大数据下机械智能故障诊断的机遇与挑战

doi:10.3901/JME.2018.05.094

摘要/Abstract

摘要： 机械故障是风力发电设备、航空发动机、高档数控机床等大型机械装备安全可靠运行的“潜在杀手”。故障诊断是保障机械装备安全运行的“杀手锏”。由于诊断的装备量大面广、每台装备测点多、数据采样频率高、装备服役历时长，所以获取了海量的诊断数据，推动故障诊断领域进入了“大数据”时代。而机械智能故障诊断有望成为大数据下诊断机械装备故障的“一把利器”。与此同时，大数据给机械智能故障诊断的深入研究和应用提供了新的机遇：“数据为王”的学术思想有望成为主流、诊断整机或系统级对象成为可能、全面解析故障演化过程成为趋势等；但也遇到了新的挑战：数据大而不全呈“碎片化”、故障特征提取受制于人为经验、浅层诊断模型诊断精度低等。阐述了机械智能故障诊断大数据的特点；从信号获取、特征提取、故障识别与预测三个环节，综述了机械智能故障诊断的国内外研究进展和发展动态；指出了机械智能故障诊断理论与方法在大数据背景下的挑战；最后讨论了应对这些挑战的解决途径与发展趋势。

关键词: 大数据, 机械装备, 智能故障诊断

Abstract: Faults are a potential killer of large-scale mechanical equipment, such as wind power equipment, aircraft engines and high-end CNC machine. And fault diagnosis plays an irreplaceable role in ensuring the health operation of such equipment. Since the amount of the equipment diagnosed is great and the number of the sensors for the equipment is large, massive data are acquired by the high sampling frequency after the long-time operation of equipment. Such massive data promote fault diagnosis to enter the era of big data. And machinery intelligent fault diagnosis is a promising tool to deal with mechanical big data. In the big data era, new opportunities have been brought to intelligent fault diagnosis. For instance, data-centric academic thinking will become mainstream, it makes fault diagnosis in the system level possible, and a comprehensive analysis of faults becomes a trend. Meanwhile, new challenges have also been brought:the data are big but fragmentary, the fault feature extraction relies on much prior knowledge and diagnostics expertise, and the generalization ability of the shallow diagnosis model is weak. The characteristics of big data in intelligent fault diagnosis are discussed, and the inland and overseas research advances are reviewed from the three steps of intelligent fault diagnosis. The existing key problems of the current research in the era of big data are pointed out, and the approaches and research directions to these problems are discussed in the end.

Key words: big data, intelligent fault diagnosis, mechanical equipment

中图分类号:

TH17

雷亚国, 贾峰, 孔德同, 林京, 邢赛博. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5): 94-104.

LEI Yaguo, JIA Feng, KONG Detong, LIN Jing, XING Saibo. Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era[J]. Journal of Mechanical Engineering, 2018, 54(5): 94-104.

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