Predictive Maintenance Excellence with MongoDB Atlas

Solution Overview

MongoDB Atlas powers an end-to-end predictive maintenance solution that helps manufacturers prevent equipment failures and optimize maintenance operations through four strategic stages:

1. Machine prioritization and criticality analysis

   • Addresses the question: "Which machine should I prioritize for predictive maintenance and why?"

   • Uses machine learning and RAG-based analysis to prioritize critical equipment.

   • Leverages historical data and expert knowledge to make informed decisions.

2. Failure prediction

   • Answers the critical question: "What is the root cause of imminent failure?"

   • Processes real-time sensor data through Atlas Stream Processing.

   • Enables early detection of potential failures before they occur.

3. Maintenance plan generation

   • Focuses on: "How should I schedule the repair procedure?"

   • Automatically generates detailed repair plans using large language models.

   • Combines maintenance manuals, inventory data, and resource information.

4. Maintenance guidance generation

   • Addresses: "How do I get better guidance on fixing machines?"

   • Provides enhanced maintenance guidance by integrating service notes and repair instructions.

   • Delivers instructions directly to technicians' mobile devices through Change Streams.

Figure 1: Four stages of predictive maintenance workflow

By leveraging MongoDB's unified data platform capabilities like vector search and real-time analytics, organizations can achieve significant operational improvements: reducing downtime by 15-20%, increasing labor productivity by 5-20%, and cutting maintenance costs by 30-60%.

Reference Architectures

Machine Prioritization Architecture

This architecture leverages RAG (retrieval-augmented generation) to determine which machines require predictive maintenance. The system processes two types of input data:

• Structured data: Production parameters and machine breakdown frequency.

• Unstructured data: Institutional knowledge in documents.

The workflow aggregates and operationalizes both data types as vector embeddings in MongoDB Atlas. Using Vector Search, it performs semantic search to provide relevant context to an LLM (via Amazon Bedrock or Cohere in this case), which generates contextual responses to prioritization queries. This helps maintenance teams make data-driven decisions about which machines need attention first.

Figure 2. AI system diagram for machine prioritization recommendations

Sensor Data Processing Architecture

This real-time architecture processes machine sensor data through six key stages:

1. Data collection: A prioritized milling machine with DAQ (data acquisition) captures critical metrics (product type, temperature, speed, torque, tool wear).

2. Stream processing: Real-time transformation of raw sensor data.

3. Data storage: Centralized storage in MongoDB Atlas with single view capability.

4. Change detection: Monitoring for significant data changes.

5. ML inference: Running trained models to predict potential failures.

6. Dual output: Visualization through Atlas Charts and mobile notifications via Change Streams.

Figure 3. Real-time sensor monitoring with MongoDB Atlas

Work Order Generation Architecture

This architecture automates maintenance work order creation through:

1. Document processing: Machine manuals and old work orders are chunked and converted to vectors using AWS/Cohere embedding models.

2. Vector storage: Embeddings stored in MongoDB Atlas.

3. Work order generation: A specialized app that:

   • Uses LLMs to generate appropriate work order templates.

   • Pulls inventory and resource information through aggregation.

   • Creates detailed repair plans based on machine documentation.

Figure 4. AI-powered work order generation system diagram

Maintenance Guidance Architecture

This architecture enhances operator instructions through a RAG approach:

1. Service note processing: Converts multilingual PDF service notes to text.

2. Translation: Processes non-English content (Spanish in this case) through translation models.

3. Instruction generation: Combines translated service notes with original repair plans using LLMs.

4. Delivery: Provides updated maintenance instructions to technicians through a mobile app.

Figure 5. RAG workflow enhances technician repair instructions

Each architecture integrates with MongoDB Atlas core capabilities while leveraging external services (Amazon Bedrock, OpenAI, Cohere) for AI/ML functionality, creating a comprehensive predictive maintenance solution.

Building the Solution

For complete implementation details, including code samples, configuration files, and tutorial videos, visit the GitHub repository. This repository provides a production-ready template for implementing predictive maintenance using MongoDB Atlas' comprehensive feature set.

Key Learnings

• MongoDB Atlas provides a unified platform for predictive maintenance by combining structured sensor data and unstructured maintenance documents, enabling both real-time monitoring and AI-powered analysis through a single-view architecture.

• The solution leverages a four-stage approach (prioritization, prediction, plan generation, guidance delivery) that integrates multiple MongoDB features including Atlas Stream Processing for real-time data, Vector Search for semantic analysis, and Change Streams for mobile alerts.

• Organizations can achieve significant operational improvements through this approach: 15-20% reduction in downtime, 5-20% increase in labor productivity, and 30-60% reduction in maintenance costs.

• The implementation combines multiple AI technologies (RAG, LLMs, ML models) with MongoDB's developer data platform capabilities to create an automated maintenance workflow—from machine prioritization to mobile repair guidance delivery.

Technologies and Products Used

Authors

• Dr. Humza Akhtar, MongoDB

• Rami Pinto, MongoDB

• Sebastian Rojas Arbulu, MongoDB