The Bently Nevada 200350-00-00 is a rugged, case-mounted seismic accelerometer. It features a stainless steel housing, 100 mV/g sensitivity, and a MIL-C-5015 connector for reliable vibration monitoring in Trendmaster Pro systems.
The Bently Nevada 200350-00-00 serves as a high-performance seismic transducer for industrial machinery monitoring. This general-purpose accelerometer measures vibration levels by detecting casing acceleration. Its hermetically sealed stainless steel housing ensures reliable operation in extreme conditions. The design specifically resists moisture and chemical exposure in heavy industrial zones.
| Attribute | Details |
| Manufacturer | Bently Nevada (Baker Hughes) |
| Model Number | 200350-00-00 |
| Transducer Type | Piezoelectric Accelerometer |
| Sensitivity | 10.2 mV/m/s² (100 mV/g) ±5% |
| Connector Type | 2-pin MIL-C-5015 |
| Mounting Thread | 1/4-28 UNF |
| Weight | 0.05 kg (Transducer only) |
| System Origin | United States |
The 200350-00-00 utilizes a piezoelectric sensing element to capture mechanical vibration data. This internal sensor generates a voltage signal proportional to the acceleration along its primary axis. The device maintains high signal integrity even over long cable runs. Furthermore, the top-mounted MIL connector simplifies cable attachment during tight installation window. A standard threaded base allows for secure coupling to diverse machinery surfaces.
This accelerometer integrates seamlessly with the Trendmaster Pro condition monitoring platform. It works directly with the 149811-02 Constant Current Input Card. The sensor also supports the 164746-01 Seismic Direct Input Card. These combinations provide a complete path for predictive maintenance data. The transducer performs optimally when calibrated at 100 Hz within standard thermal ranges.
Reliability makes this sensor ideal for continuous vibration tracking on rotating equipment. It monitors motors, pumps, and small fans in power plants and refineries. The rugged build quality supports use in cooling tower environments. Maintenance teams use the resulting data to identify bearing wear and imbalance early. Consequently, this leads to reduced repair costs and less unplanned downtime.
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