Predictive Maintenance Solutions

Condition-based monitoring (CbM) is defined as a predictive maintenance strategy that continuously monitors the condition of assets using different types of sensors and uses the data extracted from sensors to monitor assets in real time. The collected data can help manufacturers increase throughput and asset utilization by reducing maintenance costs and asset downtime. CbM can be used to establish trends, predict failure, calculate the lifetime of an asset, and increase safety in manufacturing plants.

Analog Devices’ deep domain knowledge across sensing, signal processing, communications, power management, and system design considerations, combined with our AI sensing and interpreting platform at the edge, enables our customers to deploy new condition monitoring solutions faster and extract more value, with access to higher quality data and insights. Our complete, system-level solutions provide the technology and insights to create new, high value, predictive maintenance service offerings for deployed equipment.

Explore Applications in Predictive Maintenance Solutions

Wired Asset Health Monitoring Solutions
Wireless Asset Health Monitoring Solutions

Wired Asset Health Monitoring Solutions

Wired asset health monitoring solutions with robust communications interfaces and precision sensing.

Wireless Asset Health Monitoring Solutions

Robust wireless solutions that allow for more flexible deployment of asset health monitors.

Wired Asset Health Monitoring Solutions

Wired Asset Health Monitoring Solutions

Wired asset health monitoring solutions with robust communications interfaces and precision sensing.

Wireless Asset Health Monitoring Solutions

Wireless Asset Health Monitoring Solutions

Robust wireless solutions that allow for more flexible deployment of asset health monitors.

CbM Development Platforms Accelerate Time to Market

Developing accurate, reliable condition-monitoring solutions for industrial assets requires a combination of technologies and design considerations to capture and convert critical signals into actionable insights. MEMS inertial, temperature, and magnetic field, along with supporting signal chains provide accurate and reliable data. Our open-source, embedded software carefully samples and processes signals to ensure sensor data is optimized for critical decision making. Real-time anomaly and event detection algorithms enhance condition-based monitoring solutions and provide a deeper understanding of overall machine health, helping you make actionable insights. Optimized mechanical mounting for condition monitoring solutions ensures that early defect signatures can be extracted from the sensor solution.