【作者】 杨超；

【导师】 毛文涛；

【作者基本信息】 河南师范大学 ， 计算机科学与技术， 2020， 硕士

【摘要】 滚动轴承作为机械设备的关键支撑部件,由于长期在复杂工况下运行,极易产生各种故障,导致机械设备工作状况恶化。随着传感技术的迅速发展,对机械设备运行过程中顺序、大量、快速且持续到达的流数据进行不停机故障检测变得尤为重要,具有明确的学术价值和应用需求。然而,对于滚动轴承在线异常检测问题,目前仍存在以下问题:1)检测模型无法适应在线数据的轻微波动,使得检测模型将正常样本误判为异常,从而引起较高的误报警率;2)在强噪声背景下提取弱信号难度较大,且受工作环境、设备状况等因素的影响,数据分布存在差异,基于离线工况数据训练得到的检测模型在在线工况数据上表现效果较差,从而降低检测模型鲁棒性。基于此,本文根据滚动轴承在线异常检测的需求与特点,在理论上引入增量式学习方法、注意力机制以及迁移学习方法,分别从适应在线数据不规则波动和提取强噪声背景下跨工况轴承在线异常检测特征入手,以降低检测结果误报警率为目标,提高轴承在线异常检测效果。主要工作和贡献如下:(1)针对传统异常检测方法无法自适应在线数据轻微波动的问题,本文提出了一种增量式加权支持向量数据描述(Incremental Weighted Support Vector Data Description,IW-SVDD)算法,用于滚动轴承的在线异常检测。首先,基于已有在线数据训练一个初始SVDD检测模型,并对贯序到达的在线数据进行预检测;其次,为了使检测模型能够适应在线数据的轻微波动,本文设计了一种样本状态判定(Sample State Determination,SSD)策略,该策略将在线数据划分为异常首次出现、异常连续出现、异常消失以及异常重复出现四种状态,并根据相应状态对样本赋予相应的权值;然后,使用预检测结果中违背了KKT条件的异常样本,等量替换掉原始检测模型的训练集最早期的样本,从而对训练集进行在线更新,并重新训练模型;最后,通过再训练后的SVDD检测模型对在线数据进行再检测,并得到检测结果。在IEEE PHM 2012挑战赛数据集上进行对比实验,结果表明,IW-SVDD模型能在保证检测的准确率的前提下,有效降低检测结果的误报警率。(2)针对强噪声背景下故障特征提取难度大且跨工况数据分布不一致的问题,本文提出了一种面向滚动轴承在线异常检测的深度迁移学习方法,用于跨工况下滚动轴承在线异常检测。首先,将监测信号处理成由原始信号-边际谱-频谱组成的三通道形式;其次,通过在残差注意力模块中增加不同尺寸的滤波器,并使用卷积-反卷积来重构输入信息,构建了一种多尺度残差注意力模块,用于提取对异常数据表征能力更强的注意力特征;最后,在所提取到的注意力特征基础上,构建基于交叉熵和最大均值差异正则化约束的损失函数,实现领域适配,并采用随机梯度下降算法对网络模型的参数进行优化求解,最终构建端到端的异常检测模型。在IEEE PHM 2012挑战赛数据集上的实验结果表明,与5种代表性的异常检测和诊断方法相比,本文方法能够在不延迟报警时间点的前提下,有效降低误报警。本文工作不仅适用于滚动轴承同时也可以扩展到其他类型的旋转机械,为各类旋转机械的在线健康管理和状态监控提供了新的解决方案,具有显著的理论价值与实际工程应用价值。更多还原

【Abstract】 Rolling bearings,as the key supporting components of mechanical equipment,are prone to various failures due to long-term operation under complex working conditions,which leads to the deterioration of the working conditions of mechanical equipment.With the rapid development of sensing technology,non-stop failure detection of sequential,large,fast and continuous arrival of streaming data during the operation of mechanical equipment has become particularly important,with clear academic value and application requirements.However,for the problem of online abnormal detection of rolling bearings,the following problems still exist: 1)The detection model cannot adapt to the slight fluctuations of online data,so that the detection model misinterprets the normal sample as abnormal,thereby causing a high false alarm rate;It is more difficult to extract weak signals under the background of strong noise,and due to factors such as working environment and equipment conditions,the data distribution is different.The detection model trained based on offline working condition data performs poorly on online working condition data,thus Reduce the robustness of the detection model.Based on this,this paper theoretically introduces incremental learning methods,attention mechanisms,and transfer learning methods based on the needs and characteristics of online abnormal detection of rolling bearings,respectively to adapt to irregular fluctuations in online data and extract cross-bearing bearings under strong noise background Starting with the online anomaly detection feature,the goal is to reduce the false alarm rate of the test results and improve the online anomaly detection effect of the bearing.The main work and contributions are as follows:(1)In response to the problem that traditional anomaly detection methods cannot adapt to slight fluctuations in online data,this paper proposes an incremental weighted support vector data description(IW-SVDD)algorithm for online rolling bearings abnormal detection.First,train an initial SVDD detection model based on the existing online data,and pre-detect the online data that arrives sequentially.Second,in order to make the detection model adapt to the slight fluctuations of online data,this paper designed a sample state determination State Determination(SSD)strategy,which divides online data into four states: first occurrence of abnormality,continuous appearance of abnormality,disappearance of abnormality,and repeated occurrence of abnormality,and the corresponding weight is given to the sample according to the corresponding state;then,the pre-test results are used Anomalous samples that violate the KKT condition are replaced by the same amount of the earliest samples in the training set of the original detection model,so that the training set is updated online and the model is retrained;finally,the online data is re-trained by the retrained SVDD detection model Re-test and get the test result.The comparison experiment on the IEEE PHM 2012 Challenge data set shows that the IW-SVDD model can effectively reduce the false alarm rate of the detection results while ensuring the accuracy of the detection.(2)Aiming at the problem that the fault feature extraction is difficult under strong noise background and the data distribution is inconsistent across operating conditions,this paper proposes a deep transfer learning method for online abnormal detection of rolling bearings,which is used for online abnormal detection of rolling bearings under cross operating conditions.First,the monitoring signal is processed into a three-channel form consisting of the original signal-marginal spectrum-spectrum;secondly,by adding filters of different sizes to the residual attention module,and using convolution-deconvolution to reconstruct the input Information,a multi-scale residual attention module is constructed to extract attention features with greater ability to represent abnormal data;finally,based on the extracted attention features,a cross-entropy and maximum mean difference are constructed Regularize the constrained loss function to achieve domain adaptation,and use the stochastic gradient descent algorithm to optimize the parameters of the network model,and finally build an end-to-end anomaly detection model.Experimental results on the IEEE PHM 2012 Challenge data set show that,compared with the five representative anomaly detection and diagnosis methods,this method can effectively reduce false alarms without delaying the alarm time.This work is not only applicable to rolling bearings but can also be extended to other types of rotating machinery.It provides new solutions for online health management and condition monitoring of various rotating machinery,and has significant theoretical value and practical engineering application value.更多还原