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在齿轮噪源存在的变转速滚动轴承故障诊断过程中,因混合信号中转频分量相对较小,使得基于时频表达的阶比跟踪技术受到限制。虽然基于故障特征频率的角域重采样能提取轴承的故障特征,但这种算法不能确定故障位置,而且可能会出现误判。针对这一问题,提出了基于角域自回归(auto regressive,简称AR)模型滤波的处理方法。该方法利用线调频小波路径追踪算法从降采样处理的混合信号中提取齿轮瞬时啮合频率趋势线并估计转速,根据估计转速信息对原混合信号进行等角度重采样,获得了角域信号。利用角域信号中齿轮啮合振动成分具有周期性的特点,使用AR模型对其滤波,并且对滤波后信号进行包络阶比分析,完成故障判断。通过处理仿真信号和实验信号,验证了该方法不仅能有效地去除齿轮噪声,并且可以判断轴承故障位置。 相似文献
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Considerable studies have been carried out on fault diagnosis of gears, with most of them concentrated on conventional vibration analysis. However, besides the complexity of gear dynamics, the diagnosis results in terms of vibration signal are easily misjudged owing to the interference of sensor position or other components. In this paper, an alternative gearbox fault detection method based on the instantaneous rotational speed is proposed because of its advantages over vibration analysis. Depending on the timer/counter-based method for the pulse signal of the optical encoder, the varying rotational speed can be obtained e ectively. Owing to the coupling and meshing of gears in transmission, the excitations are the same for the instantaneous rotational speed of the input and output shafts. Thus, the di erential signal of instantaneous rotational speeds can be adopted to eliminate the e ect of the interference excitations and extract the associated feature of the localized fault e ectively. With the experiments on multistage gearbox test system, the di erential signal of instantaneous speeds is compared with other signals. It is proved that localized faults in the gearbox generate small angular speed fluctuations, which are measurable with an optical encoder. Using the di erential signal of instantaneous speeds, the fault characteristics are extracted in the spectrum where the deterministic frequency component and its harmonics corresponding to crack fault characteristics are displayed clearly. 相似文献
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针对变速下齿轮裂纹故障信号微弱,难以提取这一特点,提出了基于线调频小波路径追踪的阶比能量解调算法,并将其应用于变速下的齿轮裂纹故障诊断。该方法先采用线调频小波路径追踪算法提取齿轮的啮合频率分量,由此得到转速信号;然后利用转速信号对原始信号进行等角度采样得到角域平稳信号;接着对角域平稳信号进行带通滤波和角域平均运算以消除干扰噪声的影响;最后使用能量算子解调求取瞬时频率和瞬时幅值,根据瞬时频率和瞬时幅值进行故障诊断。应用实例表明,该方法能有效地提取变速下的齿轮裂纹故障。 相似文献
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针对齿轮传动系统中齿轮等零部件易出现故障或失效等问题,提出了一种基于深度学习理论的齿轮传动系统故障诊断方法。首先利用深度置信网络强大的特征自提取能力,对齿轮传动系统的振动信号进行特征提取,然后通过DBNs的复杂映射表征能力对故障信号进行故障判别。诊断实例表明,若不对齿轮振动的原始时域信号进行特征提取,直接利用DBNs对其进行诊断时,故障识别正确率只能达到 60%左右;如果对时域信号进行简单的傅里叶变换后,再利用 DBNs 对处理后的振动信号频谱进行诊断分析,正确率能达到 99.7%,从而证明了所提故障诊断方法的简易性和有效性。 相似文献
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针对齿轮故障特征微弱,在强背景噪声下难以有效提取的问题,提出了一种改进奇异谱分解(ISSD)结合奇异值分解(SVD)的齿轮故障特征提取方法。针对奇异谱分解(SSD)算法中模态参数需凭经验选取的缺陷,基于散布熵优化算法对SSD算法进行了改进,在得到既定的一组奇异谱分量的基础上,根据峭度值最大准则筛选出了最佳奇异谱分量并进行了SVD处理,采用奇异值能量标准谱自适应地确定了信号重构阶数以还原信号和提高降噪效果。最后对信号进行包络解调以提取齿轮故障特征,将所提方法运用到仿真信号和齿轮实测信号中,并同传统包络谱、SSD包络谱以及经验模态分解结合SVD(EMD-SVD)方法进行了对比分析,结果表明,所提方法的降噪和特征提取效果更佳,能够更加有效地实现齿轮故障的判别。 相似文献
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基追踪降噪及在齿轮故障诊断中的应用 总被引:2,自引:0,他引:2
从信号分析的角度,引入基追踪降噪(Basis Pursuit De-N0ising)的基本原理。通过对含有白噪声的典型脉冲信号进行降噪处理.展示基追踪降噪在脉冲信号特征提取中的优点。最后对变速箱齿轮故障信号进行降噪处理。结果表明,该方法可以有效去除强噪声干扰,提取脉冲故障特征信号。通过实例证明了该方法的有效性和工程实用性。 相似文献
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《Measurement》2014
Filtering techniques are used to improve the signal to noise ratio (SNR) for better feature extraction. The time synchronous averaging (TSA) is one of such method that is based on averaging periodic sections. However, it fails to give significant results for an asynchronous or fluctuating speed condition. Moreover, most of the real life applications of gear are in asynchronous conditions. The aim of this paper is to develop a methodology which is robust for fault detection of gears under fluctuating load and speed conditions. A multiple-pulse individually rescaled-time synchronous averaging (MIR-TSA) technique in conjunction with conventional time synchronous averaging has been proposed. A 2-D finite element methodology based on principal or linear elastic fracture mechanics is adopted for predicting the crack propagation path at the root of gear tooth. The crack has been introduced using wire electrode discharge machining (WEDM). The vibration signals were recorded using drivetrain dynamic simulator (DDS) setup for various combination of load and crack length both for constant as well as fluctuating speed. Various time domain features such as root mean square, crest factor and kurtosis have been calculated using classical TSA and proposed MIR-TSA. A comparison of different extracted features between the proposed method and classic TSA has also been outlined. It has been observed that the proposed method enhances the fault detection under fluctuating speed conditions. 相似文献
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基于扭振信号的齿轮故障诊断研究 总被引:3,自引:0,他引:3
在分析了齿轮啮合扭振的基础上提出了利用轴系扭振信号进行齿轮诊断的方法,度验结果表明扭振信号受环境噪声影响小,对故障敏感,作为齿轮早期故障诊断信息来源的扭振信号优于传统的箱体振动信号,还介绍了一种适用于状态监测的扭振测试方法。 相似文献
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基于线调频小波路径追踪阶比循环平稳解调的齿轮故障诊断 总被引:5,自引:1,他引:5
为从变转速齿轮箱振动信号中提取齿轮故障特征,提出基于线调频小波路径追踪的阶比循环平稳解调方法。该方法利用线调频小波路径追踪算法估计振动信号中的转速信号,根据转速信号对信号进行等角度采样,获取角域周期平稳信号,求取角域信号的循环自相关函数,在特征循环阶比处对循环自相关函数进行切片,并对切片进行解调分析得到切片解调谱,依据切片解调谱进行齿轮故障诊断。由于线调频小波路径追踪算法具有精度高和抗噪能力强的优点,而循环平稳解调算法可以有效提取淹没在噪声中的周期性故障特征,因而,该方法结合了二者的优点,适合于变转速齿轮信号的故障特征提取。算法仿真和应用实例表明,该方法能有效地提取变转速齿轮箱振动信号中的齿轮故障特征。 相似文献
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提出一种基于自适应线调频基原子分解(adaptive chirplet atomic decomposition,ACAD)的时域同步平均方法,并将其应用于低信噪比下变转速齿轮故障诊断。首先对齿轮振动信号进行ACAD分解估计齿轮所在轴的转速曲线;然后根据转速曲线对信号进行等角度重采样,以满足时域同步平均方法对信号周期平稳的要求;再利用时域同步平均方法对重采样信号进行处理,处理后的信号具有很高的信噪比;最后,对其进行FFT变换,其阶次谱上非常清晰地显示齿轮的调制阶次,从而揭示齿轮的故障信息。仿真算例与应用实例证明了该方法的有效性。 相似文献
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当齿轮出现断齿、裂纹等局部故障时,其振动信号会出现周期性冲击脉冲。在齿轮故障早期,由于冲击脉冲微弱,常淹没在齿轮的啮合频率、转频等谐波成分以及噪声中,因此,对于齿轮早期故障,直接对齿轮振动信号做包络谱分析以诊断齿轮局部故障通常效果不佳。针对这一问题,将信号共振稀疏分解方法与包络谱分析相结合,提出了基于信号共振稀疏分解与包络谱的齿轮故障诊断方法。该方法采用信号共振稀疏分解将冲击脉冲从齿轮振动信号中分离出来,然后对冲击脉冲做Hilbert包络分析,获取冲击脉冲出现的周期,进而对齿轮状态和故障进行识别。仿真算例和应用实例证明了该方法的有效性。 相似文献
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Vibration signals measured from a gearbox are complex multi-component signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures and a substantial amount of noise. This article presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS) algorithm is developed and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio); which make meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a pair of healthy gears and one pair suffering from a tooth breakage with severity fault 1 (25% tooth removal), and fault 2 (50% tooth removal) under loads (0%, and 80% of the total load). The experimental results show remarkable improvements and enhance gear fault features. This paper illustrates that the new approach offers a more effective way to detect early faults. 相似文献
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Xihui Chen Gang Cheng Hongyu Li Yong Li 《Journal of Mechanical Science and Technology》2017,31(3):1035-1047
Planetary gear is widely used in large-scale complex mechanical systems. However, because of the particularity of planetary gear transmission, serious wear and fatigue crack failures often occur in the sun gear, planet gears, and inner gear ring. In addition, every type of fault will experience different degradation processes. Improving the operation reliability of mechanical equipment through fault diagnosis of planetary gears and monitoring their degradation process is beneficial. This paper proposes a planetary gear fault identification method based on Dual-tree Complex wavelet transform (DT-CWT) threshold denoising and Laplacian eigenmaps (LE). The noise reduction processing of the original signal is achieved by the DT-CWT threshold denoising method, which takes full advantage of DT-CWT and is combined with the wavelet threshold of rigrsure principle. The original high-dimensional feature set, including the time domain features, frequency domain features, permutation entropy, and fractal box dimension of the denoised signal, is constructed from multi-angles. To solve the problems of excessively large feature dimension and the existence of redundant information, the LE algorithm is used to reduce the dimension of the original high-dimensional feature set, and the low-dimensional sensitive features are obtained. Through the above method, the effective identification of different fault states and different degradation states of the planetary gear are achieved. 相似文献
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为了分析基于齿背接触刚度的高速斜齿轮瞬态振动放大特性,针对高转速瞬态工况下斜齿轮齿面啮合-脱啮-齿背接触的齿面实际承载接触状态,建立了同时考虑啮合时间与齿面振动位移耦合机理的斜齿轮动态啮合刚度。在细化考虑齿背啮合机理、基于齿背实际啮合刚度的模型基础上,进一步建立斜齿轮啮合型瞬态振动模型,并在此基础上展开不同齿侧间隙以及齿背接触对系统瞬态振动特性影响分析研究。搭建封闭功率流式斜齿轮瞬态扭转振动测试试验台,对基于齿背接触刚度的斜齿轮瞬态振动特性进行了验证。该研究具有较好的理论研究意义,有利于斜齿轮传动系统在航空传动、新能源传动系统上的应用推广,进一步提升高转速齿轮系统的瞬态振动噪声品质。 相似文献