Fault Detection Diagnostics

AI-powered early fault detection and diagnostics to reduce downtime and improve safety

Business impact

• Operational uptime — Increases by preventing unexpected equipment failures through early fault detection
• Safety incidents — Decreases by identifying faults before they cause hazardous conditions
• Maintenance costs — Reduces by enabling predictive maintenance and avoiding emergency repairs
• Fault detection rate — Improves through AI models analyzing complex sensor data for anomalies
• Response time to faults — Shortens by automating fault alerts and diagnostics for faster action

Data requirements

• Sensor telemetry data (Numeric) — Provides real-time measurements for anomaly detection and fault diagnosis
• Image and video feeds (Image) — Used for visual fault detection via computer vision models
• Operational logs (Text) — Contains historical fault and maintenance records for model training
• Battery charge-discharge cycles (Numeric) — Time-series data used to predict battery degradation and faults
• 3D printer camera images (Image) — Real-time visual data to detect printing defects without extra hardware

AI methods and techniques

• Predictive AI — Forecasts faults before they occur using historical and real-time data patterns
• Generative AI — Simulates fault scenarios and generates diagnostic insights for complex systems
• Symbolic AI — Incorporates domain knowledge and rules to interpret fault causes and explanations

AI models and model families

LSTM, Deep Neural Networks, Convolutional Neural Networks, Graph Neural Networks, BERT-style time-series models

Industries

Real-world evidence

11 documented case studies on record.

Companies using this: Above Surveying, Credit Karma, Elektron Motors, Euler, Gridspertise, IMDEA Materials Institute, LG Energy Solution, Lyse Energy, NTT Data, R Space Systems, University Texas.