1  Fundamental Theory of Deep Learning and Machine Vision

Deep learning belongs to machine learning technology, which is based on artificial neural networks and constructs multi-layer neural network models. It can automatically learn complex patterns and feature representations from massive data. A neural network is composed of numerous interconnected layers of neurons, and data is transmitted between layers to complete feature extraction and transformation. Its deep structure enables the model to learn advanced abstract features, resulting in significant achievements in fields such as image and speech recognition.

Machine vision uses optical devices and computer algorithms to simulate the human visual system. With the help of cameras, image information is collected, and through image processing algorithms such as filtering, enhancement, and segmentation, interesting features are extracted to achieve tasks such as perception, measurement, recognition, analysis, classification, localization, and defect detection of target objects.

2、 Architecture Design of Mechanical Equipment Fault Prediction and Intelligent Operation and Maintenance Platform

The mechanical equipment fault prediction and intelligent operation and maintenance platform adopts a layered architecture to ensure efficient and stable operation. The specific architectures are as follows:

Data collection layer: Located at the bottom layer, like the "tentacles" of the platform, it collects physical quantities and operational status information such as temperature and vibration from multiple sources such as equipment sensors and monitoring devices, accurately obtaining raw data.Data processing and analysis layer: located in the middle, it receives data from the collection layer, preprocesses it first, removes noise, fills in missing values and standardizes them, and then uses deep learning and machine vision algorithms to mine and analyze, construct fault prediction models, and undertake the "brain" responsibility of intelligent data processing.Application display layer: At the top level, a visual interface is used to present fault prediction, equipment health assessment, and operation and maintenance suggestions to management personnel, maintenance personnel, etc., helping them to timely understand the equipment status and make decisions. Collaborate at all levels to achieve platform functionality.

3、 Mechanical equipment fault prediction model based on deep learning and machine vision

The data for predicting mechanical equipment faults is complex and dynamic, and recurrent neural networks (RNNs) and their improved versions of long short-term memory networks (LSTMs) are commonly used. RNN can handle time series data, but there are issues with vanishing or exploding gradients. LSTM introduces gate control mechanism to solve the long-term dependency problem and performs well in fault prediction. To meet the specific equipment failure prediction requirements, the model needs to be improved. For example, by combining attention mechanisms, the model can focus on key time step features when processing sequence data, enhancing the capture of fault sensitive features. At the same time, based on the distribution of different fault data, optimize the network structure, such as adjusting the number and layers of hidden layer neurons, improving the model fitting and generalization ability, and ensuring accurate fault prediction under complex working conditions.

4  Conclusion

The research focuses on deep learning and machine vision, from theoretical foundations to platform architecture, model construction, and implementation and application, to conduct research on mechanical equipment fault prediction and intelligent operation and maintenance platform. This platform will significantly improve the efficiency and reliability of mechanical equipment operation and maintenance, and is expected to be further optimized in the future, exerting greater value in more industrial scenarios.

reference:

[1] Geng Weitao Mechanical surface defect detection based on deep learning [J]. Automation Applications, 2024, 65 (13): 173-175