In industrial applications, detecting a fault in time is critical to ensure production safety. Edge-cloud collaborative condition monitoring provides a more flexible solution to achieve both computational efficiency and accuracy. In this paper, an Edge-cloud collaborative multi-level fault diagnosis model is developed based on stacked sparse autoencoder to minimize the fault detection time, meanwhile, the diagnostic accuracy can also be guaranteed. By filtering most of the normal data and less model inference time, the anomaly detection model on the edge can minimize the fault detection time. When a fault occurs, the fault data will be sent to the cloud to infer the fault details. The experimental results show that the proposed method can detect faults 0.12s earlier on average compared to edge-inferencing after cloud-trained method.
Edge-cloud collaboration provides a better solution for condition monitoring, which can reduce response time while maintaining computational efficiency. In practical condition monitoring scenarios, the individual differences among equipment often decrease the accuracy of diagnostic models. To tackle this problem, a transfer learning method based on stacked sparse autoencoder is proposed, which employs a data regularization strategy to improve feature extraction ability. The fault diagnosis model trained in the cloud transfers its model parameters and structure to the edge side. By a finetuning process with a small amount of data, and the model is further updated for condition monitoring of the individual machine. The experimental results show that the proposed KT-SAE method has improved transfer accuracy compared to other related transfer learning methods.
The influence of parameters on load distribution between teeth of HCR involute spur gear was studied. The load distribution was studied and verified theoretically by test. Under the condition of given parameter, the change curve of load distribution and maximum load sharing rate were studied when addendum coefficient, pressure angle, tooth number, modification coefficient and base pitch error respectively changed. The test result show that, under high loads, the maximum load sharing rate are linearly correlation to addendum coefficient, pressure angle, modification coefficient and base pitch error. With the increase of the number of teeth, the change rate of the maximum load sharing rate decreases from large to small, then tends to be flat. The rate of change in load sharing at the point of tooth engagement is small when the top height co-efficient, pressure angle, number of teeth and coefficient of variation change, and relatively large when the base joint deviation changes.
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