络分类器中进行缺陷的自动识别。研究结果表明,应用EEMD以及TSA相结合的方法去噪效果良好,数据进行处理以后所反映的缺陷特征明显;应用BP神经网络进行的智能识别避免了传统分析中过多依靠人主观判断而产生的缺陷,识别结果更准确。
Aiming at overcoming the difficulties in noise analysis, such as the complexity of the gear noise signals, and the interference by outside noises, a new noise analysis method was proposed based on ensemble empirical mode decomposition （EEMD）algorithm, time synchronous averaging（TSA）and back propagation （BP）neural network. EEMD was used to extract useful signals from the original signal based on the gear mesh frequency and multiplication of the mesh frequency. TSA was used for further de-noising. Then the feature values of the tested signals after de-noising were calculated. Discriminative features among different gear defect type were selected and taken as the input of BP neural network, the type of gear defect was effectively identified. The results of our experiments indicate that the proposed method based on EEMD and TSA enhances the denoise effect, and effctive features are obtained after the de-noising. Moreover, the inference of the gear defect type based on the BP neural network could avoid the disadvantages of humans＇ subjective judgments, and achieves accurate identification results.
出处
《机电工程》
CAS
2013年第6期678-682,共5页
Journal of Mechanical & Electrical Engineering
关键词
齿轮缺陷检测
集合经验模态分解
降噪
BP神经网络
时域同步平均
gear defect detection
ensemble empirical mode decomposition（EEMD）
denoising
back propagation（BP） neural network
time synchronous averaging（TSA）
