Sensor Type:Vibration
Sensing Range:0.1 to 10 mm Peak-peak
Sensitivity:0.1 um/Pa
Output Type:4-20 mA
Installation Type:Non-contact
Operating Temperature:-40°C to +85°C
Package/Casing:Stainless Steel Enclosure
Material Coding:316L Stainless Steel
Characteristic:IP67 Waterproof
Termination Type:Gold-plated Connector
Minimum Number of Packages:1
Application Areas:Rotating Equipment Monitoring
Item Number:004
Certified Product:ISO 9001:2015
Series:990-05-XX-01-00
Model:MOD:147202-01
The Bently Nevada 990-05-XX-01-00 MOD:147202-01 Vibration Transmitter is a state-of-the-art solution for critical machinery monitoring in industrial settings. Its robust design ensures reliable operation under harsh conditions, providing accurate real-time vibration data to optimize maintenance schedules and enhance operational efficiency.
Featuring a wide sensing range and high sensitivity, this transmitter is capable of detecting even subtle changes in vibration patterns, which are crucial indicators of impending equipment failures. This early warning system allows for proactive maintenance, reducing unexpected downtime and maintenance costs.
With its IP65-rated metal enclosure, the unit is protected against dust ingress and can withstand light water exposure, making it suitable for a variety of industrial applications where environmental conditions may vary. The terminal block termination facilitates easy integration into existing control systems, streamlining installation processes.
The Bently Nevada 990-05-XX-01-00 MOD:147202-01 is backed by comprehensive documentation and support, ensuring seamless integration and long-term reliability. It is designed to work seamlessly with other Bently Nevada products, offering a cohesive solution for comprehensive industrial monitoring.
Ideal for use in power generation facilities, petrochemical plants, and manufacturing industries, this vibration transmitter plays a pivotal role in maintaining operational safety and efficiency. It is an indispensable tool for engineers and maintenance teams looking to enhance their predictive maintenance strategies.
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