This n8n workflow is designed to automatically detect potential sensor faults using AI and machine learning. It begins with a webhook trigger that receives sensor data, then processes and analyzes the data to identify anomalies or faults, and finally logs the results into a Google Sheet for review.
Step-by-step, the workflow works as follows:
1. The process is initiated through a POST webhook at the ‘sensor_fault_detector’ endpoint, which receives data from sensors.
2. The incoming data is sent to a ‘Splitter’ node that divides the data into manageable chunks for processing.
3. The data chunks are then transformed into text using the ‘TextSplitterCharacterTextSplitter’ node.
4. The text data is embedded into vector representations via the ‘Embeddings’ node using OpenAI’s API, enabling semantic search and similarity analysis.
5. These vectors are inserted into a ‘vector store’ (Supabase) for storage.
6. When a new data point arrives, a ‘Query’ retrieves relevant vectors for comparison.
7. The retrieved vectors are processed through a custom ‘Tool’ node that interfaces with the vector database.
8. The workflow maintains contextual information using a ‘Memory’ buffer.
9. The core analysis is performed by a language model (‘Chat’ node) hosted on HuggingFace, which evaluates whether the sensor data indicates a fault.
10. The analysis results are encapsulated by an ‘Agent’ node, which defines the decision logic.
11. Finally, the outcomes, including fault detections, are logged into a Google Sheet for historical analysis and troubleshooting.
This workflow is particularly useful in industrial or IoT environments where real-time sensor monitoring and fault detection are crucial for maintaining system reliability and safety, enabling quick responses to potential issues.
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