Effect Of Transmission Path Research Articles

Mathematic modeling of vibration transmission path with discontinuous contact interfaces for gear dynamics in the gear-shaft-bearing-housing system

A twelve-DOF model of gear-shaft-bearing-housing system is developed to study the vibration transmission path characteristics under different internal excitations considering effect of discontinuous contact interfaces. Gear meshing generates vibrations to propagate through the transmission path consisting of gear, shaft, bearing, housing and multiple contact interfaces to reach the external transducer. Effect of contact interfaces between different transmitting components, including that between gear and shaft, inner race and outer race, outer race and housing are characterized using parallel contact stiffness and damping elements. Dynamic responses of transmitting components are analyzed for three internal excitations of single frequency sinusoidal force, dynamic meshing force of both normal and spalled gear tooth. An explicit mathematic model of transmission path function is constructed, which considers effect of transmission path on both amplitude attenuation and frequency structure shift for gear with/without fault. The transmission path function is verified with model predictions, finite element simulation and experiments.

Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution

Rolling bearings are the key components in the modern machinery, and tough operation environments often make them prone to failure. However, due to the influence of the transmission path and background noise, the useful feature information relevant to the bearing fault contained in the vibration signals is weak, which makes it difficult to identify the fault symptom of rolling bearings in time. Therefore, the paper proposes a novel weak signal detection method based on time-delayed feedback monostable stochastic resonance (TFMSR) system and adaptive minimum entropy deconvolution (MED) to realize the fault diagnosis of rolling bearings. The MED method is employed to preprocess the vibration signals, which can deconvolve the effect of transmission path and clarify the defect-induced impulses. And a modified power spectrum kurtosis (MPSK) index is constructed to realize the adaptive selection of filter length in the MED algorithm. By introducing the time-delayed feedback item in to an over-damped monostable system, the TFMSR method can effectively utilize the historical information of input signal to enhance the periodicity of SR output, which is beneficial to the detection of periodic signal. Furthermore, the influence of time delay and feedback intensity on the SR phenomenon is analyzed, and by selecting appropriate time delay, feedback intensity and re-scaling ratio with genetic algorithm, the SR can be produced to realize the resonance detection of weak signal. The combination of the adaptive MED (AMED) method and TFMSR method is conducive to extracting the feature information from strong background noise and realizing the fault diagnosis of rolling bearings. Finally, some experiments and engineering application are performed to evaluate the effectiveness of the proposed AMED-TFMSR method in comparison with a traditional bistable SR method.

A NEW GEARBOX FAULT DIAGNOSIS METHOD BASED ON LUCY–RICHARDSON DECONVOLUTION

Gear and bearing faults are the main causes of gearbox failure. Till now, incipient fault diagnosis of these two components has been a problem and needs further research. In this context, it is found that Lucy–Richardson deconvolution (LRD) proved to be an excellent tool to enhance fault diagnosis in rolling element bearings and gears. LRD’s good identification capabilities of fault frequencies are presented which outperform envelope analysis. This is very critical for early fault diagnosis. The case studies were carried out to evaluate the effectiveness of the proposed method. The results of simulated and experimental studies show that LRD is efficient in alleviating the negative effect of noise and transmission path. The results of simulation and experimental tests demonstrated outperformance of LRD compared to classical envelope analysis for fault diagnosis in rolling element bearings and gears, especially when it is applied to the processing of signals with strong background noise.

Fault diagnosis of rolling bearing’s early weak fault based on minimum entropy de-convolution and fast Kurtogram algorithm

The rolling bearing’s early stage fault feature is very weak for reasons of the signal attenuation phenomenon between the fault source and the sensor collecting the fault signal and the interference of environment noise such as the rotor rotating frequency and its harmonics and so on. The feature extraction of rolling bearing’s early weak fault is not only very important but also very hard. The minimum entropy de-convolution and Fast Kurtogram algorithm are combined in the paper for rolling bearing’s early stage weak fault feature extraction. The effect of transmission path is de-convolved effectively, and the impulses are clarified using minimum entropy de-convolution technique firstly. Then the obtained signal by minimum entropy de-convolution is handled by the Fast Kurtogram algorithm and an optimal filter is established. At last the envelope de-modulation is applied on the filtered signal and better feature extraction result is obtained compared with the other methods such as wavelet transform, frequency slice wavelet transformation and ensemble empirical mode decomposition. The effectiveness and advantages of the proposed method are verified through simulation signal and experiment.

Vibration signal modeling of a planetary gear set for tooth crack detection

In a planetary gearbox, there are multiple vibration sources, and the transmission path of vibration signals changes due to the rotation of the carrier. This study aims to model the vibration signals of a planetary gearbox and investigate the vibration properties in the healthy condition and in the cracked tooth condition. A dynamic model is developed to simulate the vibration source signals. A modified Hamming function is proposed to represent the effect of the transmission path. By incorporating the effect of multiple vibration sources and the effect of transmission path, resultant vibration signals of a planetary gearbox are obtained. Through analyzing the resultant vibration signals, some vibration properties of a planetary gearbox are revealed and the fault symptoms of sun gear tooth crack are identified and located. Finally, the proposed approach is experimentally verified.

A normalized Hilbert-Huang transform technique for bearing fault detection

Bearings are commonly used in rotary machinery, whereas up to half of machinery malfunctions could be related to bearing defects. Unfortunately reliable fault detection systems still remain a challenging task, especially when bearing defect-related features are nonstationary. A new normalized Hilbert-Huang transform (NHHT) technique is proposed in this paper for vibration-based bearing fault detection. The NHHT for bearing fault detection takes two processes: firstly the vibration signal is denoised to highlight defect-related impulses; and secondly representative features are extracted for bearing fault detection. Vibration signal denoising is carried out by the use of the maximum kurtosis deconvolution filter to reduce impedance effect of transmission path of the measured vibration signal. A novel strategy based on D’Agostino-Person normality is suggested to enhance the distinctive intrinsic mode functions for representative features extraction and formulation for bearing fault detection. The effectiveness of the proposed NHHT technique is verified by a series of experimental tests corresponding to different bearing health conditions, and its robustness in bearing fault detection is examined by the use of data sets from a different experimental setup.

An energy kurtosis demodulation technique for signal denoising and bearing fault detection

Rolling element bearings are commonly used in rotary machinery. Reliable bearing fault detection techniques are very useful in industries for predictive maintenance operations. Bearing fault detection still remains a very challenging task especially when defects occur on rotating bearing components because the fault-related features are non-stationary in nature. In this work, an energy kurtosis demodulation (EKD) technique is proposed for bearing fault detection especially for non-stationary signature analysis. The proposed EKD technique firstly denoises the signal by using a maximum kurtosis deconvolution filter to counteract the effect of signal transmission path so as to highlight defect-associated impulses. Next, the denoised signal is modulated over several frequency bands; a novel signature integration strategy is proposed to enhance feature characteristics. The effectiveness of the proposed EKD fault detection technique is verified by a series of experimental tests corresponding to different bearing conditions.

Effect of Transmission Path on Measured Gear Vibration

The transmission of gear tooth meshing vibration from the tooth contact region to the shaft, through rolling element bearings and then through the gearbox casing can have an important influence on the measured vibration sepctrum. In critical applications the measurement of the torsional vibration of the gear may be preferable.