Denoising Fault-Aware Wavelet Network:A Signal Processing Informed Neural Network for Fault Diagnosis

Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et.

Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.

Dynamics and Fault Diagnosis of Railway Vehicle Gearboxes:A Review

The railway vehicle gearbox is an important part of the railway vehicle traction transmission system which ensures the smooth running of railway vehicles.However,as the running speed of railway vehicles continues to increase,the railway vehicle gearbox is exposed to a more demanding operating environment.Under both internal and external excitations,the gearbox is prone to faults such as fatigue cracks,and broken teeth.It is crucial to detect these faults before they result in severe failures and accidents.Therefore,understanding the dynamics and fault diagnosis of railway vehicle gearbox is needed.At present,there is a lack of systematic review of railway vehicle gearbox dynamics and fault diagnosis.So,this paper systematically summarizes the research progress on railway vehicle gearbox dynamics and fault diagnosis.To this end,this paper first summarizes the latest research progress on the dynamics of railway vehicle gearboxes.The dynamics and vibration characteristics of the gearbox are summarized under internal and external excitations,as well as faulty conditions.Then,the stateof-the-art signal processing and artificial intelligence methods for fault diagnosis of railway vehicle gearboxes are reviewed.In the end,future research prospects are given.

Time-frequency Feature Extraction Method of the Multi-Source Shock Signal Based on Improved VMD and Bilateral Adaptive Laplace Wavelet

Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery.Therefore,it is difficult to extract,analyze,and diagnose mechanical fault features.To accurately extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery,a study on the time-frequency feature extraction method of multi-source shock signals is conducted.Combining the characteristics of reciprocating mechanical vibration signals,a targeted optimization method considering the variational modal decomposition(VMD)mode number and second penalty factor is proposed,which completed the adaptive decomposition of coupled signals.Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals,a new bilateral adaptive Laplace wavelet(BALW)is established.A search strategy for wavelet local parameters of multi-shock signals is proposed using the harmony search(HS)method.A multi-source shock simulation signal is established,and actual data on the valve fault are obtained through diesel engine fault experiments.The fault recognition rate of the intake and exhaust valve clearance is above 90%and the extraction accuracy of the shock start position is improved by 10°.

Compound Fault Diagnosis for Rotating Machinery:State-of-the-Art,Challenges,and Opportunities

Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

A NEW QUADRATIC TIME-FREQUENCY DISTRIBUTIONAND A COMPARATIVE STUDY OF SEVERAL POPULARQUADRATIC TIME-FREQUENCY DISTRIBUTIONS

A new quadratic time-frequency distribution (TFD) with a compound kernel is proposed and a comparative study of several popular quadratic TFD is carried out. It is shown that the new TFD with compound kernel has stronger ability than the exponential distribution (ED) and the cone-shaped kernel distribution (CKD) in reducing cross terms, meanwhile almost not decreasing the time-frequency resolution of ED or CKD.

Improved Multi-Bandwidth Mode Manifold for Enhanced Bearing Fault Diagnosis

Variational mode decomposition(VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold(Triple M, TM) is one variation of the VMD, which units multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm named local tangent space alignment(LTSA). The merit of the TM method is that the bearing fault-induced transients extracted contain low level of in-band noise without optimization of the VMD parameters. However, the determination of the neighborhood size of the LTSA is time-consuming, and the extracted fault-induced transients may have the problem of asymmetry in the up-and-down direction. This paper aims to improve the efficiency and waveform symmetry of the TM method.Specifically, the multi-bandwidth modes consisting of the fault-related modes with different bandwidths are first obtained by repeating the recycling VMD(RVMD) method with different bandwidth balance parameters. Then, the LTSA algorithm is performed on the multi-bandwidth modes to extract their inherent manifold structure, in which the natural nearest neighbor(Triple N, TN) algorithm is adopted to efficiently and reasonably select the neighbors of each data point in the multi-bandwidth modes. Finally, a weight-based feature compensation strategy is designed to synthesize the low-dimensional manifold features to alleviate the asymmetry problem, resulting in a symmetric TM feature that can represent the real fault transient components. The major contribution of the improved TM method for bearing fault diagnosis is that the pure fault-induced transients are extracted efficiently and are symmetrical as the real. One simulation analysis and two experimental applications in bearing fault diagnosis validate the enhanced performance of the improved TM method over the traditional methods. This research proposes a bearing fault diagnosis method which has the advantages of high efficiency, good waveform symmetry and enhanced in-band noise removal capability.

A New Method for Rolling Element Bearing Fault Diagnosis Based on Cyclostationary Theory

The theory of cyclostationary and its application are very important for the analysis and processing of a non stationary signal. The paper introduces second order cyclostationary statistics, with emphass on discussion of cyclic periodogram arithmetic. Comparing the time smoothed cyclic periodogram with the frequency smoothed cyclic periodogram, we found that the former is more useful to extract the feature of cyclostationary signals. The method has been applied to analyze the vibration signal of a rolling element bearing measured on a test bench, and proved to be effective. Meanwhile, we have compared it with traditional power spectral density analysis, and the results prove that the time smoothed cyclic periodogram is more available to diagnose the fault of a rolling element bearing.

An Improved Second-Order Multisynchrosqueezing Transform for the Analysis of Nonstationary Signals

Second-order multisynchrosqueezing transform(SMSST),an effective tool for the analysis of nonstationary signals,can significantly improve the time-frequency resolution of a nonstationary signal.Though the noise energy in the signal can also be enhanced in the transform which can largely affect the characteristic frequency component identification for an accurate fault diagnostic.An improved algorithm termed as an improved second-order multisynchrosqueezing transform(ISMSST)is then proposed in this study to alleviate the problem of noise interference in the analysis of nonstationary signals.In the study,the time-frequency(TF)distribution of a nonstationary signal is calculated first using SMSST,and then aδfunction is constructed based on a newly proposed time-frequency operator(TFO)which is then substituted back into SMSST to produce a noisefree time frequency result.The effectiveness of the technique is validated by comparing the TF results obtained using the proposed algorithm and those using other TFA techniques in the analysis of a simulated signal and an experimental data.The result shows that the current technique can render the most accurate TFA result within the TFA techniques employed in this study.

储能变流器信号高精度故障诊断方法

随着能源转型和碳中和的全球发展趋势,储能变流器关键组件的稳定性变得至关重要。特别是其功率器件和散热器在实际运行中的稳定性直接关系到整个系统的可靠性。关注储能变流器功率模组振动信号的故障诊断问题,传统诊断方法处理复杂信号时往往面临挑战,需要频繁地调整参数。此外,由于储能变流器的工作环境复杂,现有深度学习诊断方法的性能也不尽如人意。为此,提出一种基于大模型知识和通道注意力网络的储能变流器功率模组故障诊断方法LLMCAN。首先通过预训练的大规模语言模型,在特征提取过程中利用丰富的领域知识,增强模型对复杂功率模组振动信号的分析能力。其次引入通道注意力网络使模型能够自适应学习信号中不同通道之间的关系,提高故障诊断的准确性。在包含1000条真实工况数据的储能变流器信号数据集上进行验证,其中包括正常工况和9种故障模式。实验结果表明,该方法在多种度量指标下均显示出优越性能,其中诊断准确率高达99.8%,远超传统方法,为储能变流器功率模组的故障诊断提供一个高效、准确的解决方案。

针对冲击性故障信号的谱融合特征提取算法

利用盲解卷积方法在时域中进行故障信号特征提取时,常会出现多个信号混淆分离结果,但以往的研究中只强调了分离的部分,而很少对分离后的信号进行进一步的处理,给实际应用造成不便。这里在盲解卷积和谱融合的基础之上,使用核改进的模糊c均值聚类算法,针对机械故障信号的脉冲特性,提出一种针对冲击性故障信号处理的实用型算法。计算机仿真实验证实了该算法的有效性。此算法优化了以往的聚类筛选方法,可以有效排除反卷积后诸多无用信号的干扰,将故障脉冲信号的特征准确提取出来,能提高故障诊断的效率。

MCKD在一种新型随机共振系统下的转动体故障诊断研究

为解决由高阶项限制引起的输出饱和问题,利用分段势函数抗饱和的优良特性,提出了一种新的非饱和三稳二阶随机共振(UTSOSR)系统。首先,通过仿真实验验证了该系统能够显著改善经典三稳二阶随机共振系统的输出饱和问题。其次,基于绝热近似理论,推导出UTSOSR系统的稳态概率密度,平均首次通过时间和功率谱放大因子(SA),并通过分析系统各参数对这些性能指标的影响,来更加深入地探究系统的动力学行为。将SA和信噪比增益(Gsnr)作为评价指标,通过数值仿真验证了UTSOSR系统具有更优越的信号增强和抗噪声性能。同时,为了获得更优的输出性能,将最大相关峭度解卷积(MCKD)与UTSOSR系统相结合,提出MCKD-UTSOSR方法对目标信号特征进行提取。最后,联合遗传算法和变步长网格优化算法寻找MCKD-UTSOSR方法的最优参数,并应用于转动体微弱故障信号检测。数据分析结果表明,MCKD-UTSOSR方法相比于其他方法,其信噪比提升了1.1289~23.5854 dB,谱峰峰值提升了88.423~7488.118133,为实际工程中高效的信号处理和故障检测提供了创新和可靠的解决方案。

轴流风机故障诊断中的信号特征选择方法研究

为确保煤矿工作人员的安全及提高生产效率,研究了一种基于多类型信号特征选择的轴流风机故障诊断模型。该模型主要通过监测风机在不同工况下的振动加速度信号,并经过信号处理获得速度信号和位移信号。通过提取这些信号的时频域统计特征,并利用极致梯度提升(Extreme Gradient Boosting,XGBoost)技术进行特征选择,以增强诊断准确性。实验结果显示,通过特征选择优化后的多信号数据集,模型在测试集上的平均判识准确率达到98.33%,对数损失仅为0.0534。相较于单一信号或未进行特征选择的数据集,模型表现出更高的效率和准确度,显著提升了故障诊断的可靠性和速度,从而有效减少了由风机故障可能导致的安全隐患。

基于SO-PAA-GAF和AdaBoost集成学习的高压断路器故障诊断

针对在小样本和复杂工况下高压断路器故障诊断识别精度不高的问题,提出一种基于振动信号处理和Ada Boost集成学习的高压断路器故障诊断方法。首先,搭建高压断路器实验平台并采集8种工况下的分闸振动信号。其次,对振动信号进行绝对值处理后,使用分段聚合近似(piecewise aggregate approximation,PAA)进行分段平均,将输出的新序列采用格拉姆角场(Gramian angular field,GAF)转换成图片,并使用Relief F方法对提取的高维图片特征进行重要度排序。最后,将保留的重要特征输入到Ada Boost集成学习模型进行故障诊断,并用蛇优化算法确定最优PAA分段步长和输入分类器特征数量,以进一步提高故障诊断精度。通过分析多种信号处理方式及分类模型可知,图片信号和Ada Boost集成学习模型能够有效处理振动信号并准确判断故障类型,为准确、可靠地诊断高压断路器故障提供了新途径。

滚动轴承健康智能监测和故障诊断机制研究综述

轴承作为工业设备机械系统中最关键并且最容易发生故障的零件之一,长期处在高负荷的运行状态。当其发生故障时或者不可逆的磨损时,可能带来事故甚至造成巨大经济损失。因此,对其进行有效的健康监测和故障诊断,对于保障工业设备安全稳定运行有着重要的意义。为进一步促进轴承健康监测和故障诊断技术的发展,对当前现有的模型及方法进行分析与总结,并对现有技术进行划分、对比。从使用的振动信号数据分布出发,首先,对数据分布均匀下的相关方法进行整理,主要按照基于信号分析和基于数据驱动两方面进行研究现状的分类、分析与总结,对该情况下故障检测方法所存在的不足与缺陷进行概述。其次,考虑实际工况下数据采集通常具有不均衡特性的问题,对处理该类情况下的检测方法进行总结,并将现有研究中对该问题的不同处理技术根据其侧重点不同分为数据处理方法、特征提取方法、模型改进方法,并对所存在的问题进行分析。最后,对现有工业设备中轴承故障检测存在的挑战及未来发展方向进行了总结与展望。

基于改进层次斜率熵(IHSloE)的信号低频和高频故障特征提取方法

采用传统的基于粗粒化处理的多尺度特征提取方法,无法提取故障信号中的高频部分的故障信息,导致其提取到的故障特征难以准确地表征滚动轴承的故障状态和动态特性,无法保证故障诊断的可靠性和准确性。针对该缺陷,提出了一种基于改进层次斜率熵(IHSloE)和随机森林(RF)的滚动轴承故障诊断方法。首先,利用改进层次化处理代替粗粒化处理,实现了信号的多尺度分析目的,基于斜率熵,提出了改进层次斜率熵的非线性动力学指标;随后,利用IHSloE方法提取了滚动轴承振动信号的故障特征,建立了表征滚动轴承故障特性的故障特征;最后,基于RF模型建立了多故障分类器,并将故障特征输入至RF分类器进行了训练和测试,以实现滚动轴承的故障识别目的;利用滚动轴承数据集进行了实验,并将其与其他的故障特征提取指标进行了对比。研究结果表明:IHSloE方法采用改进的层次化处理,能够快速有效地提取出振动信号中的高频故障特征,诊断准确率达到了99%,而特征提取时间仅为149.35 s;相较于采用粗粒化处理和层次处理的特征提取方法,其准确率至少提高了2%和1%,证明该方法适用于滚动轴承的故障诊断。

Exponential time-frequency distribution of mechanical vibration signals

An improved exponential time-frequency distribution is presented. Compared with those exponential time-frequency distributions represented by the Choi-Williams distribution, this distribution is designed to suit the properties of mechanical vibration signals, and is easier to be applied in the mechanical vibration signal processing. It has a strong cross-term suppression ability and is aliasing free.

柴油机多源冲击振动信号稀疏表示及其故障诊断应用

柴油机在船舶、核电、车辆等领域应用广泛,对其进行监测与故障诊断具有重要意义。随着设备健康监测技术的发展,数据存储压力日益显著,信号稀疏表示成为一种有效的解决措施。针对柴油机振动信号具有强冲击、非平稳的特点,提出一种基于分解信号(Decomposed Signal,DS)字典的柴油机多源冲击信号稀疏表示方法,并以稀疏系数作为特征应用于柴油机气门间隙异常故障诊断。首先,采用变分时域分解(Variational Time-domain Decomposition,VTDD)对信号进行处理获得分解信号。然后,将分解信号组成DS字典。接着,通过正交匹配追踪(Orthogonal Matching Pursuit,OMP)算法实现原信号和分解冲击信号的稀疏表示。最后,以稀疏系数作为特征进行柴油机气门间隙异常故障诊断。测试结果表明,所提方法具有较好的应用效果,故障诊断准确率高于90%。

旋转机械故障诊断中的振动信号模型综述

对旋转机械振动信号进行信号处理,能够有效提取特征进行故障诊断。振动信号模型来自旋转机械运动学和动力学机理,以数学形式表达,可以指导信号处理方法的设计。随着故障机理研究和信号处理方法研究的推进,研究人员对信号模型进行了发展,并基于这些信号模型设计了相应的信号处理方法。首先,介绍了一般化的信号模型,包括周期信号模型、循环平稳信号模型、自适应谐波模型、波形函数模型、任意阶谐波模型等,以及对应的信号处理方法;其次,分别介绍定工况和变工况条件下针对轴承和齿轮的典型振动信号模型及对应信号处理方法;最后,对振动信号模型的研究发展进行总结和展望,旨在回顾旋转机械故障诊断所涉及的信号模型,并说明其在信号处理算法设计和故障诊断特征提取中的价值和意义。

基于改进MFCC算法的风力机叶片故障诊断方法

针对传统声信号特征处理方法无法有效提取叶片声音特征、导致叶片故障诊断准确率低的问题,提出一种基于改进梅尔频率倒谱系数(MFCC)算法的风力机叶片故障诊断方法。首先采用快速傅里叶变换(FFT)分析不同风速下叶片声音信号和风噪的频率特性,明确叶片声音信号的频率分布区域,将全频段分为三部分;然后采用粒子群优化算法(PSO)对梅尔(Mel)函数在不同频段上的敏感度进行优化,在迭代过程中将MFCC算法提取的叶片声音特征进行聚类,以轮廓系数作为适应度函数;最后基于支持向量机(SVM)构建分类器,实现风力机叶片故障的准确识别。以华北某风电场的叶片声音采集数据为算例,考察该算法在不同风速工况下的适应性,验证该方法的有效性。