基于共振解调技术的滚动轴承故障自动诊断系统

介绍了基于共振解调技术的滚动轴承故障诊断原理、特点及实现方法 ,开发了可在线自动检测和诊断滚动轴承故障的软件系统     

基于FPGA的滚动轴承故障在线检测系统设计北大核心CSCD

针对传统滚动轴承故障诊断方案不能实时在线诊断的问题,设计了基于FPGA的滚动轴承故障在线检测系统。在硬件方面,设计了信号调理电路,实现了A/D转换,完成了滚动轴承信号在线采集,设计了上位机界面,能够实时显示检测结果,实现了结果的可视化;在算法方面,利用共振解调技术,完成了滚动轴承故障频率的提取。最后在QPZZ-Ⅱ实验台对系统进行测试,并与软件检测方法对比检验系统实时性能。结果表明:系统能有效提取滚动轴承故障特征频率,解决了传统检测方案效率低的问题,满足滚动轴承故障实时在线检测的需要。     

基于共振解调的滚动轴承故障诊断的研究与实现

滚动轴承故障诊断是机械故障检测中的一个重要方面,通过对滚动轴承典型故障机理及实际振动特征的分析,发现共振解调是一种有效分析滚动轴承故障信号的方法,而且具有一定的现实意义。     

实现滚动轴承损伤类故障自动诊断的一种简便方法

提出了一种用共振解调技术实现滚动轴承损伤类故障自动诊断的简便方法。     

浅析滚动轴承故障自适应谐振解调技术

本文提出一种利用共振进行共振解调的方法。运用经验模式分析方法,将故障划分成多个固有模式,以最小的特征值为主要特征,以最佳特征值为最大共振频率,并利用自适应选取不同的频段。利用希尔伯特变换,对滤波后的信号进行了理解、调和分析,得到包含故障特征的低频包络,并进行了辨识。与现有的谐波解调方法相比,该方法更具实际应用价值。     

基于平方包络谱相关峭度的最优共振解调诊断滚动轴承故障

利用峭度指标识别滚动轴承共振频带,结合包络分析解调故障特征,是滚动轴承故障诊断的常用方法。峭度指标虽然能够表征瞬态冲击特征的强弱,却无法利用瞬态冲击特征循环发生的特点,导致其难以区分脉冲噪声和循环瞬态冲击,无法准确识别共振频带,进而容易导致错误的故障诊断结果。受峭度和信号自相关的启发,重新定义相关峭度,提出平方包络谱相关峭度新指标;并结合Morlet小波滤波和粒子群优化算法,提出一种滚动轴承最优共振解调方法。通过与峭度、谱峭度等进行对比,仿真和试验分析结果表明平方包络谱相关峭度能够准确识别循环瞬态冲击;最优共振解调能够稳健确定共振频带的最优中心频率和带宽,准确解调诊断滚动轴承故障,验证了平方包络谱相关峭度在检测循环瞬态冲击和识别最优共振频带中的有效性和优越性。     

滚动轴承表面损伤故障智能诊断新方法

本文针对目前基于小波变换的滚动轴承故障诊断研究中普遍存在小波变换参数选取和故障特征计算无法自动完成的问题,提出了一种基于小波包变换的滚动轴承故障特征自动提取技术,实现了小波函数参数的自动选取和故障特征的自动提取.最后,基于结构自适应神经网络方法建立了滚动轴承的集成神经网络智能诊断模型,利用实际的滚动轴承实验数据进行了验证,结果表明了本文方法的有效性.     

滚动轴承故障诊断及经验总结

介绍了滚动轴承故障的基本形式、国内新近的监测方法及诊断原理,并针对滚动轴承故障进行了经验总结。     

滚动轴承故障程度识别与诊断研究

通过滚动轴承模拟故障试验台,获取了滚动轴承外圈、内圈和滚动体不同剥落程度时的振动信号,并对故障程度的识别与诊断进行了探索.采用经验模态分解方法对轴承信号进行分解,得到其固有模态分量,然后将前8阶分量的有效值作为特征向量输入BP神经网络,进行故障程度识别与诊断,滚动轴承3种类型不同程度的故障被准确地区分出来.     

球轴承滚动面划伤缺陷在线检测方法

建立了球轴承滚动面划伤缺陷的动力学模型,对轴承振动特性进行了仿真分析,使用AR谱估计方法自动捕捉高频共振的主频,并采用缺陷程度作为判断依据,从而提高信噪比。试验结果表明:该检测方法可以有效地进行轴承不同零件滚动面划伤缺陷的在线诊断,并自动识别缺陷类型。     

Application of an improved kurtogram method for fault diagnosis of rolling element bearings

Kurtogram, due to the superiority of detecting and characterizing transients in a signal, has been proved to be a very powerful and practical tool in machinery fault diagnosis. Kurtogram, based on the short time Fourier transform (STFT) or FIR filters, however, limits the accuracy improvement of kurtogram in extracting transient characteristics from a noisy signal and identifying machinery fault. Therefore, more precise filters need to be developed and incorporated into the kurtogram method to overcome its shortcomings and to further enhance its accuracy in discovering characteristics and detecting faults. The filter based on wavelet packet transform (WPT) can filter out noise and precisely match the fault characteristics of noisy signals. By introducing WPT into kurtogram, this paper proposes an improved kurtogram method adopting WPT as the filter of kurtogram to overcome the shortcomings of the original kurtogram. The vibration signals collected from rolling element bearings are used to demonstrate the improved performance of the proposed method compared with the original kurtogram. The results verify the effectiveness of the method in extracting fault characteristics and diagnosing faults of rolling element bearings.     

滚动轴承故障诊断的神经网络方法

总结分析了轴承的故障形式及原因,给出了振动频率,阐述了Bp网络的结构及算法,并对实例建立BP神经网络。     

滚动转子式压缩机故障的在线检测

探讨了滚动转子式压缩机故障的在线检测技术,通过对其工作过程的分析,确定了用壳体振动作为故障分析信号,并结合小波包和神经网络方法将正常与异常压缩机区分开来,现场的检测结果表明本文方法准确可靠,具有较高的检测效率。     

Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review

Vibration monitoring of rolling element bearings by the high-frequency resonance technique is reviewed. It is shown that the procedures for obtaining the spectrum of the envelope signal are well established, but that there is an incomplete understanding of the factors which control the appearance of this spectrum. Until the envelope spectrum can be fully explained, use of the technique is limited     

一种改进CNN在轴承故障诊断中的应用

针对轴承智能故障诊断过程中的特征自适应提取和在变工况下诊断能力差的问题,提出了一种基于特征通道权重调整的“端对端”一维卷积神经网络(Squeeze-Excitation Convolutional Neural Network,SECNN)滚动轴承故障诊断模型。首先采用一维卷积神经网络自适应地从原始振动信号中提取特征进行分类;然后通过增加特征通道权重模块来获取通道全局信息,学习特征通道之间的依赖关系;再据此对特征通道权重进行调整,增强滚动轴承故障诊断模型在变工况下的特征自适应提取能力。通过轴承实验台数据的验证结果表明:SECNN在多个变载荷工况下的故障诊断准确率均值达到97%,相比于传统方法提高了20%左右。同时利用t-SNE技术可视化特征提取过程,进一步验证了所提取的诊断模型的有效性。     

基于自动编码器数据降维的滚动轴承故障诊断研究

针对滚动轴承振动信号多域特征数据维数较高的问题,采用自动编码器(Auto-Encoder,AE)对特征数据进行降维处理,实现故障诊断.该方法首先提取滚动轴承振动信号中的特征数据,其次通过自动编码器对特征数据进行降维,最后将降维后的数据用于训练BP(Back Propagation)神经网络,并进行故障诊断.为验证自动编...     

Faults diagnosis of rolling element bearings based on modified morphological method

In order to effectively smooth noise and extract the impulse components in the vibration signals of defective rolling element bearings, a new modified morphology analytical method has been proposed. In this method, average of the closing and opening operator has been used as the morphology operator. Being the flat and zero adopted as the shape and the height of structure element (SE), respectively, the optimized length of SE is defined by a new proposed criterion (called SNR criterion). The effect of the new method is validated by both simulated impulsive signal and vibration signal of three defective rolling bearings with an outer, an inner and a rolling element faults and compared with Nikolaou’s method. The result shows that the proposed method has the superior performance in extracting impulsive characteristics of vibration signals, especially for the high level noise signals, and can implement better in diagnosis of defective rolling element bearing.     

Dry lubrication for rolling element spacecraft parts

Dry lubrication of spacecraft hardware is reviewed and some of the logic and test philosophy used in selecting and qualifying the dry lubricants presented. Examples of current applications are given, together with comments on and data from selected references     

An adaptive SK technique and its application for fault detection of rolling element bearings

In this paper, we propose an adaptive spectral kurtosis (SK) technique for the fault detection of rolling element bearings. The primary contribution is adaptive determination of the bandwidth and center frequency. This is implemented with successive attempts to right-expand a given window along the frequency axis by merging it with its subsequent neighboring windows. Influence of the parameters such as the initial window function, bandwidth and window overlap on the merged windows as well as how to choose those parameters in practical applications are explored. Based on simulated experiments, it can be found that the proposed technique can further enhance the SK-based method as compared to the kurtogram approach. The effectiveness of the proposed method in fault detection of the rolling element bearings is validated using experimental signals.     

Quantitative diagnosis of fault severity trend of rolling element bearings

The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condition and fault type but also the severity of the fault. This means fault severity quantitative analysis is one of most active and valid ways to realize proper maintenance decision. Aiming at the deficiency of the research in bearing single point pitting fault quantitative diagnosis, a new back-propagation neural network method based on wavelet packet decomposition coefficient entropy is proposed. The three levels of wavelet packet coefficient entropy(WPCE) is introduced as a characteristic input vector to the BPNN. Compared with the wavelet packet decomposition energy ratio input vector, WPCE shows more sensitive in distinguishing from the different fault severity degree of the measured signal. The engineering application results show that the quantitative trend fault diagnosis is realized in the different fault degree of the single point bearing pitting fault. The breakthrough attempt from quantitative to qualitative on the pattern recognition of rolling element bearings fault diagnosis is realized.