Product Overview
The 330100-50-05 operates as a proximity sensor inside the Bently Nevada 3300 series. This non-contact transducer measures shaft vibration and displacement in rotating machinery. Consequently, it supports steam turbines, compressors, and centrifugal pumps across power generation facilities. Industrial plants depend on this sensor for continuous condition monitoring.
Technical Specifications
| Parameter | Value |
|---|---|
| Model | 330100-50-05 |
| Series | 3300 |
| Function | Proximity Sensor / Transducer |
| Probe Diameter | 5 mm |
| Linear Range | 2 mm |
| Output Sensitivity | 7.87 mV/µm (200 mV/mil) |
| Total Length | 0.5 m (50 = 50 cm) |
| Thread Type | 3/8-24 UNF |
| Connector | 3-pin coaxial |
| Operating Temperature | -35°C to +177°C |
| Frequency Response | 0 to 10 kHz |
| Weight | Approximately 90 g |
| Cable Type | FEP-insulated, armored |
| Mounting | Case-isolated, reverse mount |
| Supply Voltage | -18 Vdc to -24 Vdc |
| Isolation | Galvanically isolated |
Key Features and Interfaces
Furthermore, the 330100-50-05 offers a 3-pin coaxial interface for extension cable connection. Its compact 5 mm probe tip suits installations with tight radial clearance. In addition, the armored FEP cable resists abrasion, moisture, and chemical exposure. Therefore, technicians install it quickly inside crowded turbine housings.
Installation and Compatibility
Moreover, this sensor pairs directly with 3300 Proximitor modules and extension cables. The case-isolated design blocks ground loops that corrupt measurement signals. Because the unit operates across a wide temperature range, it handles hot casings and cold outdoor sites. Subsequently, engineers deploy it for radial vibration and axial position measurements.
Why Choose This Sensor
Additionally, the 330100-50-05 meets API 670 requirements for vibration monitoring chains. Its rugged construction withstands vibration, moisture, and electromagnetic interference. Meanwhile, the factory calibration removes field adjustment steps and shortens commissioning time. As a result, maintenance teams gain dependable data for predictive maintenance programs.
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