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Modern industrial production, the industrial sector and daily life are inextricably linked to a plethora of chemical machinery and equipment, which serve as indispensable production apparatus. It is therefore paramount to guarantee their optimal functionality. It is therefore of great significance to the field of chemical machinery and equipment that fault monitoring is given due attention. The progressive advancement of computer and wireless communication technology has facilitated the maturation of expert diagnosis systems and the Internet of Things. Consequently, fault monitoring systems based on these technologies are poised for widespread adoption. In this work, the instrument operation data collected by IoT devices is used to undergo preliminary cleaning and normalization to ensure the consistency and accuracy of the data. This preprocessed data is then fed into a multi-layered deep neural network (DNN) model. In the model training stage, the backpropagation algorithm is used to calculate the gradient of each layer, and the weights and biases in the network are adjusted by the stochastic gradient descent (SGD) optimization algorithm to minimize the loss function. Through iterative training, the model gradually improves its ability to identify failure modes and predict accuracy. This approach enables deep neural networks to automatically learn complex fault signatures from large amounts of data and effectively detect and predict instrument failures in real-world applications. An experimental platform was set up in the LabView programming environment, equipped with 10 sensors, which were used to collect experimental data from the sensors and conduct fault monitoring experiments. The model shows high accuracy and robustness in different types of instrument fault detection, proving its effectiveness in practical industrial applications.
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