Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout

Bearing pitting, one of the common faults in mechanical systems, is a research hotspot in both academia and industry. Traditional fault diagnosis methods for bearings are based on manual experience with low diagnostic efficiency. This study proposes a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout regularization. Deep separable convolution extracts features from the raw bearing vibration signals, during which a 3 × 1 convolutional kernel with a one-step size selects effective features by adjusting its weights. The similarity pruning process of the channel convolution and point convolution can reduce the number of parameters and calculation quantities by evaluating the size of the weights and removing the feature maps of smaller weights. The spatial dropout regularization method focuses on bearing signal fault features, improving the independence between the bearing signal features and enhancing the robustness of the model. A batch normalization algorithm is added to the convolutional layer for gradient explosion control and network stability improvement. To validate the effectiveness of the proposed method, we collect raw vibration signals from bearings in eight different health states. The experimental results show that the proposed method can effectively distinguish different pitting faults in the bearings with a better accuracy than that of other typical deep learning methods.     

基于一维卷积神经网络和SoftMax分类器的风电机组行星齿轮箱故障检测

将卷积神经网络引入风机故障检测领域,设计了一种一维卷积神经网络的结构,并和SoftMax分类器相结合构造了一种双层智能诊断架构。一维卷积神经网络用于行星齿轮箱数据的特征提取,SoftMax分类器对提取的特征进行分类。与传统智能算法相比,该方法具有训练样本少,可直接使用原始数据训练网络;计算效率高,可以适应实时诊断的需要。试验结果证明,该方法可以有效地诊断出不同工况下的行星齿轮箱中的齿轮故障。     

Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning

Impulse components in vibration signals are important fault features of complex machines. Sparse coding(SC) algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding(FSC) and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.     

聚类分析的数据挖掘方法及其在机械传动故障诊断中的应用

采用网格化处理的思想,通过对基于密度的聚类分析方法进行改进,提出了一种新的聚类算法.这种算法通过对齿轮传动系统的故障信号进行测试、对故障类型进行了判定,对不同转速下齿轮传动振动信号进行谱熵计算、并采用网格划分方法将其表示在二维和三维空间分布平面内,可以较好地将正常、裂纹、磨损等类型的故障进行聚类和识别,并通过试验验证表明能够对不同工作状态的齿轮传动信号进行可靠的聚类与区分,聚类率为96%以上.说明该方法对齿轮故障进行区分与诊断是切实可行和有效的.      

基于小波神经网络的齿轮系统故障诊断

通过对齿轮系统在不同的运转状态下不同的故障类型进行试验测试分析,获取了有关的测试信号,对振动特征信号进行了小波阈值去噪,采用离散小波变换(DWT)对去噪后的信号进行8层分解处理,对各层的小波系数进行了小波重构,得到8层细节信号和1层近似信号,并计算了各层信号的能量,得到了信号的能量分布特征.在此基础上把各层信号特征作为神经网络的输入,进行了网络的研究、分析处理和故障分类,并对小波神经网络方法与单独采用神经网络方法的故障诊断结果进行了比较评价.研究表明,去噪处理后的效果比没有去噪的信号特征更加明显,而采用小波神经网络诊断方法,对于齿轮无故障、齿根裂纹故障、分度圆裂纹故障和齿面磨损故障能够进行很好地区分与诊断,其诊断成功率均在95%以上,可对实际工程工作的齿轮系统进行故障诊断.      

基于WPD-KVI-Hilbert变换相结合的滚动轴承早期故障特征精准识别

为了能够有效地从轴承早期故障激励的高频振动信号中提取出故障特征信息,基于最优小波包基选取方法和峭度值最大筛选原则,提出了一种改进的小波包分解（WPD）、峭度值指标（KVI）与Hilbert变换相结合的滚动轴承早期故障特征识别方法。计算选取最优小波包基,确定分解层数;采用WPD方法对轴承故障振动信号进行分解,获得若干个Node分量;基于峭度值指标最大原则筛选出有效的Node分量进行信号重构;对重构信号进行包络解调分析,提取出故障特征频率对轴承故障进行诊断。采用建立的方法对凯斯西储大学滚珠轴承外圈、内圈故障实验数据和自行开展的滚棒轴承外圈、滚动体故障实验数据进行了分析与诊断。研究结果表明：该方法能够有效提高故障信号高频分辨率、保留周期性冲击成分,并能准确有效提取出滚珠和滚棒轴承故障特征频率的1~7倍频及其与轴转频调制的系列边频带频率,实现对滚动轴承故障特征的精准识别与故障诊断。     

A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train

Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the helicopter. During health condition monitoring, the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear train. According to actual situation, this paper proposed a multi-criteria fusion feature selection algorithm (MCFFSA) to identify an optimal feature subset from the high-dimensional original feature space. In MCFFSA, a fault feature set of multiple domains, including time domain, frequency domain and wavelet domain, is first extracted from the raw vibration dataset. Four targeted criteria are then fused by multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find Proto-efficient subsets, wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme learning machine (SBELM). Further, F-measure is adopted to identify the optimal feature subset, which was employed for subsequent fault diagnosis. The effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission platform. The experimental results illustrate the superiority of combination of MOEA/D and SBELM in MCFFSA, and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms.     

基于CWT-CNN的齿轮箱运行故障状态识别

针对传统故障诊断中提取的特征不具有自适应能力、很难匹配特定故障的问题,提出了一种基于连续小波变换（CWT）和二维卷积神经网络（CNN）的齿轮箱故障诊断方法。该方法对齿轮箱故障振动信号采用连续小波变换构造其时频图,以其为输入构建卷积神经网络模型,通过多层卷积池化形成深层分布式故障特征表达。利用反向传播算法调整网络各层的结构参数,使模型建立从信号特征到故障状态之间的准确映射。在不同工况和不同故障状态下的实验中,故障识别准确率达到了99.2%,验证了方法有效性。采用这种自适应学习信号中丰富的信息的方法,可以为故障诊断智能化提供基础。      

基于参数自适应变分模态分解的行星齿轮箱故障诊断

针对变分模态分解需要人为设定模态数量以及在强噪声情况下容易造成分解错误的问题,提出了依据功率谱密度极值点自适应确定模态数量与中心频率的参数自适应变分模态分解方法,通过信号仿真分析验证了方法的有效性。基于参数自适应变分模态分解提出了一种行星齿轮箱故障诊断方法,应用于行星齿轮箱第2级太阳轮裂纹的故障诊断,行星齿轮箱传动实验台的试验结果表明:该方法能实现振动信号准确分解,有效提取和辨别出故障特征频率,实现了在强背景噪声和微弱故障信号情况下对第2级太阳轮裂纹故障的准确诊断。      

直升机行星传动轮系故障诊断研究进展

行星传动轮系是直升机传动系统的核心部件,是直升机健康和使用监测系统(HUMS)重要的监测对象。直升机行星传动轮系具有结构复杂紧凑、组件繁多、工况瞬时多变以及使用环境恶劣等特点,导致直升机行星传动轮系振动信号污染严重、成分复杂,具有较强的非平稳性和耦合调制特征。另外复杂的故障模式、较少的故障样本,也都增加了直升机行星传动轮系故障诊断的难度。面对这些难题,研究人员在基于信号降噪与信号分离、时频分析与解耦解调、数学建模与模式识别的故障诊断技术上取得了丰硕的成果。面对仍然存在的一些亟待研究和解决的问题,提出了直升机行星传动轮系故障诊断技术的研究方向以及未来的发展趋势。     

基于经验模式分解法的航空发动机振动传感器故障诊断技术研究

在深入研究经验模式分解法基本理论的基础上,针对航空发动机振动传感器故障的时频特征,提出一种基于经验模式分解法的传感器故障诊断新方法.该方法的关键在于将含有传感器故障的航空发动机振动测试信号进行经验模式分解,利用这种方法的局部自适应特性和时频多分辨率分析将传感器输出信号的局部特性细化,使故障信息凸显出来.分析结果表明,该方法可以准确诊断传感器软、硬故障,有效降低误报率和漏报率,具有很好的应用价值.     

基于形态分量分析与阶次跟踪的齿轮箱复合故障诊断方法

针对变转速下齿轮箱复合故障的故障特征提取,提出了基于形态分量分析与阶次跟踪的齿轮箱复合故障诊断方法.该方法根据齿轮箱复合故障振动信号中齿轮和滚动轴承故障成分的形态差异性,先用形态分量分析将其分解为包含齿轮局部故障信息的谐振分量、包含滚动轴承局部故障信息的冲击分量和随机噪声分量,再根据实测转速信号分别对谐振分量和冲击分量进行包络阶次分析,根据各包络阶次谱诊断齿轮箱复合故障.算法仿真和应用实例表明:该方法能有效分离变转速下齿轮和滚动轴承的故障特征,且其故障特征提取效果要优于传统的包络阶次谱方法.      

转速波动状态下涡轮泵典型故障诊断方法

利用涡轮泵振动信号的变换域信息可有效地检测与诊断故障。针对涡轮泵转子叶片断裂与脱落这种典型故障,首先分析其出现的原因,并从动力学的角度研究其振动特征,选择可有效反映该故障的特征频率。然而,涡轮泵转速波动会造成这些特征频率提取的困难,为此提出一种解决此难题的新思路,通过一系列变换域处理来消除转速波动对振动频率的影响,在变换域中提取出稳定的特征频率,从而解决了涡轮泵转速波动状态下该型故障诊断问题。通过涡轮泵历史试车故障数据的验证表明,通过跟踪变换域中这些特征频率的幅值变化,可以有效检测与诊断涡轮泵转子叶片断裂与脱落故障。     

A novel sparse feature extraction method based on sparse signal via dual-channel self-adaptive TQWT

Sparse signal is a kind of sparse matrices which can carry fault information and simplify the signal at the same time. This can effectively reduce the cost of signal storage, improve the efficiency of data transmission, and ultimately save the cost of equipment fault diagnosis in the aviation field. At present, the existing sparse decomposition methods generally extract sparse fault characteristics signals based on orthogonal basis atoms, which limits the adaptability of sparse decomposition. In this paper, a self-adaptive atom is extracted by the improved dual-channel tunable Q-factor wavelet transform (TQWT) method to construct a self-adaptive complete dictionary. Finally, the sparse signal is obtained by the orthogonal matching pursuit (OMP) algorithm. The atoms obtained by this method are more flexible, and are no longer constrained to an orthogonal basis to reflect the oscillation characteristics of signals. Therefore, the sparse signal can better extract the fault characteristics. The simulation and experimental results show that the self-adaptive dictionary with the atom extracted from the dual-channel TQWT has a stronger decomposition freedom and signal matching ability than orthogonal basis dictionaries, such as discrete cosine transform (DCT), discrete Hartley transform (DHT) and discrete wavelet transform (DWT). In addition, the sparse signal extracted by the self-adaptive complete dictionary can reflect the time-domain characteristics of the vibration signals, and can more accurately extract the bearing fault feature frequency.     

涡扇发动机气路传感器故障诊断

为了实现对某涡扇发动机传感器故障的在线诊断,提出并设计了1种基于在线贯序极端学习机的故障诊断算法。其核心思想是在定位某传感器故障后,在线建立针对该故障传感器"预学习"的信号重构算法,解决多故障混叠问题。在线信号重构算法以泛化能力指标为判定条件,利用选择策略对算法网络权值进行选择性更新,提高了故障诊断系统的实时性。以某型涡扇发动机为对象开展了传感器故障诊断与重构仿真,结果表明:该算法能够对发动机单、双传感器故障进行准确地诊断与信号重构,且具有良好的实时性。     

加快神经网络训练速度的方法在旋转机械故障诊断中的应用

根据一双跨转子实验台,模拟了转子与静子在轮盘处及轴颈处碰磨、轴系不对中及转子不平衡故障,通过一个信号自动处理装置记录下转子正常振动信号及发生各种故障时的信号,然后利用研制的人工神经网络系统对故障示例进行学习。通过在实际中诊断故障,证明这种是可行的。本文还针对人工神经网络(BP算法)存在的训练速度慢的问题,提出了一个加快网络训练速度的新方法(ARBP算法),较大提高了网络的训练速度。      

减速器振动信号的经验模态分解

时域同步平均是直升机减速器诊断技术的基础,目前这种方法依赖于转速传感器提供相位同步信号。探讨了应用经验模态分解代替时域同步平均分析减速器振动信号的方法。构建了一个减速器振动信号模型,提取了故障特征信号。对经验模态分解过程进行了理论推导,证明经验模态分解可以分离出故障特征信号,给出了信号分离的充分条件。将这种方法应用于直升机减速器的两种故障(点蚀和裂纹)振动数据,结果表明经验模态分解正确地分离出了故障特征信号,信号特征更为显著。     

基于频谱法与神经网络的航空起动发电机的故障检测与诊断

应用频谱法对航空直流起动发电机发电状态进行故障检测与诊断。采用对电机的电枢纹波电流信号进行频谱分析,提取该信号在频率域特征量,将频谱特征向量作为学习样本,通过训练,使神经网络能够反映频谱特征向量和故障类型的映射关系,从而达到故障检测与诊断的目的。电机故障实验和分析表明,与常规方法相比,频谱分析与神经网络相结合的方法进行实时检测和诊断具有简单、有效等优点。     

基于动态观测器的导弹武器系统故障诊断方法

为解决导弹武器系统多故障模式问题,提出了基于动态观测器的诊断方法,即采用1个动态观测器去检测一系列故障。当把耦合故障表示成故障信号的组合时,该方法可推广到耦合故障的诊断中。     

卷积神经网络和峭度在轴承故障诊断中的应用

针对传统智能诊断方法依靠专家知识和人工提取数据特征工作量大的问题,结合深度学习方法在特征提取和处理大数据方面的优势,研究了一种基于卷积神经网络和振动信号峭度指标的滚动轴承故障诊断方法。该方法将深度学习应用于轴承故障诊断,提取滚动轴承正常状态、内圈故障、外圈故障和滚动体故障4种状态的振动信号,将振动信号分段处理得到峭度指标,使用数据到图像的转换方法将峭度指标转换为灰度图,送入卷积神经网络模型完成故障分类。在进行滚动轴承故障诊断的实验时,所提的模型诊断准确率达到99.5%,高于传统支持向量机(SVM)算法的95.8%。