本文選題：滾動軸承　切入點：譜峭度　出處：《上海大學》2014年博士論文　論文類型：學位論文

【摘要】：滾動軸承是最為廣泛應用的旋轉機械通用零部件之一，其工作狀態(tài)的優(yōu)劣直接關系到整臺機組乃至整條生產(chǎn)線的生產(chǎn)質(zhì)量和安全，因此，滾動軸承故障診斷技術的研究具有十分重要的意義。 特征提取是故障診斷技術的關鍵環(huán)節(jié)，本文針對滾動軸承故障特征提取現(xiàn)有方法的不足，深入研究了譜峭度及原子分解兩種有效的故障特征提取方法： (1)譜峭度為經(jīng)典滾動軸承共振解調(diào)方法提供了有效的自適應頻帶選擇工具，然而，雙參數(shù)同時精確定位的短時傅利葉變換譜峭度方法由于其巨大的計算量限制了實用性，而固定帶寬的單參數(shù)定位Protrugram方法失去了帶寬自適應性。本文根據(jù)滾動軸承故障振動信號的調(diào)幅特性，提出包絡定位FFT譜峭度方法，分步實現(xiàn)了中心頻率與帶寬的定位，解決了計算量與自適應性之間的矛盾。通過三種滾動軸承故障診斷方法的對比研究，驗證了新方法的有效性、自適應性以及實用性。 (2)有限沖擊響應（FIR）濾波器快速譜峭度方法提供了快速定位帶寬與中心頻度的近似方法，但由于其濾波器建立在傅利葉變換基礎上，對非平穩(wěn)信號特征提取具有局限性。本文提出小波包濾波器組快速譜峭度方法，并根據(jù)滾動軸承故障信息分布頻帶廣泛的特點，利用同步平均降噪原理，提出將指定同一分解層的部分或者全部子帶包絡譜的累積包絡譜方法，有效增強了軸承有用信息，提高了對滾動軸承故障特征的識別能力。 (3)同一類特征原子組成的字典難以適應實際信號由多種物理現(xiàn)象混合而成的復雜性，使得信號分解結果稀疏度不足、物理解釋困難。本文根據(jù)滾動軸承振動信號特征，構造由余弦包（CP）原子與小波包原子（WP）組成的混合字典，并提出快速CPWP混合原子分解匹配追蹤算法，提高了分解結果的稀疏性，，增強了物理解釋性。通過對滾動軸承的故障診斷，表明CPWP混合原子分解能夠有效提取到?jīng)_擊成分與載波成分，全面反映了滾動軸承故障特征。 (4)構造與實際復雜變化信號一致的參數(shù)化原子具有很大的困難。本文根據(jù)滾動軸承故障沖擊的循環(huán)平穩(wěn)與隨機性雙重特性，利用匹配追蹤對原子構造的寬松條件以及對信號的近似表達，提出從故障信號中提取特征波形，構造非參數(shù)化字典的非參數(shù)化原子分解診斷方法，從匹配度、稀疏度以及頻率分辨率幾方面研究了新方法的優(yōu)越性。通過對滾動軸承實測信號的診斷分析，并與譜相關密度方法以及包絡解調(diào)方法進行對比，驗證了該方法的有效性。
[Abstract]:Rolling bearing is one of the most widely used universal parts of rotating machinery. Its working condition is directly related to the production quality and safety of the whole unit and even the whole production line. The research of rolling bearing fault diagnosis technology is of great significance. Feature extraction is the key link of fault diagnosis technology. In this paper, two effective fault feature extraction methods, spectral kurtosis and atomic decomposition, are studied in order to overcome the shortcomings of the existing methods for fault feature extraction of rolling bearings. Spectral kurtosis provides an effective adaptive frequency band selection tool for the classical resonance demodulation method for rolling bearings. However, the short-time Fourier transform spectral kurtosis method, which can be accurately located by two parameters at the same time, has limited its practicability due to its huge computational complexity. In this paper, according to the amplitude modulation characteristic of rolling bearing fault vibration signal, the envelope positioning FFT spectrum kurtosis method is proposed, which realizes the location of center frequency and bandwidth step by step. The contradiction between computation and self-adaptability is solved, and the validity, adaptability and practicability of the new method are verified by the comparative study of three fault diagnosis methods for rolling bearings. The fast spectral kurtosis method of finite impulse response (FIR) filters provides an approximate method for fast location bandwidth and center frequency, but the filter is based on Fourier transform. In this paper, a fast spectral kurtosis method for wavelet packet filter banks is proposed. According to the wide frequency distribution of fault information of rolling bearings, the principle of synchronous average noise reduction is used. An accumulative envelope spectrum method with partial or all sub-band envelope spectrum assigned to the same decomposition layer is proposed, which can effectively enhance the useful information of bearings and improve the ability to identify the fault characteristics of rolling bearings. 3) the dictionary composed of the same kind of characteristic atoms is difficult to adapt to the complexity of the actual signals mixed by many physical phenomena, which makes the results of signal decomposition insufficient in sparsity and difficult in physical interpretation. In this paper, according to the characteristics of the vibration signals of rolling bearings, A hybrid dictionary consisting of cosine wrapped atom and wavelet packet atom is constructed, and a fast CPWP hybrid atomic decomposition matching tracing algorithm is proposed, which improves the sparsity of decomposition results and enhances the physical interpretation. The results show that the CPWP hybrid atomic decomposition can extract the impact and carrier components effectively and reflect the fault characteristics of rolling bearings. It is difficult to construct a parameterized atom which is consistent with the actual complex variation signal. In this paper, the cycle stability and randomness of rolling bearing fault impact are analyzed. Based on the loose conditions of atomic construction and approximate expression of signals by matching tracing, a nonparametric atomic decomposition diagnosis method is proposed to extract characteristic waveforms from fault signals and construct nonparametric dictionaries. The superiority of the new method is studied in terms of sparsity and frequency resolution. The effectiveness of this method is verified by the diagnosis and analysis of the measured signals of rolling bearings and the comparison with the spectral correlation density method and the envelope demodulation method.
【學位授予單位】：上海大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TH133.33;TH165.3

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