The Bently Nevada 3500/92 is a key component of the 3500 Series Monitoring System, specifically designed for machinery protection and condition monitoring in various industrial applications.

Product Name:

Bently Nevada 3500/92-04-01-00 Module
Part Number: 136180-01

Product Description:

The 3500/92 module is a dynamic monitoring module that provides real-time data collection, processing, and reporting on machinery performance and health. This module is part of the Bently Nevada 3500 series, which is recognized for its advanced diagnostic capabilities in monitoring rotary machinery such as turbines, compressors, pumps, and other critical machinery.

Product Parameters:

• Input Channels: Depending on the configuration, can handle multiple channels for vibration, thrust, and other parameters.
• Signal Processing: High-performance digital signal processing algorithms for reliable and accurate data analysis.
• Multi-Sensor Support: Compatible with various types of sensors (e.g., proximity probes, accelerometers).
• Diagnostics: Provides comprehensive diagnostic capabilities, allowing for predictive maintenance.

Specifications:

• Power Supply: Typically operates on a DC power supply (exact values depend on specific module configurations).
• Temperature Range: Designed to operate in standard industrial temperature ranges.
• Protection Rating: Usually has IP ratings for dust and moisture protection, suitable for industrial environments.
• Dimensions: Standard form factor to fit into the 3500 rack, typically around 5.2 inches wide.
• Weight: Approximately 1-2 lbs (exact weight may vary).
• Communication: Supports communication with the 3500 series rack and the System 1 software for advanced analysis and reporting.

Product Use:

The Bently Nevada 3500/92 module is utilized in a variety of industrial applications for:

• Machinery Protection: Monitoring critical parameters to protect against failure due to vibration, imbalance, misalignment, etc.
• Condition Monitoring: Continuous monitoring of equipment to analyze performance trends and detect anomalies.
• Predictive Maintenance: Enabling timely maintenance actions based on real-time and historical performance data to prevent unplanned outages.

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