Bently Nevada 330103-00-10-10-02-00 Probe

Product Overview

The 330103-00-10-10-02-00 functions as an 8 mm proximity probe within the Bently Nevada 3300 XL series. This non-contact transducer measures shaft vibration, position, and speed on rotating machinery. Consequently, it supports steam turbines, compressors, and large motors across power plants. Industrial operators depend on this probe for continuous condition monitoring.

Technical Specifications

Parameter	Value
Model	330103-00-10-10-02-00
Series	3300 XL
Function	8 mm Proximity Probe
Probe Tip Diameter	8 mm
Linear Range	2 mm (80 mil)
Output Sensitivity	7.87 mV/µm (200 mV/mil)
Total Length	1.0 m (10 = 100 cm)
Cable Length	1.0 m
Thread Type	3/8-24 UNF
Connector	ClickLoc coaxial, 3-pin
Operating Temperature	-35°C to +177°C
Frequency Response	0 to 10 kHz
Weight	Approximately 120 g
Cable Type	FEP-insulated, stainless steel armor
Mounting	Case-isolated, reverse mount
Supply Voltage	-17.5 Vdc to -26 Vdc

Key Features and Interfaces

Furthermore, the 330103-00-10-10-02-00 offers a ClickLoc coaxial connector for tool-free cable attachment. Its 8 mm probe tip suits standard clearance installations on medium and large machines. 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 probe pairs directly with 3300 XL extension cables and Proximitor sensors. 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, axial position, and keyphasor measurements.

Why Choose This Probe

Additionally, the 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.