本文選題：紅外熱像 + 電氣設(shè)備　； 參考：《沈陽工業(yè)大學(xué)》2015年碩士論文

【摘要】：監(jiān)測電氣設(shè)備的熱狀況,在對于保持電氣系統(tǒng)的可靠性上是非常必要的。電氣設(shè)備中的老化等因素將導(dǎo)致過熱狀況的產(chǎn)生,而這些熱問題最終會導(dǎo)致設(shè)備故障的產(chǎn)生。此外,設(shè)備故障產(chǎn)生后需要花費大量的維修成本、人力,還有可能變成災(zāi)難,造成人生傷害甚至是死亡。因此,識別設(shè)備運行狀態(tài)是否處在正常狀態(tài)下的這一過程,對于維持系統(tǒng)的可靠性和穩(wěn)定性是非常重要的。如今,紅熱成像技術(shù)由于其快速、可靠、非接觸性以及良好的經(jīng)濟性能,被廣泛的應(yīng)用于設(shè)備的故障檢測及診斷上。本文提出了一種利用紅外熱像儀獲取電氣設(shè)備紅外溫度熱像圖,并根據(jù)數(shù)據(jù)進行分析來評價設(shè)備熱狀態(tài)的方法。電氣設(shè)備的紅外溫度熱像圖,是在設(shè)備不解體、不影響設(shè)備運行的情況下通過紅外熱成像儀獲取。選用的紅外熱像儀是由日氣NEC公司出產(chǎn)的。獲取紅外溫度熱像圖后,首先,對紅外溫度熱像圖進行手動分割獲取有效區(qū)域,繼而,進行預(yù)處理和圖像增強。圖像增強中,本文采用了三種方法進行對比,直方圖均衡化、二維離散小波變換和二維經(jīng)驗?zāi)B(tài)分解。隨之,提取故障部位與相關(guān)組件的不同的一階直方圖特征和灰度共生矩陣特征作為特征量,共計22個特征量。使用主成分分析與判別分析相結(jié)合的方法進行特征的優(yōu)化選取,先利用主成分分析法對特征量進行初步的選擇,從22個特征量中選擇出15個特征量,再利用判別分析法從15個特征量中剔除5個特征量。最終從22個特征量中選擇出10個特征量作為狀態(tài)分類系統(tǒng)的輸入。在最終的狀態(tài)分類上采用兩種方法：判別分析法和人工神經(jīng)網(wǎng)絡(luò)方法。將利用判別分析分類的結(jié)果與利用神經(jīng)網(wǎng)絡(luò)方法進行分類的結(jié)果進行對比。結(jié)果表明,利用判別分析進行故障狀態(tài)分類相較于人工神經(jīng)網(wǎng)絡(luò)有更好的性能體現(xiàn),最優(yōu)結(jié)果為精確度82.6%。
[Abstract]:Monitoring the thermal condition of electrical equipment is necessary to maintain the reliability of electrical system. Factors such as aging in electrical equipment will lead to overheating, and these thermal problems will eventually lead to equipment failure. In addition, equipment failure will cost a lot of maintenance costs, manpower, and may become a disaster, causing life damage or even death. Therefore, it is very important to identify whether the equipment is in the normal state or not, to maintain the reliability and stability of the system. Nowadays, red thermal imaging technology is widely used in fault detection and diagnosis of equipment due to its rapid, reliable, non-contact and good economic performance. In this paper, a method of obtaining infrared thermogram of electrical equipment by infrared thermal imager and analyzing the data to evaluate the thermal state of the equipment is presented. The infrared thermogram of electrical equipment is obtained by infrared thermal imager without disintegrating the equipment and not affecting the operation of the equipment. The selected infrared thermal imager is produced by NEC. After obtaining the infrared thermogram, the infrared thermal image is segmented manually to obtain the effective region, and then the preprocessing and image enhancement are carried out. In image enhancement, three methods, histogram equalization, two-dimensional discrete wavelet transform and two-dimensional empirical mode decomposition, are used. Subsequently, different first-order histogram features and gray level co-occurrence matrix features of fault location and related components are extracted as feature quantities, and a total of 22 feature quantities are extracted. The method of combining principal component analysis (PCA) with discriminant analysis is used to optimize the selection of features. First, the method of principal component analysis is used to select the characteristic quantity, and 15 characteristic quantities are selected from the 22 characteristic quantities. Then the discriminant analysis was used to eliminate 5 characteristic variables from 15 characteristic variables. Finally, 10 of the 22 feature variables are selected as the input of the state classification system. Two methods are used in the final state classification: discriminant analysis and artificial neural network. The results of discriminant analysis and neural network are compared. The results show that the performance of fault state classification by discriminant analysis is better than that of artificial neural network, and the best result is 82.6 accuracy.
【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP391.41;TN219

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本文編號：1780451