Edge Predictive Maintenance With Local Actuation for Equipment
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Solution Overview
Problem
Current predictive maintenance techniques for equipment in industries like semiconductor fabrication rely heavily on cloud computing, which leads to high latency, increased costs, and environmental concerns due to energy-intensive processing, and fail to provide real-time insights effectively, resulting in unplanned downtime and significant financial losses.
Innovation Solution
A system utilizing edge computing nodes that capture raw input data, process it locally to identify deviations, and perform automated actuations, while also training AI models through adaptive federated learning to predict equipment deterioration, thereby reducing reliance on cloud processing and minimizing data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud computing is used for processing raw input data, then computational power and storage capacity are improved, but data transmission latency and processing time increase
Solution Approach 1:
The system segments the centralized cloud computing architecture into distributed edge computing nodes deployed at multiple locations. Each edge node independently processes raw input data locally, eliminating the need for data transmission to centralized cloud data centers. This segmentation resolves the contradiction by providing sufficient computational power through distributed processing while eliminating data transmission latency entirely.
Solution Approach 2:
Edge computing nodes serve as intermediaries between data capture devices and the final analysis system. These intermediate processing points perform local data processing and filtering, reducing the need for continuous data transmission to cloud infrastructure. This intermediary approach maintains computational capabilities while minimizing transmission delays.
2Power
If cloud computing is used for predictive maintenance processing, then processing capability is improved, but processing costs increase
Solution Approach 1:
The system divides the processing workload across multiple distributed edge computing nodes rather than concentrating all processing in centralized cloud data centers. This segmentation allows each node to handle local processing tasks independently, reducing the need for energy-intensive data transmission and centralized processing infrastructure, thereby lowering overall processing costs while maintaining adequate processing capability.
Solution Approach 2:
Edge computing nodes perform self-service processing by autonomously analyzing raw input data locally without requiring continuous intervention from centralized cloud resources. This self-service capability eliminates the need for expensive, energy-intensive cloud processing for routine analytical tasks, reducing processing costs while maintaining sufficient processing capability through distributed intelligence.
3Power
If cloud computing is used for data processing, then computational resources are improved, but carbon emissions increase
Solution Approach 1:
By segmenting the centralized cloud computing model into distributed edge computing nodes, the system eliminates the need for large-scale, energy-intensive data centers. Each edge node uses local computational resources efficiently, dramatically reducing the carbon footprint associated with data transmission and centralized processing while maintaining adequate computational resources through distributed processing power.
Solution Approach 2:
The system converts the previously harmful effect of energy-intensive cloud data center operations into a beneficial distributed processing architecture. By deploying edge computing nodes throughout the network, the system transforms the need for centralized computational power into a distributed model that reduces energy consumption and carbon emissions while maintaining or improving processing efficiency.
4Measurement precision
If cloud-based predictive maintenance is implemented, then processing accuracy is improved, but real-time response capability deteriorates
Solution Approach 1:
The system segments the processing function across distributed edge computing nodes that are strategically positioned close to data sources. This segmentation enables local processing with high accuracy while eliminating data transmission delays, thereby achieving both high measurement precision and real-time response capability simultaneously.
Solution Approach 2:
Edge computing nodes perform preliminary data processing and analysis locally before any potential cloud interaction. This preliminary action ensures that critical real-time decisions are made with high accuracy based on local processing capabilities, eliminating the need to wait for cloud-based analysis and thereby achieving both processing accuracy and real-time response.
Data Source
AI summary
A system and method for facilitating predictive maintenance of an equipment is disclosed. The system may include a data capturer, a plurality of edge computing nodes and a cloud computing device. Each edge computing node may include a first processor. The cloud computing device may include a second processor. The first processor may receive the raw input data from the data capturer and may process the raw input data to obtain a representative data. The representative data may include an insight pertaining to a deviation in the at least one variable and a corresponding remedial action to be taken to correct the deviation. The deviation may be related to a deterioration in the condition of the equipment. The respective edge computing node may facilitate a regulation of the deviation by performing an automated actuation based on the corresponding remedial action.