Fault Diagnosis Of Mechanical Systems Research Articles

Testing and evaluation of water hydraulic components by acoustic emission and wavelet analysis

Abstract In condition monitoring and fault diagnosis of machinery, vibration analysis is the major method and wavelet analysis is widely implemented. The acoustic emission (AE) method is another important and effective signal in fault diagnosis of mechanical systems. In this paper, the AE signal is applied to diagnose the piston condition of the water hydraulic motor in a water hydraulic system. The AE signal is obtained under different flow rates and torques. The wavelet-based signal processing technique is used to analyze the fluid-borne vibration and structure-borne vibration (acoustic signal). The spectrum based on wavelet analysis is effective in analyzing the acoustic signals under specific frequency ranges, compared with the conventional power spectrum density (PSD). The results show root mean square (RMS) of the wavelet-based acoustic signal analysis is very effective for the fault diagnosis of piston cracks under the different flow rates and torques.

Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis

When detecting a weak and high-frequency signal submerged in strong noise, the existing large parameter stochastic resonance (LPSR) models need either a high sampling frequency or a large number of sample points. To breach the above limits and raise the usability of LPSR, a novel method named frequency-shifted and re-scaling stochastic resonance (FRSR) is proposed in this paper. By shifting and re-scaling the frequency, FRSR provides a way to alleviate the contradiction between sampling frequency and the number of sample points. The proposed method is verified with simulated signals. The results show that this method is useful in weak fault diagnosis of mechanical systems which involve high feature frequencies. Compared with the existing LPSR models, FRSR just requires a lower sampling frequency, a smaller data length and has higher efficiency. Finally, the proposed method is applied to a milling machine tool fault diagnosis and an outer ring fault feature of the spindle bearing is found successfully. Thus, FRSR is a meaningful try for SR coming into practical use of mechanical fault diagnosis.

An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network

In the present study, a fault diagnosis system is proposed for internal combustion engines using wavelet packet transform (WPT) and artificial neural network (ANN) techniques. In fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. The signal processing algorithm of the present system is gained from previous work used for speech recognition. In the preprocessing of sound emission signals, WPT coefficients are used for evaluating their entropy and treated as the features to distinguish the fault conditions. Obviously, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the wavelets are used as mother wavelets to build and perform the proposed WPT technique. In the classification, to verify the effect of the proposed generalized regression neural network (GRNN) in fault diagnosis, a conventional back-propagation network (BPN) is compared with a GRNN network. The experimental results showed the proposed system achieved an average classification accuracy of over 95% for various engine working conditions.

Asynchronous input gear damage diagnosis using time averaging and wavelet filtering

Vibration signals are often used for fault diagnosis in mechanical systems because they are containing dynamic information of mechanical elements. Vibration signals from a gearbox are usually noisy and the signal-to-noise ratio (SNR) is so low that feature extraction of signal components is very difficult, especially in practical situations. One of the solutions to this problem is applying signal time-averaging techniques in time domain for signal denoising, but using this method is only possible when gearbox input shaft rotation is constant or synchronous. In this paper, a new noise canceling method, based on time-averaging method for asynchronous input, is developed, and then complex Morlet wavelet is implemented for feature extraction and diagnosis of different kind of local gear damages. The complex Morlet wavelet, used in this work, is adaptive because the parameters are not fixed. The proposed method is implemented on a simulated signal and real test rig of Yahama motorcycle gearbox. Both simulation and experimental results have proved that the method is very promising in analysis of the signal and fault diagnosis of gearbox.

Analysis of bilinear oscillators under harmonic loading using nonlinear output frequency response functions

In this paper, the new concept of nonlinear output frequency response functions (NOFRFs) is extended to the harmonic input case, an input-independent relationship is found between the NOFRFs and the generalized frequency response functions (GFRFs). This relationship can greatly simplify the application of the NOFRFs. Then, beginning with the demonstration that a bilinear oscillator can be approximated using a polynomial-type nonlinear oscillator, the NOFRFs are used to analyse the energy transfer phenomenon of bilinear oscillators in the frequency domain. The analysis provides insight into how new frequency generation can occur using bilinear oscillators and how the sub-resonances occur for the bilinear oscillators, and reveals that it is the resonant frequencies of the NOFRFs that dominate the occurrence of this well-known nonlinear behaviour. The results are of significance for the design and fault diagnosis of mechanical systems and structures which can be described by a bilinear oscillator model.

Resonances and resonant frequencies for a class of nonlinear systems

Resonant phenomena for a class of nonlinear systems, which can be described by a single-degree-of-freedom (sdof) model with a polynomial-type nonlinear stiffness, are investigated using nonlinear output frequency response functions (NOFRFs). The concepts of resonance and resonant frequencies are proposed for this class of nonlinear systems using the NOFRF concept, and the effects of damping on the resonances and resonant frequencies are analyzed. These results produce a novel interpretation of energy transfer phenomena in this class of nonlinear systems and show how the damping effect influences the system resonant frequencies and amplitudes. The results are important for the design and fault diagnosis of mechanical systems and structures which can be described by the sdof nonlinear model.