With the development of the times, people have higher requirements for information quality, data transmission and processing. Timely fault detection and maintenance of mechanical equipment in the workshop can reduce the loss of the factory. In order to prove that data processing plays an important role in practical application, this paper studies the advantages of anomaly detection algorithm in equipment detection and maintenance of mechanical workshop. This paper mainly uses the methods of experiment and comparison to obtain the accuracy of the relevant detection algorithm model by changing the variable record data. The experimental results show that the accuracy of DBM model is more than 99% when the number of hidden layers is 2. With the increase of hidden layers, the recognition accuracy of DBN model decreases.

Olcese Umberto. Equipment Detection and Maintenance in Mechanical Workshop Based on Anomaly Detection Algorithm. Kinetic Mechanical Engineering (2022), Vol. 3, Issue 2: 1-9. https://doi.org/10.38007/KME.2022.030201.

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