机械工程学报
機械工程學報
궤계공정학보
Journal of Mechanical Engineering
2015年
21期
49-56
,共8页
雷亚国%贾峰%周昕%林京
雷亞國%賈峰%週昕%林京
뢰아국%가봉%주흔%림경
机械健康监测%深度学习理论%大数据分析
機械健康鑑測%深度學習理論%大數據分析
궤계건강감측%심도학습이론%대수거분석
machinery health monitoring%deep learning theory%big data analysis
机械装备正在朝着高速、高精、高效方向发展,为了确保这些装备的健康运行,健康监测系统采集了海量数据来反映机械的健康状况,促使机械健康监测领域进入了"大数据"时代.机械大数据具有大容量、多样性与高速率的特点,研究和利用先进的理论与方法,从机械装备大数据中挖掘信息,高效、准确地识别装备的健康状况,成为机械装备健康监测领域面临的新问题.深度学习理论作为模式识别和机器学习领域最新的研究成果,以强大的建模和表征能力在图像和语音处理等领域的大数据处理方面取得了丰硕的成果.结合机械大数据的特点与深度学习的优势,提出了一种新的机械装备健康监测方法.该方法通过深度学习利用机械频域信号训练深度神经网络,其优势在于能够摆脱对大量信号处理技术与诊断经验的依赖,完成故障特征的自适应提取与健康状况的智能诊断,因此克服了传统智能诊断方法的两大缺陷:需要掌握大量的信号处理技术结合丰富的工程实践经验来提取故障特征;使用浅层模型难以表征大数据情况下信号与健康状况之间复杂的映射关系.试验结果表明,该方法实现了多种工况、大量样本下多级齿轮传动系统不同故障位置不同故障类型的故障特征自适应提取与健康状况准确识别.
機械裝備正在朝著高速、高精、高效方嚮髮展,為瞭確保這些裝備的健康運行,健康鑑測繫統採集瞭海量數據來反映機械的健康狀況,促使機械健康鑑測領域進入瞭"大數據"時代.機械大數據具有大容量、多樣性與高速率的特點,研究和利用先進的理論與方法,從機械裝備大數據中挖掘信息,高效、準確地識彆裝備的健康狀況,成為機械裝備健康鑑測領域麵臨的新問題.深度學習理論作為模式識彆和機器學習領域最新的研究成果,以彊大的建模和錶徵能力在圖像和語音處理等領域的大數據處理方麵取得瞭豐碩的成果.結閤機械大數據的特點與深度學習的優勢,提齣瞭一種新的機械裝備健康鑑測方法.該方法通過深度學習利用機械頻域信號訓練深度神經網絡,其優勢在于能夠襬脫對大量信號處理技術與診斷經驗的依賴,完成故障特徵的自適應提取與健康狀況的智能診斷,因此剋服瞭傳統智能診斷方法的兩大缺陷:需要掌握大量的信號處理技術結閤豐富的工程實踐經驗來提取故障特徵;使用淺層模型難以錶徵大數據情況下信號與健康狀況之間複雜的映射關繫.試驗結果錶明,該方法實現瞭多種工況、大量樣本下多級齒輪傳動繫統不同故障位置不同故障類型的故障特徵自適應提取與健康狀況準確識彆.
궤계장비정재조착고속、고정、고효방향발전,위료학보저사장비적건강운행,건강감측계통채집료해량수거래반영궤계적건강상황,촉사궤계건강감측영역진입료"대수거"시대.궤계대수거구유대용량、다양성여고속솔적특점,연구화이용선진적이론여방법,종궤계장비대수거중알굴신식,고효、준학지식별장비적건강상황,성위궤계장비건강감측영역면림적신문제.심도학습이론작위모식식별화궤기학습영역최신적연구성과,이강대적건모화표정능력재도상화어음처리등영역적대수거처리방면취득료봉석적성과.결합궤계대수거적특점여심도학습적우세,제출료일충신적궤계장비건강감측방법.해방법통과심도학습이용궤계빈역신호훈련심도신경망락,기우세재우능구파탈대대량신호처리기술여진단경험적의뢰,완성고장특정적자괄응제취여건강상황적지능진단,인차극복료전통지능진단방법적량대결함:수요장악대량적신호처리기술결합봉부적공정실천경험래제취고장특정;사용천층모형난이표정대수거정황하신호여건강상황지간복잡적영사관계.시험결과표명,해방법실현료다충공황、대량양본하다급치륜전동계통불동고장위치불동고장류형적고장특정자괄응제취여건강상황준학식별.
Mechanical equipment in modern industries becomes more automatic, precise and efficient. To fully inspect its health conditions, condition monitoring systems are used to collect real-time data from the equipment, and massive data are acquired after the long-time operation, which promotes machinery health monitoring to enter the age of big data. Mechanical big data has the properties of large-volume, diversity and high-velocity. Effectively mining characteristics from such data and accurately identifying the machinery health conditions with advanced theories become new issues in machinery health monitoring. To harness the properties of mechanical big data and the advantages of deep learning theory, a health monitoring and fault diagnosis method for machinery is proposed. In the proposed method, deep neural networks with deep architectures are established to adaptively mine available fault characteristics and automatically identify machinery health conditions. Correspondingly, the proposed method overcomes two deficiencies of the traditional intelligent diagnosis methods: (1) the features are manually extracted relying on much prior knowledge about signal processing techniques and diagnostic expertise; (2) the used models have shallow architectures, limiting their capability in fault diagnosis issues. The proposed method is validated using datasets of multi-stage gear transmission systems, which contain massive data involving different health conditions under various operating conditions. The results show that the proposed method is able to not only adaptively mine available fault characteristics from the data, but also obtain higher identification accuracy than the existing methods.