3500/22 146031-02 Belonging to the Bently Nevada 3500 series

Product model: BENTLY 3500/22 146031-02
Working voltage: 24VDC

Working temperature: -40 ℃~85 ℃

Working humidity: 0~95%

Interface type: RS-485
BENTLY 3500/22 146031-02 is a mechanical vibration monitoring system used to monitor the vibration level of rotating machinery. It can detect bearing vibration, mechanical imbalance, and other mechanical problems to ensure the normal operation of mechanical equipment

  • Email:sauldcsplc@outlook.com
  • Phone:+86 18350224834
  • WhatsApp:+8618350224834

Description

3500/22 146031-02 Belonging to the Bently Nevada 3500 series

3500/22 146031-02 Product Introduction
  • BENTLY 3500/22 146031-02 gateway is designed with multi protocol compatibility and modularity, seamlessly connecting industrial systems, 
  • operating at a wide temperature range of -40 ° C to 85 ° C, supporting 8-channel rotating equipment monitoring, 
  • remote configuration to improve efficiency, and ensuring stable power and automation.
1、 Product Overview
BENTLY 3500/22 146031-02 is part of the Bently Nevada 3500 series monitoring system and is a communication gateway module designed specifically for industrial automation and process control applications.
It can connect the Bently Nevada 3500 system with other control systems or host computers to achieve data interconnectivity.
2、 Main characteristics
Communication interface: This module has rich communication interfaces and supports multiple communication protocols such as Ethernet, RS232, RS485, etc. It can be compatible with protocols such as Modbus and TCP/IP,
thus achieving integration with other industrial control systems.
input/output signal
Input signal: 0-10V DC, 4-20mA DC.
Output signal: 0-10V DC, 4-20mA DC.
Channel Count: Provides 8 analog input and 8 analog output channels to meet various monitoring and control requirements.
Working parameters:
Working voltage: 24VDC.
Working temperature range: -40 ° C to+85 ° C (some sources mention -30 ° C to+65 ° C or -40 ° C to+70 ° C, depending on the application environment).
Working humidity: 0% to 95%.
Remote configuration and management: Supports remote configuration and management functions, allowing for remote system settings and monitoring through the network, improving operational efficiency.
Modular design: Adopting a modular design, it is easy to install and maintain, reducing system downtime and maintenance costs.
High reliability and stability: After rigorous testing and validation, it ensures stable performance in various harsh environments, suitable for multiple fields such as industrial automation, process control, mechanical vibration monitoring and protection.
Contact Us
 
Mobile phone: 18350224834
 
E-mail: sauldcsplc@outlook.com
 
WhatsApp:+86 18350224834

(2) Data collection and traceability issues. Data collection issues often occur, and many assembly lines lack “end-to-end traceability.” In other words, there are often no unique identifiers associated with the parts and processing steps being produced. One workaround is to use a timestamp instead of an identifier. Another situation involves an incomplete data set. In this case, omit incomplete information parts or instances from the forecast and analysis, or use some estimation method (after consulting with manufacturing experts).(3) A large number of features. Different from the data sets in traditional data mining, the features observed in manufacturing analysis may be thousands. Care must therefore be taken to avoid that machine learning algorithms can only work with reduced datasets (i.e. datasets with a small number of features).(4) Multicollinearity, when products pass through the assembly line, different measurement methods are taken at different stations in the production process. Some of these measurements can be highly correlated, however many machine learning and data mining algorithm properties are independent of each other, and multicollinearity issues should be carefully studied for the proposed analysis method.(5) Classification imbalance problem, where there is a huge imbalance between good and bad parts (or scrap, that is, parts that do not pass quality control testing). Ratios may range from 9:1 to even lower than 99,000,000:1. It is difficult to distinguish good parts from scrap using standard classification techniques, so several methods for handling class imbalance have been proposed and applied to manufacturing analysis [8].(6) Non-stationary data, the underlying manufacturing process may change due to various factors such as changes in suppliers or operators and calibration deviations in machines. There is therefore a need to apply more robust methods to the non-stationary nature of the data. (7) Models can be difficult to interpret, and production and quality control engineers need to understand the analytical solutions that inform process or design changes. Otherwise the generated recommendations and decisions may be ignored.

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