Bently Nevada 330500-02-00 Velomitor Sensor

Product Overview

The 330500-02-00 operates as a Velomitor piezo-velocity sensor within the Bently Nevada 3300 series. This solid-state device measures absolute casing vibration on rotating machinery. Consequently, it delivers reliable velocity data for steam turbines, compressors, and pumps. Power plants and refineries depend on this sensor for continuous machine protection.

Technical Specifications

Parameter	Value
Model	330500-02-00
Series	3300 Velomitor
Function	Piezo-velocity Sensor
Sensitivity	3.94 mV/mm/s (100 mV/in/s)
Frequency Range	4.5 Hz to 1 kHz
Amplitude Range	0 to 1,270 mm/s peak
Supply Voltage	-18 Vdc to -24 Vdc
Output Impedance	100 Ω nominal
Operating Temperature	-35°C to +120°C
Storage Temperature	-40°C to +125°C
Mounting	1/4-28 UNF stud, case-isolated
Connector	2-pin MIL-style
Weight	Approximately 145 g
Dimensions	30 mm × 58 mm
Housing Material	Stainless steel
Isolation	Galvanically isolated

Key Features and Interfaces

Furthermore, the 330500-02-00 provides a 2-pin MIL-style connector for direct cable attachment. Its stainless steel housing withstands harsh plant environments and corrosive atmospheres. In addition, the case-isolated design blocks ground loops that distort vibration signals. Therefore, technicians mount it quickly on bearing housings and machine casings.

Installation and Compatibility

Moreover, this sensor pairs directly with 3300 monitoring systems and extension cables. The 1/4-28 UNF stud mount suits standard tapped holes on industrial machinery. Because the unit requires no external signal conditioner, it simplifies wiring inside crowded cabinets. Subsequently, engineers deploy it for absolute casing vibration and bearing monitoring.

Why Choose This Sensor

Additionally, the 330500-02-00 meets API 670 requirements for vibration monitoring chains. Its wide frequency range captures both low-speed and high-speed machine faults. Meanwhile, the rugged construction resists vibration, moisture, and electromagnetic interference. As a result, maintenance teams gain dependable data for predictive maintenance strategies.