人工智能在机械制造设备故障诊断与维修中的应用

Application of Artificial Intelligence in Fault Diagnosis and Maintenance of Machinery Manufacturing Equipment

为了提升机械制造设备的故障诊断准确率及维修效率,采用深度学习算法,特别是卷积神经网络(CNN)和循环神经网络(RNN),对设备操作数据进行处理与分析。通过这些算法,系统能够自主抽取关键特性,并据此对设备的运行状况作出推测与判定。结果表明,在轴承故障的诊断中,人工智能算法的准确率高达95%,召回率为93%,F1分数为0.94,检测时间仅需1.5 h,误报率仅为2%。此外,通过应用人工智能的维修决策系统,维修响应时间平均缩短至2.5 h,故障诊断准确率达到95%,平均维修时间为4 h,维修成功率提升至98%。由此,在机器设备的缺陷检测与修复领域深层次学习技术性能表现优越。在极高的不确定性条件下特别是当使用了卷积神经网络(CNN)和循环神经网络(RNN)时,该系统能够高效率地对设备的操作数据进行处理与分析,准确地提取出核心特征,从而能对机器的运作状态作出精确的预测评估。

In order to improve the fault diagnosis accuracy and repair efficiency of machinery manufacturing equipment,deep learning algorithms,especially convolutional neural network(CNN)and recurrent neural network(RNN),are used to process and analyse equipment operation data.Through these algorithms,the system is able to extract key characteristics autonomously,and make speculation and judgement on the operating condition of the equipment accordingly.The results show that in the diagnosis of bearing faults,the AI algorithms have an accuracy rate of 95%,a recall rate of 93%,an F1 score of 0.94,a detection time of only 1.5 h,and a false alarm rate of only 2%.In addition,by applying the maintenance decision-making system of AI,the average maintenance response time is shortened to 2.5 h,the accuracy of fault diagnosis reaches 95%,the average maintenance time is 4 h,and the maintenance success rate is increased to 98%.As a result,the performance of the deep learning technology in the field of defect detection and repair of machines and equipment is superior.The system is able to process and analyse the operational data of the equipment in a highly efficient manner under very high uncertainty conditions especially when Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)are used,and accurately extracts the core features to enable accurate predictive assessment of the machine's operational status.