Predictive Maintenance
Enhancing Maintenance Efficiency
through Early Detection
Our predictive maintenance solution automates the monitoring process by continuously collecting measurement data from wireless vibration sensors. It integrates measurement history with real-time vibration analysis to constantly monitor and assess machinery health. This automatic surveillance ensures that all data captured is fully utilized for maximum predictive accuracy.
Early detection of potential issues facilitates timely repairs and maintenance planning. When the backend system identifies a potential problem, maintenance personnel have sufficient time to decide when to perform corrective actions. Using historical data, the system can be trained to recognize these issues early, which saves on maintenance resources such as time and money.
Tracking Key Metrics
Our IoT Monitor system employs advanced techniques to proactively detect potential issues in machinery before they develop into serious failures. It continuously monitors key metrics including temperature, RMS, Crest, Kurtosis, P2P, and Z2P, issuing alerts when fault criteria are met. The system also detects anomalies by identifying changes in vibration patterns within the FFT (Fast Fourier Transform) frequency spectrum. This approach is particularly effective for diagnosing common problems in rotating machines such as unbalance, misalignment, and bearing faults, which are determined based on widely researched knowledge.
Utilizing Data History
Based on historical data and the positive impacts of maintenance actions, the system can be set to enhance its precision in detecting issues. For instance, after lubricating a bearing, there is typically a notable reduction in vibration levels initially. By analyzing such changes, the system can be configured to gauge the effectiveness of the maintenance performed and improve its predictive capabilities. This approach enables the system to forecast the optimal timing for the next lubrication more accurately, thereby preventing unnecessary wear and potential bearing failure.
