Predictive Maintenance for Machines Using n8n and AI

This workflow automates the process of predicting machine downtime by leveraging AI and natural language processing within n8n. It begins with a webhook trigger, which receives data related to machine status or issues. The data is then split into manageable chunks using a text splitter, and embeddings are generated via OpenAI’s API to capture semantic information. These embeddings are stored in a Weaviate vector database, enabling efficient similarity searches.

When a prediction query is initiated, the workflow retrieves relevant data from the vector store, using a question as input. AI tools and language models, including Anthropic’s API, are employed to analyze the retrieved information, generate insights, and assess whether a machine might experience downtime. The results, including predictions and relevant details, are appended to a Google Sheet for logging and further analysis.

This automation is particularly useful for industrial environments where proactive maintenance can minimize costly downtime by predicting failures before they occur. It seamlessly integrates AI-driven analysis with real-time data collection and logging, making maintenance operations smarter and more predictive.