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Last updated on 2024年8月28日

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DCS

What are the best practices for DCS data exchange and standardization?

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A distributed control system (DCS) is a network of controllers, sensors, actuators, and communication devices that manage and regulate industrial processes. A DCS can improve efficiency, reliability, safety, and quality of operations, but it also poses challenges for data exchange and standardization. How can you ensure that your DCS data is consistent, accurate, and accessible across different platforms, vendors, and protocols? Here are some best practices for DCS data exchange and standardization that can help you optimize your DCS performance and integration.

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Zohaib Jahan

18k+ | DeltaV DCS Engineer | TüV SIS, PLC Programming, Automation | Empowering Industry Transformation and Achieve…
Umar Saeed

Instrument & Control Systems Specialist|14 years experience|Project Management

1 Define your data requirements

Before you start exchanging data between your DCS and other systems, you need to define your data requirements clearly and comprehensively. What data do you need to collect, store, analyze, and share? What are the sources, formats, frequencies, and destinations of your data? What are the quality, security, and compliance standards for your data? By defining your data requirements, you can identify the gaps, risks, and opportunities for improvement in your data exchange and standardization process.

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2 Adopt common data models

One of the main challenges for DCS data exchange and standardization is the diversity of data models and schemas used by different DCS vendors, devices, and protocols. To overcome this challenge, you need to adopt common data models that can facilitate the interoperability and compatibility of your DCS data. Common data models are standardized representations of data elements, attributes, and relationships that can be shared and understood by different systems. Some examples of common data models for DCS are OPC UA, ISA-95, and EDDL.

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3 Use middleware and gateways

Another challenge for DCS data exchange and standardization is the complexity and heterogeneity of the communication networks and protocols involved in a DCS. To simplify and streamline the data exchange and standardization process, you need to use middleware and gateways that can act as intermediaries and translators between your DCS and other systems. Middleware and gateways are software or hardware components that can connect, convert, and route data between different networks, protocols, and formats. Some examples of middleware and gateways for DCS are OPC servers, MQTT brokers, and protocol converters.

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4 Implement data validation and cleansing

Even if you have defined your data requirements, adopted common data models, and used middleware and gateways, you still need to ensure that your DCS data is valid and clean before you exchange and standardize it. Data validation and cleansing are techniques that can detect, correct, and remove errors, inconsistencies, and anomalies in your DCS data. Data validation and cleansing can improve the accuracy, completeness, and integrity of your DCS data, as well as reduce the risks of data loss, corruption, or misuse.

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Umar Saeed

Instrument & Control Systems Specialist|14 years experience|Project Management
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In an era of data-analytics, DCS data-base has gained a lot of importance. Most DCS manufacturers offer built-in tools for data validation and integrity checks within the process automation environment. These mainly include features like input validation, range checks and real time data quality monitoring. Data consistency checks, real time diagnostics and data validation functions become even more important if there is a DCS integration via OPC to 3rd party apps or clients. Most DCS Historians provide data validation and capabilities within its data historian environment ensuring that the long term storage is accuracy and consistent.

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5 Apply data governance and management

The final best practice for DCS data exchange and standardization is to apply data governance and management principles and practices to your DCS data. Data governance and management are frameworks and processes that can define the roles, responsibilities, policies, and procedures for the creation, collection, storage, access, use, and disposal of your DCS data. Data governance and management can help you ensure that your DCS data is aligned with your business objectives, compliant with relevant regulations, and protected from unauthorized or malicious access.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Zohaib Jahan

18k+ | DeltaV DCS Engineer | TüV SIS, PLC Programming, Automation | Empowering Industry Transformation and Achieve Excellence
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Quite interesting topic, I will briefly add 3 points on DCS Data Exchange: 1) Define clear data requirements and adopt consistent information modeling across DCS. 2) Utilize OPC UA (Open Platform Communications Unified Architecture) for interoperability and leverage its compliance for reliable data exchange. 3) Implement data validation and governance following ISA-95 to ensure data quality and integrity.

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What are the best practices for DCS data exchange and standardization?

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1 Define your data requirements

2 Adopt common data models

3 Use middleware and gateways

4 Implement data validation and cleansing

5 Apply data governance and management

6 Here’s what else to consider

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