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Case: Waste Recycling
Case: Recycling Plant
To ensure maximum uptime and enable reliable predictive maintenance, a waste recycling plant has adopted advanced condition monitoring technologies for its critical machinery.
A complete online wireless condition monitoring system with triaxial vibration sensors is installed on the bearings of key equipment. These sensors are battery-powered using MAX long-life batteries, offering up to 8 years of operation, and feature magnetic bases for quick and easy installation.
In parallel, current signature analysis (ESA) is performed on the equipment’s electric motors. All sensor data is collected locally at the Edge on the on-premise iQunet server. The iQunet AI service, equipped with Anomaly Detection, runs continuously on all sensors, providing early and sensitive warnings of potential failures.
Read the full story regarding setup and detailed results in the complete case study. (split in 3 parts)
SUMMARY
- Plant type: Waste treatment
- Asset type: ballistic separators, waste conveyors
- Critical parts being monitored: critical bearings on drive axes of multiple machines
- Installed iQunet monitoring:
- 3-AX iQunet wireless vibration sensors
- Battery powered
- Magnet base
- ESA iQunet current clamps on motor phases (elec. cabinet)
- Clamp over IE current transformers
- Wireless bridge
- iQunet server to collect all wireless data from all sensors
- Installed AI monitoring (on real sensor produced data)
- Analytic dashboards
- 3-AX iQunet wireless vibration sensors
During the last few days the Anomaly Monitor Dashboard shows an upwards trend. An alarm is raised.
A steep upward trend requires immediate action where a rather flat oriented trendline should require close follow up and inspection of the asset.
Once the anomaly score multiplies in a few days, it is time to maintain the asset and plan for replacement parts and service.
Conclusion
In conclusion, the implementation of triaxial vibration sensors and current signature monitoring (ESA) at the waste recycling plant has significantly enhanced the predictive maintenance capabilities for critical equipment bearings and electrical motors. The use of long-life battery-powered sensors with magnetic bases ensures easy installation and longevity, while the iQunet server and AI service provide real-time data collection and anomaly detection. The system’s ability to monitor trends and detect anomalies allows for timely interventions, thereby reducing the risk of equipment failure and optimizing maintenance schedules. The recent upward trend observed by the Anomaly Monitor underscores the system’s effectiveness in identifying potential issues early, enabling proactive maintenance actions to maintain operational efficiency and minimize downtime. The combination of advanced monitoring technologies and AI-driven analytics ensures that the plant can operate smoothly and sustainably, with minimal interruptions.