As an AI language model, I do not have personal experience or context with respect to industry practices. However, I can provide an overview of general considerations industrial architects may take into account when addressing the needs of machine learning in maintenance and repair in their designs.
Industrial architects may incorporate machine learning into their designs in the following ways:
1. Data collection and storage: In order to train machine learning models, large amounts of data need to be collected and stored. Architects may design data collection systems and data storage facilities that ensure data integrity, accessibility, and security.
2. Sensor networks: Architects may design sensor networks that collect data on equipment performance and condition. These sensor networks could be connected to machine learning models for predictive maintenance and anomaly detection.
3. Communication networks: Industrial architectures may design communication networks that enable real-time data transfer between sensors, equipment, and central systems. These networks would be designed to handle large amounts of data in real-time.
4. Machine learning models: Architects may design algorithms that enable predictive maintenance and repair by processing data collected from sensors and equipment. These models may also generate alerts and recommendations for maintenance and repair.
5. Human-machine interfaces: Industrial architects may design user interfaces that enable technicians to interact with machine learning algorithms. This would allow technicians to review recommendations for maintenance and repair and provide feedback on the effectiveness of the algorithms.
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