Industrial Predictive Maintenance System with AI on Edge
Overview
Tech4Biz Solutions developed an Industrial Predictive Maintenance System using the Qualcomm QCS605 chipset, designed to monitor machine health through vibration and temperature data. The system leverages AI-based predictive analytics and anomaly detection to identify potential machine failures before they occur, reducing downtime and enhancing operational efficiency in manufacturing and industrial automation environments.
Project Background
Unplanned downtime in industrial machinery can lead to significant financial losses and production delays. Traditional reactive maintenance methods often fail to prevent failures, while scheduled preventive maintenance can be costly and inefficient. To address this challenge, Tech4Biz Solutions aimed to create an edge-based predictive maintenance solution capable of real-time monitoring and intelligent failure prediction.
Objective
The primary objectives of the project were to:
● Develop an edge-based device to monitor vibration and temperature in industrial machines
● Use AI-based predictive analytics to forecast potential machine failures
● Detect anomalies in machine behavior
● Provide cloud-based alerts for remote monitoring
● Improve machine uptime and reduce maintenance costs
Solution Architecture
Hardware Design
● Chipset: Qualcomm QCS605 with integrated AI Engine
● Sensors: High-precision vibration and temperature sensors
● Connectivity: Wi-Fi, Bluetooth, and Ethernet
● Power Supply: Low-power design for continuous operation
● Storage: Onboard eMMC with cloud synchronization capabilities
AI Features
● Predictive Analytics: Time-series forecasting using machine learning models
● Anomaly Detection: Detects deviations from normal operating patterns
● Failure Prediction: Provides early warnings for potential machine breakdowns
● Cloud-Based Alerts: Automatic notifications sent to maintenance teams
Software Stack
● AI Models: TensorFlow Lite optimized for Qualcomm Neural Processing Engine (NPE)
● Edge Processing: Qualcomm AI Engine for real-time inference
● Cloud Integration: AWS IoT Core for remote data storage and alerts
● Mobile App: Android and iOS app for system monitoring and alert management
Implementation
● The hardware was designed using Altium Designer.
● AI models were trained using vibration and temperature datasets collected from industrial machines.
● The firmware was developed using Qualcomm Vision Intelligence Platform SDK.
● The system was deployed in 25 industrial sites for pilot testing.
Results
● Achieved 90% accuracy in predicting machine failures
● Reduced unplanned downtime by 40%
● Improved maintenance scheduling efficiency by 30%
● Positive feedback from maintenance teams on early warning capabilities
Key Benefits
● Proactive failure prediction with minimal false alarms
● Edge-based AI processing without cloud dependency
● Energy-efficient design for 24/7 operation
● Secure data transmission with AES-256 encryption
● Scalable architecture for various industrial machines
Conclusion
Tech4Biz Solutions’ Industrial Predictive Maintenance System provides a highly reliable solution for reducing unplanned downtime and improving operational efficiency in manufacturing environments. By leveraging Qualcomm’s edge AI capabilities, the system delivers accurate and timely predictions without relying heavily on cloud infrastructure, making it a cost-effective and scalable solution for industrial automation.
Future Roadmap
● Integration of AI-based root cause analysis
● Expansion to multi-sensor data fusion
● Customizable machine learning models for different machine types
● Global rollout with industrial partners
● Cloud-based dashboard for predictive insights and remote management