针对由于机床故障诊断中人工判断误差大、发现不及时等问题而造成机床部件疲劳损坏、生产停滞等现象，该研究提出将物联网、神经网络技术应用于机床故障诊断，通过在机床主要部位部署多种传感器，实现对机床运行数据的实时采集、分析和处理，完成对机床故障的智能诊断和预警，结合web应用和手机app应用实现操作人员和管理人员对机床故障、运行状态的实时掌控，提高了机床故障诊断的智能化程度和实时性，通过实测证明该研究对机床故障的诊断具有前瞻性、可靠性和实时性，提高了机床故障的诊断能力和预警能力。%When diagnosing on machine tools, there exist problems as big error, delay found, etc. , which lead to fatigue damage and stagnation production of components. To solve the problems, the technologies of IOT( internet of things) and neural network are used in diagnosing machine tools failure. Many sensors are fixed on the main parts of machine tools to conduct in-time collection , analysis and handling on running da-ta, besides, to fulfill the task of intelligent diagnosis and warning on failures. The system combined web and app to realize the operators’ and administrators’ in-time control on failures and running state. It also im-prove the intelligence level and instantaneity. the testing results showed the study and design improved the diagnosis and warning ability on machine tools’ failures.
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