The 9571-32 Cable Bently Nevada is a specialized signal conditioning module designed for integration with velocity transducer systems, featuring a 32-foot cable length option for flexible installation in various industrial settings. This module accepts input from one non-contacting 3300 NSv Proximity Probe and its matching extension cable, operating on a +12 to +35 Vdc power supply at the transmitter terminal. The Bently Nevada Standard Interconnect Cables Bently Nevada 9571-32 supports a maximum loop resistance of 1,000 Ω including cable at 35 Vdc, ensuring stable signal transmission across extended distances.

As part of the Seismoprobe velocity transducer systems, the 3500 System Cable 9571-32 Bently Nevada measures absolute vibration relative to free space on bearing housings, casings, or structural components. The two-wire system utilizes moving-coil technology that generates a voltage output directly proportional to vibration velocity, providing accurate measurement without requiring external power. This makes the module particularly suitable for portable measurement applications and environments where simplicity and reliability are paramount.

The Sensor Proximity Probe Cable Bently Nevada 9571-32 employs two twisted pairs of 22 AWG conductors with polyurethane (PUR) jacket insulation, offering durability and resistance to industrial contaminants. With typical capacitance of ≤100 pF/m, the module maintains signal integrity even in electrically noisy environments. The moving-coil design provides inherent advantages including reduced sensitivity to impact or impulsive excitation compared to solid-state alternatives.

Engineered for machinery protection applications, the 9571-32 Interconnect Cable Bently Nevada represents an optimal choice for applications where traditional acceleration-based measurements may be less effective. Its self-powered operation and robust construction make it ideal for continuous monitoring in power generation, manufacturing, and heavy industrial applications where reliable vibration data is essential for predictive maintenance and equipment protection strategies.