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