3500-53 Mechanical vibration monitoring system

Product model: BENTLY 3500-53
Working voltage: 24VDC

Working temperature: -40 ℃~85 ℃

Working humidity: 0~95%

Interface type: RS-485
BENTLY 3500-53 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

3500-53 Mechanical vibration monitoring system

3500-53 Product Introduction
  • BENTLY 3500-53 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 3500-53 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

3.2 Machine learningAs the functionality of distributed computing tools such as Spark MLLib (http://spark.apache.org/mllib) and SparkR (http://spark.apache.org/docs/latest/index.html) increases, it becomes It is easier to implement distributed and online machine learning models, such as support vector machines, gradient boosting trees and decision trees for large amounts of data. Test the impact of different machine parameters and process measurements on overall product quality, from correlation analysis to analysis of variance and chi-square hypothesis testing to help determine the impact of individual measurements on product quality. This design trains some classification and regression models that can distinguish parts that pass quality control from parts that do not. The trained models can be used to infer decision rules. According to the highest purity rule, purity is defined as Nb/N, where N is the number of products that satisfy the rule and Nb is the total number of defective or bad parts that satisfy the rule.Although these models can identify linear and nonlinear relationships between variables, they do not represent causal relationships. Causality is critical to determining the true root cause, using Bayesian causal models to infer causality across all data.3.3 VisualizationA visualization platform for collecting big data is crucial. The main challenge faced by engineers is not having a clear and comprehensive overview of the complete manufacturing process. Such an overview will help them make decisions and assess their status before any adverse events occur. Descriptive analytics uses tools such as Tableau (www.tableau.com) and Microsoft BI (https://powerbi.microsoft.com/en-us) to help achieve this. Descriptive analysis includes many views such as histograms, bivariate plots, and correlation plots. In addition to visual statistical descriptions, a clear visual interface should be provided for all predictive models. All measurements affecting specific quality parameters can be visualized and the data on the backend can be filtered by time.

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