【摘要】：設(shè)備故障樣本缺乏、狀態(tài)檢測與故障診斷分離、訓(xùn)練與測試過程相互獨(dú)立是制約現(xiàn)有智能故障診斷方法廣泛應(yīng)用的主要原因。借鑒生物免疫機(jī)理，開展對設(shè)備適應(yīng)性強(qiáng)、對故障樣本依賴程度低，并且具有連續(xù)學(xué)習(xí)能力的設(shè)備在線學(xué)習(xí)異常檢測與故障診斷方法具有重要的科學(xué)意義。 為了提高傳統(tǒng)實(shí)值反面選擇算法檢測器的覆蓋率和減少冗余檢測器，提出了固定邊界反面選擇算法、精細(xì)固定邊界反面選擇算法、基于邊界樣本的界面檢測器和基于約簡邊界樣本的界面檢測器。在深入討論算法的基礎(chǔ)上，應(yīng)用15組2維人造數(shù)據(jù)集和Iris數(shù)據(jù)集進(jìn)行仿真實(shí)驗(yàn)，分析了四種檢測器的異常檢測性能。與其它異常檢測算法相比，訓(xùn)練樣本參數(shù)相同時(shí)，多數(shù)情況下，此四種算法具有更好的檢測性能；另外，當(dāng)檢測率相近時(shí)，檢測器(邊界樣本)數(shù)量依次減少。 在全面分析固定邊界反面選擇算法和基于邊界樣本界面檢測器特性的基礎(chǔ)上，提出了小樣本在線學(xué)習(xí)固定邊界反面選擇算法和小樣本在線學(xué)習(xí)界面檢測器，并分析了造成兩種算法過學(xué)習(xí)與欠學(xué)習(xí)的原因。通過仿真實(shí)驗(yàn)，，討論了這兩種小樣本在線學(xué)習(xí)異常檢測算法相對于傳統(tǒng)反面選擇算法的優(yōu)勢，分析了小樣本在線學(xué)習(xí)界面檢測器優(yōu)于小樣本在線學(xué)習(xí)固定邊界反面選擇算法的原因。借鑒免疫系統(tǒng)的疫苗機(jī)理，在小樣本在線學(xué)習(xí)界面檢測器算法的基礎(chǔ)上引入活性疫苗克服了欠學(xué)習(xí)降低了誤報(bào)警率，引入惰性疫苗抑制了過學(xué)習(xí)提高了檢測率。 在深入分析界面檢測器特性的基礎(chǔ)上，引入異常度和異常等級兩個(gè)概念，結(jié)合界面檢測器的連續(xù)學(xué)習(xí)特性，提出了具有小樣本在線學(xué)習(xí)異常檢測與故障診斷能力的自適應(yīng)超環(huán)檢測器。將分布在非己空間內(nèi)有限的故障樣本構(gòu)建小樣本在線學(xué)習(xí)故障界面檢測器，并引入類間隸屬度概念，實(shí)現(xiàn)了對已知類型故障樣本分類、未知類型故障樣本聚類的功能。 使用軸承故障數(shù)據(jù)進(jìn)行仿真，討論了各種條件下自適應(yīng)超環(huán)檢測器的小樣本在線學(xué)習(xí)異常檢測與故障診斷性能，與其它故障診斷方法相比，自適應(yīng)超環(huán)檢測器的診斷準(zhǔn)確率更高。自適應(yīng)超環(huán)檢測器不僅實(shí)現(xiàn)了異常檢測與故障診斷一體化，而且具備在線學(xué)習(xí)能力；不僅具備小樣本故障診斷能力，還能識別未知類型故障；不僅能隨時(shí)加入故障樣本，還具備數(shù)據(jù)壓縮功能，具有廣泛的應(yīng)用前景。
[Abstract]:The lack of equipment fault samples, the separation of state detection and fault diagnosis, and the independence of training and testing processes are the main reasons that restrict the wide application of the existing intelligent fault diagnosis methods. Based on the biological immune mechanism, it is of great scientific significance to carry out the methods of equipment online learning anomaly detection and fault diagnosis, which have strong adaptability to equipment, low dependence on fault samples and continuous learning ability. In order to improve the coverage of the traditional real value negative side selection algorithm and reduce the redundant detector, a fixed boundary inverse selection algorithm and a fine fixed boundary reverse surface selection algorithm are proposed. The interface detector based on boundary sample and the interface detector based on reduced boundary sample. On the basis of in-depth discussion of the algorithm, 15 groups of 2D artificial data sets and Iris data sets are used to carry out simulation experiments, and the anomaly detection performance of four kinds of detector is analyzed. Compared with other anomaly detection algorithms, when the training sample parameters are the same, the four algorithms have better detection performance in most cases, in addition, when the detection rate is similar, the number of detector (boundary samples) decreases in turn. Based on the comprehensive analysis of the fixed boundary inverse selection algorithm and the characteristics of the interface detector based on the boundary sample, a small sample online learning fixed boundary negative surface selection algorithm and a small sample online learning interface detector are proposed. The causes of overlearning and underlearning of the two algorithms are analyzed. Through simulation experiments, the advantages of these two small sample online learning anomaly detection algorithms over the traditional negative selection algorithm are discussed. The reason why the small sample online learning interface detector is superior to the small sample online learning fixed boundary negative selection algorithm is analyzed. Based on the vaccine mechanism of immune system, the active vaccine is introduced on the basis of small sample online learning interface detector algorithm to overcome underlearning and reduce the false alarm rate, while the introduction of lazy vaccine suppresses overlearning and improves the detection rate. Based on the in-depth analysis of the characteristics of the interface detector, the concepts of anomaly degree and anomaly grade are introduced, and the continuous learning characteristics of the interface detector are combined. An adaptive hyperloop detector with the ability of small sample online learning anomaly detection and fault diagnosis is proposed. The fault samples distributed in non-self space are constructed to construct small sample online learning fault interface detector, and the concept of inter-class membership degree is introduced to realize the function of classification of known types of fault samples and clustering of unknown types of fault samples. The performance of small sample online learning anomaly detection and fault diagnosis of adaptive hyperloop detector under various conditions is discussed by using bearing fault data. Compared with other fault diagnosis methods, The diagnostic accuracy of adaptive hyperloop detector is higher. The adaptive hyperloop detector not only realizes the integration of anomaly detection and fault diagnosis, but also has the ability of online learning, not only the ability of small sample fault diagnosis, but also the ability to identify unknown types of faults. It can not only add fault samples at any time, but also has the function of data compression, and has a wide range of application prospects.
【學(xué)位授予單位】：上海大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TP274;TH165.3

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