Predictive Maintenance

Use AI to predict equipment failures and optimize maintenance schedules proactively

Business impact

• Asset uptime — Increases by predicting failures early and scheduling maintenance proactively
• Maintenance costs — Decrease due to fewer emergency repairs and optimized resource allocation
• Operational efficiency — Improves by reducing unexpected breakdowns and streamlining maintenance workflows
• Downtime reduction — Minimized through early fault detection and timely interventions
• Safety incidents — Reduced by preventing hazardous equipment failures before they occur

Data requirements

• IoT sensor data (Numeric) — Provides real-time monitoring of equipment conditions like vibration and temperature
• Historical maintenance logs (Structured) — Used to train models on past failure patterns and maintenance outcomes
• Operational performance data (Numeric) — Captures machine usage and load to contextualize sensor readings
• Acoustic and thermal imaging (Image, Audio) — Detects anomalies not visible in numeric data, such as overheating or unusual sounds
• Satellite and geospatial data (Image) — Supports remote monitoring of large assets and infrastructure for early fault detection

AI methods and techniques

• Predictive AI — Analyzes historical and real-time data to forecast equipment failures and remaining useful life
• Generative AI — Accelerates data analysis and generates diagnostic insights for maintenance decision support
• Agentic AI — Autonomously manages maintenance workflows and tool selection for proactive interventions

AI models and model families

GPT-4o, Claude, Llama, Custom ML models, Neural networks, Random forests

Industries

Real-world evidence

13 documented case studies on record.

Companies using this: Air France, BMW, Deloitte, GE, Korean Air, NTT Data, Rolls Royce, Saipem Sp A, Schneider Electric, Siemens AG, Space42, Tata Elxsi.