Research on Instantaneous Angular Speed Signal Separation Method for Planetary Gear Fault Diagnosis

Planetary gear train is a critical transmission component in large equipment such as helicopters and wind turbines. Conducting damage perception of planetary gear trains is of great significance for the safe operation of equipment. Existing methods for damage perception of planetary gear trains mainly rely on linear vibration analysis. However, these methods based on linear vibration signal analysis face challenges such as rich vibration sources, complex signal coupling and modulation mechanisms, significant influence of transmission paths, and difficulties in separating damage information. This paper proposes a method for separating instantaneous angular speed (IAS) signals for planetary gear fault diagnosis. Firstly, this method obtains encoder pulse signals through a built-in encoder. Based on this, it calculates the IAS signals using the Hilbert transform, and obtains the time-domain synchronous average signal of the IAS of the planetary gear through time-domain synchronous averaging technology, thus realizing the fault diagnosis of the planetary gear train. Experimental results validate the effectiveness of the calculated IAS signals, demonstrating that the time-domain synchronous averaging technology can highlight impact characteristics, effectively separate and extract fault impacts, greatly reduce the testing cost of experiments, and provide an effective tool for the fault diagnosis of planetary gear trains.

Analysis and Simulation for Planetary Gear Fault of Helicopter Based on Vibration Signal

Fault diagnosis for helicopter's main gearbox based on vibration signals by experiments always requires high costs. To solve this problem,a helicopter's planetary gear system is taken as an example. Firstly,a simulation model is established by McFadden,and analyzed under ideal condition. Then this model is developed and improved as the delay-time model of the vibration signal which determines the phase-change of sidebands when the system is running. The cause and change-rules of planetary gear system's vibration signal are analyzed to establish the fault diagnosis model.At the same time,the vibration signal of fault condition is simulated and analyzed. This simulation method can provide a reference for fault monitoring and diagnosis for planetary gear system.

Gearbox Fault Diagnosis using Adaptive Zero Phase Time-varying Filter Based on Multi-scale Chirplet Sparse Signal Decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

Measuring the Qatar-Kazeron Fault Dip Using Random Finite Fault Simulation of September 27, 2010 Kazeron Earthquake and Analytical Signal Map of Satellite Magnetic Data

In this research the fault parameters causing the September 27, 2010 Kazeron Earthquake with a magnitude of MW = 5.8 (BHRC) were determined using the random finite fault method. The parameters were recorded by 27 accelerometer stations. Simulation of strong ground motion is very useful for areas about which little information and data are available. Considering the distribution of earthquake records and the existing relationships, for the fault plane causing the September 27, 2010 Kazeron Earthquake the length of the fault along the strike direction and the width of the fault along the dip direction were determined to be 10 km and 7 km, respectively. Moreover, 10 elements were assumed along the length and 7 were assumed along the width of the plane. Research results indicated that the epicenter of the earthquake had a geographic coordination of 29.88N - 51.77E, which complied with the results reported by the Institute of Geophysics Tehran University (IGTU). In addition, the strike and dip measured for the fault causing the Kazeron Earthquake were 27 and 50 degrees, respectively. Therefore, the causing fault was almost parallel to and coincident with the fault. There are magnetic discontinuities on the analytical signal map with a north-south strike followed by a northwest-southeast strike. The discontinuities are consistent with the trend of Kazeron fault but are several kilometers away from it. Therefore, they show the fault depth at a distance of 12 km from the fault surface.

Assessment of the Relationship between ESR Signal Intensity and Grain Size Distribution in Shear Zones within the Atotsugawa Fault System, Central Japan

For the first time, a relationship between ESR signal intensity and grain size distribution (sieve technique) in shear zones within the Atotsugawa fault system have been investigated using fault core rocks. The grain size distributions were estimated using the sieve technique and microscopic observations. Stacks of sieves with openings that decrease consecutively in the order of 4.75 mm, 1.18 mm, 600 μm, 300 μm, 150 μm and 75 μm were chosen for this study. Grain size distributions analysis revealed that samples further from the slip plane have larger d50 (average gain size) (0.45 mm at a distance of 30 - 50 mm from the slip plane) while those close to the slip plane have smaller d50 values (0.19 mm at a distance of 0 - 10 mm from the slip plane). This is due to intensive crushing that is always associated with large displacement during fault activities. However, this pattern was not respected in all shear zones in that, larger d50 values were instead observed in samples close to the slip plane due to admixture of fault rocks from different fault activities. Results from ESR analysis revealed that the relatively finer samples close to the slip plane have low ESR signals intensity while those further away (coarser) have relatively higher signal intensity. This tendency however, is not consistence in some of the shear zones due to a complex network of anatomizing faults. The variation in grain size distribution within some of the shear zones implies that, a series of fault events have taken place in the past thus underscoring the need for further investigation of the possibility of reoccurrence of faults.

Analogue and Mixed-Signal Production Test Speed-Up by Means of Fault List Compression

Accurate test effectiveness estimation for analogue and mixed-signal Systems on a Chip (SoCs) is currently prohibitive in the design environment. One of the factors that sky rockets fault simulation costs is the number of structural faults which need to be simulated at circuit-level. The purpose of this paper is to propose a novel fault list compression technique by defining a stratified fault list, build with a set of “representative” faults, one per stratum. Criteria to partition the fault list in strata, and to identify representative faults are presented and discussed. A fault representativeness metric is proposed, based on an error probability. The proposed methodology allows different tradeoffs between fault list compression and fault representation accuracy. These tradeoffs may be optimized for each test preparation phase. The fault representativeness vs. fault list compression tradeoff is evaluated with an industrial case study—a DC-DC (switched buck converter). Although the methodology is presented in this paper using a very simple fault model, it may be easily extended to be used with more elaborate fault models. The proposed technique is a significant contribution to make mixed-signal fault simulation cost-effective as part of the production test preparation.

Intelligent Diagnosis of Short Hydraulic Signal Based on Improved EEMD and SVM with Few Low-dimensional Training Samples

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD（empirical mode decomposition） and EEMD（ensemble empirical mode decomposition） are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM（support vector machine）, extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD＋SVM method is compared with the EEMD＋SVM and EMD＋SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

IMPROVED SINGULAR VALUE DECOMPOSITION TECHNIQUE FOR DETECTING AND EXTRACTING PERIODIC IMPULSE COMPONENT IN A VIBRATION SIGNAL

Vibration acceleration signals are often measured from case surface of arunning machine to monitor its condition. If the measured vibration signals display to have periodicimpulse components with a certain frequency, there may exist a corresponding local fault in themachine, and if further extracting the periodic impulse components from the vibration signals, theseverity of the local fault can be estimated and tracked. However, the signal-to-noise ratios (SNRs)of the vibration acceleration signals are often so small that the periodic impulse components aresubmersed in much background noises and other components, and it is difficult or inconvenient for usto detect and extract the periodic impulse components with the current common analyzing methods forvibration signals. Therefore, another technique, called singular value decomposition (SVD), istried to be introduced to solve the problem. First, the principle of detecting and extracting thesignal periodic components using singular value decomposition is summarized and discussed. Second,the infeasibility of the direct use of the existing SVD based detecting and extracting approach ispointed out. Third, the approach to construct the matrix for SVD from the signal series is improvedlargely, which is the key program to improve the SVD technique; Other associated improvement is alsoproposed. Finally, a simulating application example and a real-life application example ondetecting and extracting the periodic impulse components are given, which showed that the introducedand improved SVD technique is feasible.

Frequency Loss and Recovery in Rolling Bearing Fault Detection

Rolling element bearings are key components of mechanical equipment. The bearing fault characteristics are a ected by the interaction in the vibration signals. The low harmonics of the bearing characteristic frequencies cannot be usually observed in the Fourier spectrum. The frequency loss in the bearing vibration signal is presented through two independent experiments in this paper. The existence of frequency loss phenomenon in the low frequencies, side band frequencies and resonant frequencies and revealed. It is demonstrated that the lost frequencies are actually suppressed by the internal action in the bearing fault signal rather than the external interference. The amplitude and distribution of the spectrum are changed due to the interaction of the bearing fault signal. The interaction mechanism of bearing fault signal is revealed through theoretical and practical analysis. Based on mathematical morphology, a new method is provided to recover the lost frequencies. The multi-resonant response signal of the defective bearing are decomposed into low frequency and high frequency response, and the lost frequencies are recovered by the combination morphological filter(CMF). The e ectiveness of the proposed method is validated on simulated and experimental data.

Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

The performance of traditional vibration based fault diagnosis methods greatly depends on those hand- crafted features extracted using signal processing algo- rithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised represen- tation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal struc- tures, this paper proposes a multiscale representation learning （MSRL） framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at dif- ferent scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multi- scale representations. Finally, the multiscale representa- tions are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies： motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

Wavelet transform and its applicationto control system fault detection

Wavelet analysis theory is a new theory developed in recent years, it is a new timefrequency localization method. As its analyzing precision can be changed and focused to anydetail of the analyzed signal., it is very useful to study unstationary signals. In this paper wemainly study the wavelet theory a,of its application in control systems. Furthermore, we use it todetect the fault of an underwater vehicle 's direction angle, and attained excellent results from thesimulation.

drive signal在阀门智能定位器故障诊断中的应用

论文介绍了Fisher厂家DVC6000系列智能定位器drive signal信号的原理以及其在现场阀门故障诊断中的应用。

A Method for Gear Fault Diagnosis Based on the Empirical Mode Decomposition

According to the characteristics of gear fault vibration signals, a methodfor gear fault diagnosis based upon the empirical mode decomposition (EMD) is proposed in thispaper. By using EMD, any complicated signal can be decomposed into a finite and often small numberof intrinsic mode functions (IMFs) , which are based upon the local characteristic time scale of thesignal. Thus, EMD is perfectly suitable for non-stationary signal processing and faultcharacteristics extracting. It is well known that a gear vibration signal consists of a number offrequency family components, each of which is a modulated signal. Thus, we can use EMD to decomposea gear fault vibration signal into a number of IMF components, some of which correspond to thefrequency families, and the others are noises. Therefore, the frequency families can be separatedand the noise can be decreased at the same time. The proposed method has been applied to gear faultdiagnosis. The results show that both the sensitivity and the reliability of this method aresatisfactory.

PERFORMANCE ANALYSIS OF SECOND-ORDER STATISTICS FOR CYCLOSTATIONARY SIGNALS

The second order statistics for cyclostationary signals were introduced, and their performance were discussed. It especially researched the time lag characteristic of the cyclic autocorrelation function and spectral correlation characteristic of spectral correlation density function. It was pointed out that those functions can be available to extract the time vary information of the kind of non stationary signals. Using the relations of time lag cyclic frequency and frequency cyclic frequency independently, vibration signals of a rolling element bearing measured on test bed were analyzed. The results indicate that the second order cyclostationary statistics might provide a powerful tool for the feature extracting and fault diagnosis of rolling element bearing.

Fault Detection for PMSM Motor Drive Systems by Monitoring Inverter Input Currents

This paper has proposed a fault detecting method for DC supplied permanent magnet synchronize motor(PMSM)drive systems by monitoring the drive DC input current.This method is based on the fault signal propagation from the torque disturbance on the motor shaft to the inverter input currents.The accuracy of this fault signal propagation is verified by the Matlab simulation and experiment tests with the emulated faulty conditions.The feasible of this approach is shown by the experimental test conducted by the Spectra test rig with the real gearbox fault.This detection scheme is also suitable for monitoring other drive components such as the power converter or the motor itself using only one set of current transducers mounted at the DC input side.

Fault Feature Extraction of Diesel Engine Based on Bispectrum Image Fractal Dimension

Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early fault signals are mostly weak energy signals, and time domain or frequency domain features will be overwhelmed by strong back?ground noise. In order consistent features to be extracted that accurately represent the state of the engine, bispectrum estimation is used to analyze the nonlinearity, non?Gaussianity and quadratic phase coupling(QPC) information of the engine vibration signals under different conditions. Digital image processing and fractal theory is used to extract the fractal features of the bispectrum pictures. The outcomes demonstrate that the diesel engine vibration signal bispectrum under different working conditions shows an obvious differences and the most complicated bispectrum is in the normal state. The fractal dimension of various invalid signs is novel and diverse fractal parameters were utilized to separate and characterize them. The value of the fractal dimension is consistent with the non?Gaussian intensity of the signal, so it can be used as an eigenvalue of fault diagnosis, and also be used as a non?Gaussian signal strength indicator. Consequently, a symptomatic approach in view of the hypothetical outcome is inferred and checked by the examination of vibration signals from the diesel motor. The proposed research provides the basis for on?line monitoring and diagnosis of valve train faults.

基于表面辐射声信号的柴油机进气及齿轮故障诊断

利用声振信号进行发动机故障诊断过程中,部分故障激励仅在发动机表面特定位置的振动中有较强响应,振动测点要求高,需要接触测量,部分场景难以实现.为此,提出了一种以表面辐射声为媒介、以自适应变分模态提取(adaptive variational mode extraction,AVME)进行预处理的柴油机进气故障和齿轮故障诊断方法.开展了某直列六缸重型柴油机的进气滤清器堵塞、气门间隙异常和正时齿轮损伤3类故障状态的台架试验,获取了不同故障程度下发动机表面辐射噪声.基于改进的AVME方法,实现噪声信号本征模函数(intrinsic mode function,IMF)的最优分解,通过计算IMF与原信号间的互相关系数,提取高相关IMF构成故障诊断输入.经预处理后,声信号故障特征得到有效增强,再输入到麻雀搜索算法优化支持向量机模型(support vector machine model optimized by sparrow search algorithm,SSA-SVM),进行特征参量和模型参数协同优化可以获得更好的诊断精度.试验验证表明,无需在半消声室测试,仅使用单通道声信号对3类11种程度的进气系统和齿轮故障进行诊断,前端噪声准确率最高(98.89%),顶部噪声准确率最低(88.78%);使用前、顶、后三通道噪声数据后,诊断精度可提升至99.57%.研究结论为基于声信号等非接触测量的发动机故障诊断提供了参考.

充电桩充电模块功率器件故障诊断研究综述

大功率直流充电桩是未来电动汽车充电设施的发展方向,充电模块是直流充电桩最重要以及故障率最高的部件,其中功率器件开路故障较为常见。为保证充电模块安全可靠运行,需要对充电模块功率器件开路故障进行准确识别和定位。文中首先对充电模块拓扑结构和故障类型进行分析,然后分别对基于解析模型、基于信号处理和基于知识的功率器件开路故障诊断方法进行总结,分别介绍了各类方法的基本思想、研究进展和优缺点,最后总结并展望充电模块功率器件开路故障诊断方法未来的研究和发展方向。

复合故障下风电齿轮箱声音信号耦合调制模型辨识与故障诊断

针对两级行星一级平行齿轮箱不同位置受损导致的复合故障,提出一种声音信号耦合调制模型,以辅助专家进行故障诊断。当风电齿轮箱发生复合故障时,其特征频率会以调幅和调频的形式影响不同轮系的啮合频率,为此,本文提出了复合故障下风电齿轮箱声音信号幅值耦合调制模型;利用模型参数辨识思路,确定所提耦合调制模型中不同轮系的调幅系数,并通过构建边带能量比指标,用于评价辨识效果;最后,利用声音信号耦合调制模型的重构谱,确定复合故障位置,实现具有辅助性质的故障诊断。实验与现场数据分析表明:用于评价辨识结果的边带能量比指标分别为0.948,0.972,0.977和0.9643,有效说明了模型辨识的有效性,为齿轮箱复合故障自动诊断奠定了基础。