Predictive Maintenance IoT Solutions
About Savannah Cuthbert
Commonly used in industrial IoT (IIoT) applications, predictive maintenance is a proactive maintenance strategy instilled to prolong the life of equipment while avoiding system failures. Through the combination of IoT sensors, data collection and analysis, and machine learning, assets can be effectively monitored both remotely and in real-time.
Condition-Based Maintenance vs. Predictive Maintenance vs. Prescriptive Maintenance
Unlike condition-based maintenance that only acts when an anomaly is identified–predictive maintenance takes asset monitoring a step further by utilizing algorithms to predict the optimal time of maintenance while determining resolutions in the event of a failure. An even more complex maintenance strategy is prescriptive maintenance. This system leverages AI to detect anomalies even earlier, all while interpreting data to make smart decisions working to prolong the life of industrial equipment.
Three Common Predictive Maintenance Methods
Working in conjunction with IoT sensors is a computerized maintenance management system (CMMS). A CMMS stores equipment data analytics and facilitates maintenance procedures. Whether it is for agriculture, manufacturing, or construction–IIoT applications vary in the sensor data that is required to ensure long-term performance. The following are three common types of predictive maintenance approaches:
- Vibrational Analysis
- Thermographic Analysis / Infrared Analysis
- Acoustical Analysis (Sonic & Ultrasonic)
Predictive Maintenance IoT Sensor Solutions
According to Market Research Future’s Predictive Maintenance Market Research Report, the global predictive maintenance market is expected to grow to 23B by 2025. Symmetry Electronics offers a broad portfolio of IoT sensors and Applications Engineers that can help determine optimal solutions for diverse predictive maintenance use cases. Having predictive maintenance measures in place can prolong the life of equipment, all while reducing repair and labor costs.