Bently Nevada 330180-X0-00 Proximitor Sensor

Product Overview

The 330180-X0-00 serves as a Proximitor sensor within the Bently Nevada 3300 XL system. This signal conditioner converts eddy current probe input into a proportional voltage output. Consequently, it delivers accurate shaft vibration and position data for rotating machinery. Power plants and refineries depend on this module for continuous machine protection.

Technical Specifications

Parameter	Value
Model	330180-X0-00
Series	3300 XL
Function	Proximitor / Signal Conditioner
Compatible Probes	3300 XL 5 mm and 8 mm
Output Sensitivity	7.87 mV/µm (200 mV/mil)
Linear Range	2 mm (5 mm probe)
Supply Voltage	-17.5 Vdc to -26 Vdc
Output Impedance	50 Ω nominal
Operating Temperature	-35°C to +85°C
Storage Temperature	-40°C to +100°C
Mounting	DIN rail or panel mount
Connector	3-pin coaxial
Weight	Approximately 255 g
Dimensions	100 mm × 75 mm × 25 mm
Frequency Response	0 to 10 kHz
Isolation	Galvanically isolated

Key Features and Interfaces

Furthermore, the 330180-X0-00 provides a 3-pin coaxial interface for probe and extension cable connection. Its galvanic isolation blocks ground loops that distort measurement signals. In addition, the module accepts standard 3300 XL probes without adapter hardware. Therefore, technicians replace units quickly during brief maintenance windows.

Installation and Compatibility

Moreover, this Proximitor pairs directly with 3300 XL extension cables and proximity probes. The DIN rail mount simplifies cabinet layout inside crowded control rooms. Because the unit draws minimal supply current, it suits high-density monitoring racks. Subsequently, engineers integrate it into 3500 series systems and legacy 3300 installations alike.

Why Choose This Module

Additionally, the 330180-X0-00 meets API 670 requirements for vibration monitoring chains. Its wide operating temperature supports placement near hot turbine casings. Meanwhile, the rugged housing resists vibration, moisture, and electromagnetic interference. As a result, maintenance teams gain dependable data for predictive maintenance strategies.