Predictive Maintenance
Aelius Venture delivers AI-powered Predictive Maintenance (PdM) solutions that help you anticipate equipment failures, reduce downtime, and optimize asset performance.
Get a PdM ConsultationOur Predictive Maintenance Services
We provide end-to-end solutions to help you harness the power of your operational data.
Connecting industrial assets with sensors (vibration, thermal, acoustic) to collect the real-time data needed for predictive analysis.
Building custom machine learning models that analyze sensor data to detect anomalies and predict equipment failures with high accuracy.
Analyzing historical maintenance data to identify failure patterns and develop models that predict the remaining useful life (RUL) of assets.
Integrating predictive alerts directly into your EAM or CMMS to automatically generate work orders and streamline maintenance planning.
Why Aelius Venture for PdM?
Predictive maintenance is more than just an algorithm; it requires a deep understanding of your physical assets, data infrastructure, and operational workflows.
Reduce Unplanned Downtime
Our solutions help you move from a reactive to a proactive maintenance strategy, preventing costly unplanned outages and production losses.
Optimize Maintenance Costs
By performing maintenance only when it's needed, you can reduce unnecessary preventative maintenance tasks and optimize spare parts inventory.
Improve Asset Lifespan
Proactively addressing potential issues before they become major failures helps you extend the operational life of your critical equipment.
Deep Industrial Expertise
We combine data science expertise with a deep understanding of industrial assets and maintenance processes to deliver solutions that work in the real world.
Proven Success in Asset Performance
See how we've helped industrial companies reduce downtime.
The Challenge:
An automotive manufacturer was experiencing frequent, unpredicted failures of robotic arms on their assembly line, causing costly production stoppages.
Our Solution:
Aelius Venture retrofitted the robotic arms with vibration and acoustic sensors. We developed an edge computing solution to analyze sensor data in real-time and an AI model to detect subtle anomalies that preceded a failure, sending alerts to the maintenance team 72 hours in advance.
The Outcome:
The solution led to an 80% reduction in unplanned downtime on the assembly line and a 30% reduction in maintenance costs, saving the manufacturer over $2 million annually per plant.