The Bently Nevada 330103-00-11-10-02-00 belongs to the 3300 XL 8mm series, representing the core technology for high-accuracy vibration monitoring. This non-contact proximity probe measures the distance between the sensor tip and a rotating shaft. Because it utilizes eddy current principles, the probe can track both dynamic shaft vibration and static axial position. This data remains essential for identifying mechanical imbalances or bearing wear in high-speed industrial assets.
Advanced Engineering for Severe Environments
This transducer thrives where standard sensors often fail. The 330103-00-11-10-02-00 features a polyphenylene sulfide (PPS) tip that resists chemical degradation and moisture ingress. Furthermore, the stainless steel body protects internal electronics from mechanical stress. The design specifically mitigates the impact of electromagnetic interference, ensuring signal purity for the monitoring system. Consequently, plant operators can trust these readings in the high-noise environments typical of power plants and refineries.
Technical Configuration and Specifications
This specific part number defines a standardized probe with specialized cable and thread lengths for industrial mounting.
- Model Number: 330103-00-11-10-02-00
- System Type: 3300 XL 8mm Proximity Transducer
- Mounting Thread: 3/8-24 UNF-2A
- Unthreaded Length: 1.1 inches
- Overall Case Length: 2.1 inches
- Total Cable Length: 1.0 meter (3.3 feet)
- Connector Type: Miniature coaxial ClickLoc (gold-plated)
- Operating Range: 2.0 mm (80 mils) typical
- Standard Compatibility: 3300 XL Proximitor and 3500 Monitoring Systems
Industrial Applications and Machine Protection
Facilities worldwide use this probe on critical rotating equipment such as steam turbines, centrifugal compressors, and gas expanders. The sensor integrates directly with the Bently Nevada 3500 series machinery protection system. This connection facilitates automated trip logic and real-time alerts. By detecting shaft movement at a sub-millimeter level, the probe prevents catastrophic damage. Maintenance teams use this insight to move from reactive repairs to a data-driven predictive maintenance strategy.
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