Applications Of Some New Methods In Mechine Fault Surveillance And Diagnosis

Vibration signals are often non-stationay due to the complexity of the mechanical systems, especially when the faults occur. Extracting features from those non-tationary signals is the key to successfully conducting machine condition monitoring and fault diagnosis. Time-frequency techniques have shown their advangtages for the analysis of non-stationary signals, however sometimes they can not provide comprehensive and accurate information because of their limations. The objective of this dissertation is to develop novel and effective time-frequency methods for fault detection and diagnosis.Ultrasonic detection is essential for the ultrasonic-based applications. Rather than focusing on a particular application area, we attempt to provide a general methodology for ultrasonic detection. Based on the smoothed pseudo-Wigner-Ville distribution, continuous wavelet transform and Hilbert-Huang transform, three extended time-frequency domain average (ETFDA) techniques are proposed in this dissertation. These techniques extend the time-domain average (TDA) and combine the localizing characteristics of time-frequency analysis with the abilities of the TDA to suppress noise interference. They are suitable to detect the ultrasonic even when the received signals are smeared by the noise or distorted in the medium. Numerical investigation on the performance of the ETFDA is carried out. A number of tests conducted on simulated and actual signals have demonstrated that ETFDA possesses satisfactory performances.Demodulation is an available method for fault diagnosis. Local mean decomposition (LMD) is a new adaptive signal decomposition technique, and a demodulation technique based on LMD is developed. A method of boundary processing and a strategy for determining the step size of moving average are presented to improve the algorithm of LMD. Instantaneous amplitude (IA) and instantaneous frequency (IF) of the signal can be computed independently of Hilbert transform using LMD method. A well-constructed description of the derived IA and IF is given in the form of instantaneous time-frequency spectrum (ITFS), which preserves the time and frequency information simultaneously. Results of three synthetic signals indicate that the proposed method is a better demodulation approach to extract the all the carrier and modulated components as well as the accurate IF, compared with Hilbert-Huang transform and wavelet project Hilbert spectrum. The validity of the technique is then demonstrated on the laboratory experiments and a real rotor system of a gas turbine with rub-impact fault. Due to the opposite friction during operation, the transient fluctuation of IF of the fundamental harmonic component is successfully identified in the ITFS. In addition, we find that the proposed technique is more effective and sensitive than other methods in detecting sub-harmonics and FM components contained in the rub-impact signals.In recent years, the adaptive decomposition methods have attracted many researchers'attention, because they are less influenced by human operator in practical applications. LMD and EMD are both adaptive decomposition methods. This dissertation compares LMD and EMD from four aspects through numerical simulation: local mean, decomposed components, instantaneous frequency and the wavelet-like filtering characteristic, and the results obtained are as follows: firstly, overshoot and undershoot of local mean can be avoid using LMD; secondly, more accurate IF of the signals can be acquired by LMD; thirdly, product functions contain more meaningful interpretation than IMFs and fourthly, mode separation may not occure using LMD. Then LMD and EMD are both applied in the health diagnosis of two industrial rotating machines with rub-impact and steam-excited vibration faults, respectively. The results reveal that LMD seems to be more suitable and have better performance than EMD for the incipient fault detection.In order to solve the defects of the first and second generation wavelet transform, kurtogram and EMD etc. in denoising and extracting impulse, a novel signal denoising technique and a multi-fault diagnosis technique based on the dual-tree complex wavelet transform (DTCWT) are proposed. Through numerical simulations, it is demonstrated that DTCWT enjoys better shift invariance and less spectral aliasing than first generation wavelet transform, second generation wavelet transform (SGWT) and empirical mode decomposition (EMD). These advantages arise from the relationship between two dual-tree wavelet basis functions, instead of the matching of the wavelet basis function to the signal being analyzed. Since noises inevitably exist in the measured signals, an enhanced vibration signal denoising algorithm incorporating DTCWT with NeighCoeff shrinkage is also developed. Denoising results of the vibration signals resulting from a cracked gear indicate the proposed denoising method can effectively remove noise and retain the valuable information as much as possible compared with DWT- and SGWT-based NeighCoeff shrinkage denoising methods. As is well known, the acquisition of comprehensive signatures embedded in the vibration signals is of practical importance to clearly identify the cause of the fault, especially the combined faults. In the case of multi-features detection, diagnosis results of rolling element bearings and an actual industrial equipment confirm that the proposed DTCWT-based method is a powerful and versatile tool and can consistently outperform SGWT and fast kurtogram widely used recently. Moreover, it must be noted, the proposed method is completely suitable for the on-line surveillance and diagnosis due to its good robustness and high efficiency.Monitoring and diagnosis system of vibration and acoustics for the submarine model is developed. By slave computer monitoring and the host computer analysis, running state of the equipment in the submarine model can be real-time and accurately monitored and prognosis on the incipent and potential faults can be conducted. Meanwhile, sound radiation of the model is monitored and main noise source is diagnosed, which may raise the concealment performance of the submarine model. DTCWT and LMD are used in the monitoring and diagnosis system. DTCWT is helpful to extract harmonic features of equipment in the submarine due to its good anti-aliasing filtering. Furthermore, DTCWT can be considered as a preprocessing method for LMD, which may enhance the performance of the filtering and detect more potential fault signatures.