基于流形學(xué)習(xí)的智能診斷方法研究
發(fā)布時(shí)間：2018-10-14 10:09
【摘要】：故障診斷的實(shí)質(zhì)是模式識(shí)別,主要研究?jī)?nèi)容包括信號(hào)獲取、特征提取和模式分類三個(gè)方面。特征提取是故障診斷技術(shù)中最困難而又關(guān)鍵的環(huán)節(jié),它直接影響故障診斷結(jié)果的準(zhǔn)確性和故障早期預(yù)報(bào)的可靠性。因此,在復(fù)雜運(yùn)行工況下,如何提取最優(yōu)的低維故障特征來提高故障分類性能是一個(gè)巨大的挑戰(zhàn)。本論文以流形學(xué)習(xí)算法為基礎(chǔ),深入研究了基于流形學(xué)習(xí)的特征提取與診斷技術(shù)。 針對(duì)復(fù)雜故障設(shè)備多個(gè)特征參數(shù)之間存在冗余性或不相關(guān)性,可能會(huì)增加后續(xù)分類器的時(shí)間消耗,甚至?xí)档凸收系淖R(shí)別精度,提出了基于邊界Fisher分析(MFA)算法的診斷模型。為了準(zhǔn)確而全面地獲取設(shè)備的故障信息,該模型采用多種信號(hào)處理方法進(jìn)行分析,從多角度提取多個(gè)特征參數(shù)來表征設(shè)備的運(yùn)行狀態(tài)；運(yùn)用MFA算法,從原始高維特征集中提取最具代表性的低維流形特征,并將所有低維特征輸入K近鄰分類器進(jìn)行故障識(shí)別。通過對(duì)滾動(dòng)軸承早期故障的診斷分析,驗(yàn)證了該模型的可行性和有效性。 針對(duì)機(jī)械設(shè)備故障診斷這種小樣本模式識(shí)別問題,提出了正則化核邊界Fisher分析(RKMFA)的特征提取算法及基于該算法的診斷模型。該模型運(yùn)用RKMFA算法,直接從原始高維振動(dòng)信號(hào)中提取低維流形特征,并將這些具有判別信息的少數(shù)幾個(gè)流形特征輸入K近鄰分類器,最終識(shí)別出機(jī)械系統(tǒng)的故障模式。將該模型分別應(yīng)用于軸承故障類型和內(nèi)圈損傷程度的識(shí)別,實(shí)驗(yàn)結(jié)果表明RKMFA算法是一種有效的特征提取算法,同時(shí)驗(yàn)證了該模型的優(yōu)越性。 針對(duì)機(jī)械設(shè)備故障診斷過程中獲取有標(biāo)簽故障樣本比較費(fèi)時(shí)費(fèi)力,提出了半監(jiān)督核邊界Fisher分析(SSKMFA)的特征提取算法及基于該算法的診斷模型。該模型運(yùn)用SSKMFA算法直接對(duì)原始高維振動(dòng)信號(hào)進(jìn)行學(xué)習(xí),通過大量廉價(jià)的無標(biāo)簽故障樣本和少量昂貴的有標(biāo)簽故障樣本估算故障數(shù)據(jù)的潛在流形結(jié)構(gòu),并在有標(biāo)簽故障樣本提供的監(jiān)督信息的引導(dǎo)下,學(xué)習(xí)出整個(gè)流形上的類別信息,從而提取具有判別性的低維流形特征,使得無標(biāo)簽故障樣本獲得良好的分類效果。將該模型分別應(yīng)用于軸承故障類型以及故障嚴(yán)重程度的識(shí)別和齒輪箱故障類型的診斷,實(shí)驗(yàn)結(jié)果表明該模型能大大提高故障識(shí)別精度,同時(shí)降低算法的計(jì)算復(fù)雜度。 針對(duì)基于流形學(xué)習(xí)算法提取的低維流形特征沒有明確的物理意義,導(dǎo)致其在故障診斷方面的理解性比較差等問題,提出了MFA分值的特征選擇算法及基于該算法和支持向量機(jī)分類器的診斷模型。該模型采用多種信號(hào)處理方法對(duì)故障信號(hào)進(jìn)行分析,得到一個(gè)由多個(gè)特征參數(shù)構(gòu)造的原始高維特征集；運(yùn)用MFA分值算法,挖掘隱藏在原始高維特征中的內(nèi)在規(guī)律性,從而挑選出充分反映故障本質(zhì)的敏感特征子集,將其輸入到SVM分類器中,最終識(shí)別出設(shè)備的運(yùn)行狀態(tài)。在滾動(dòng)軸承故障類型和內(nèi)圈損傷程度的診斷實(shí)驗(yàn)中,驗(yàn)證了該模型的優(yōu)越性。
[Abstract]:The essence of fault diagnosis is pattern recognition. The main research contents include signal acquisition, feature extraction and pattern classification. Feature extraction is the most difficult and critical link in fault diagnosis technology. It directly affects the accuracy of fault diagnosis and the reliability of early prediction. Therefore, it is a great challenge to extract the optimal low-dimensional fault feature to improve the fault classification performance under complex operating conditions. Based on manifold learning algorithm, this paper studies the feature extraction and diagnosis technology based on manifold learning. Aiming at the redundancy or non-correlation among multiple characteristic parameters of complex fault equipment, it is possible to increase the time consumption of subsequent classifier, even reduce the recognition precision of fault, and put forward the diagnosis based on the boundary Fisher analysis (MFA) algorithm. In order to obtain the fault information of the equipment accurately and comprehensively, the model adopts a variety of signal processing methods to analyze, extracts a plurality of characteristic parameters from multiple angles to characterize the running state of the equipment, and uses the MFA algorithm to extract the most representative low-dimensional manifold from the original high-dimensional characteristic set. Feature, and enter all low-dimension features into K-nearest classifier for failure The feasibility and feasibility of the model are verified by the diagnosis and analysis of the early faults of rolling bearings. Aiming at the problem of small sample pattern recognition of mechanical equipment fault diagnosis, a feature extraction algorithm of regular kernel boundary Fisher analysis (RKMFA) is proposed, and the algorithm based on this algorithm is proposed. The model applies the RKMFA algorithm to extract the low-dimensional manifold feature directly from the original high-dimensional vibration signal and inputs the few manifold features with the discrimination information into the K-nearest classifier, and finally identifies the mechanical system. The model is applied to the identification of the type of bearing failure and the degree of damage of the inner ring respectively. The experimental results show that the RKMFA algorithm is an effective feature extraction algorithm, and the model is verified at the same time. This paper presents the feature extraction algorithm of the semi-supervised kernel boundary Fisher analysis (SSKMFA) and based on the comparison of the tag fault samples in the fault diagnosis of mechanical equipment. The model uses SSKMFA algorithm to directly study the original high-dimensional vibration signals, estimates the potential manifold structure of fault data through a large number of cheap non-label fault samples and a small amount of expensive tag fault samples, and provides them with label fault samples. under the guidance of supervision information, the class information on the whole manifold is learned so as to extract the characteristic of the low-dimensional manifold with discrimination, so that the non-label fault sample is obtained. The model can be applied to the identification of bearing fault types and the severity of faults and the diagnosis of gearbox fault types respectively. The experimental results show that the model can greatly improve the accuracy of fault recognition and reduce the same time. In this paper, a feature selection algorithm for MFA score and its support based on manifold learning algorithm are presented. A diagnosis model of a vector machine classifier is used to analyze the fault signals by adopting a plurality of signal processing methods to obtain an original high-Viterbi collection constructed by a plurality of characteristic parameters, wherein the mining is hidden in the original high-Viterbi algorithm by using the MFA score algorithm. the inherent regularity in the high-dimensional features is selected to select a subset of sensitive features that adequately reflect the nature of the fault, which is input into the SVM classifier, most The operation state of the equipment is finally identified. In the diagnosis experiment of the fault type of rolling bearing and the degree of damage of the inner ring
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：TH165.3
本文編號(hào)：2270138
[Abstract]:The essence of fault diagnosis is pattern recognition. The main research contents include signal acquisition, feature extraction and pattern classification. Feature extraction is the most difficult and critical link in fault diagnosis technology. It directly affects the accuracy of fault diagnosis and the reliability of early prediction. Therefore, it is a great challenge to extract the optimal low-dimensional fault feature to improve the fault classification performance under complex operating conditions. Based on manifold learning algorithm, this paper studies the feature extraction and diagnosis technology based on manifold learning. Aiming at the redundancy or non-correlation among multiple characteristic parameters of complex fault equipment, it is possible to increase the time consumption of subsequent classifier, even reduce the recognition precision of fault, and put forward the diagnosis based on the boundary Fisher analysis (MFA) algorithm. In order to obtain the fault information of the equipment accurately and comprehensively, the model adopts a variety of signal processing methods to analyze, extracts a plurality of characteristic parameters from multiple angles to characterize the running state of the equipment, and uses the MFA algorithm to extract the most representative low-dimensional manifold from the original high-dimensional characteristic set. Feature, and enter all low-dimension features into K-nearest classifier for failure The feasibility and feasibility of the model are verified by the diagnosis and analysis of the early faults of rolling bearings. Aiming at the problem of small sample pattern recognition of mechanical equipment fault diagnosis, a feature extraction algorithm of regular kernel boundary Fisher analysis (RKMFA) is proposed, and the algorithm based on this algorithm is proposed. The model applies the RKMFA algorithm to extract the low-dimensional manifold feature directly from the original high-dimensional vibration signal and inputs the few manifold features with the discrimination information into the K-nearest classifier, and finally identifies the mechanical system. The model is applied to the identification of the type of bearing failure and the degree of damage of the inner ring respectively. The experimental results show that the RKMFA algorithm is an effective feature extraction algorithm, and the model is verified at the same time. This paper presents the feature extraction algorithm of the semi-supervised kernel boundary Fisher analysis (SSKMFA) and based on the comparison of the tag fault samples in the fault diagnosis of mechanical equipment. The model uses SSKMFA algorithm to directly study the original high-dimensional vibration signals, estimates the potential manifold structure of fault data through a large number of cheap non-label fault samples and a small amount of expensive tag fault samples, and provides them with label fault samples. under the guidance of supervision information, the class information on the whole manifold is learned so as to extract the characteristic of the low-dimensional manifold with discrimination, so that the non-label fault sample is obtained. The model can be applied to the identification of bearing fault types and the severity of faults and the diagnosis of gearbox fault types respectively. The experimental results show that the model can greatly improve the accuracy of fault recognition and reduce the same time. In this paper, a feature selection algorithm for MFA score and its support based on manifold learning algorithm are presented. A diagnosis model of a vector machine classifier is used to analyze the fault signals by adopting a plurality of signal processing methods to obtain an original high-Viterbi collection constructed by a plurality of characteristic parameters, wherein the mining is hidden in the original high-Viterbi algorithm by using the MFA score algorithm. the inherent regularity in the high-dimensional features is selected to select a subset of sensitive features that adequately reflect the nature of the fault, which is input into the SVM classifier, most The operation state of the equipment is finally identified. In the diagnosis experiment of the fault type of rolling bearing and the degree of damage of the inner ring
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：TH165.3
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