Big Data

» What is big data?
» How does having a lot of data improve the efficiency of industrial processes?
» What benefits does the use of big data offer in industrial automation?
» What technologies are involved in collecting big data?
» How does having a lot of data improve the safety of industrial processes?
» When you collect so much data, what then?
» Big data and Ignition
» Explore Ignition

What is big data?

Big data refers to an enormous amount of data produced by human interaction and machine activity. These data originate from various sources such as social media, sensors, mobile devices, machines, and computer systems. It’s not just about the volume of data but also about the velocity and variety of the generated and collected data. Big data involves a plethora of advanced software tools and technologies such as machine learning and data mining to discover patterns and trends and generate valuable insights for decision-making and business process optimization.

In the context of industrial automation, big data refers to the vast amount of data generated by automated production processes, machines, and sensors. This data includes operational data such as temperature, pressure, and speed, as well as log data such as errors, warnings, and alarms. Big data technologies collect and analyze this data to provide insights into the functioning of industrial processes and to improve the efficiency, reliability, and safety of production processes.

How does having a lot of data improve the efficiency of industrial processes?

By monitoring data from machines and sensors, companies can predict potential breakdowns or maintenance issues before they actually occur. This reduces unplanned downtime as well as costs. Additionally, it improves the efficiency of the production process.

It can also be used to optimize the production line through analyses of data such as machine output, energy consumption, and quality control. Based on these analyses, companies can improve and adjust processes to increase efficiency and productivity.

The use of big data also enables companies to identify and resolve quality issues in production processes, thereby improving efficiency at lower costs.

Data analytics helps companies optimize the supply chain by predicting demand patterns, improving logistics, and optimizing inventory management. Additionally, it can also be used to monitor the energy consumption of production processes, allowing companies to save costs and reduce their ecological footprint.

What benefits does the use of big data offer in industrial automation?

• Improved operational efficiency: By monitoring the performance of machines and equipment, companies can identify problems early and intervene quickly to reduce downtime and production loss.
• Increased productivity: By collecting and analyzing data on production processes, inefficiencies and bottlenecks can be identified and resolved to increase productivity.
• Enhanced product quality: By collecting data on the quality of raw materials, production processes, and end products, companies can identify and address quality issues to improve product quality.
• Better decision-making: Big data provides companies with valuable insights into their production processes and performance. This leads to better decision-making and strategies based on factual data and insights.
• Cost savings: By using big data, companies can operate more efficiently, increase productivity, and improve quality. This results in cost savings.
• Enhanced safety: Companies use big data to collect and analyze data on the safety of the production environment. This enables them to identify and reduce safety risks.

What technologies are involved in collecting big data?

There are many technologies that can be used for collecting big data. Som examples include:

Sensors
Sensors are essential for collecting data from machines and equipment. There are various types of sensors available, including temperature sensors, pressure sensors, flow sensors, and vibration sensors.

IoT (Internet of Things)
IoT is a network of connected devices that collect and share data. In industrial automation, it includes smart sensors, smart machines, and smart equipment that collect and share data.

Cloud computing
Cloud computing is a technique that stores and processes large amounts of data. This includes the use of cloud-based big data solutions such as Amazon Web Services, Microsoft Azure, and Google Cloud.

Artificial Intelligence (AI) en Machine Learning
AI and Machine Learning are used to develop predictive models based on the collected data. These models can be used to make predictions about future events and to predict maintenance needs.

Data lakes
These are large storage repositories to integrate and store data from various sources for later use in analytics.

Data analysis tools
These tools enable companies to identify patterns and trends in data and develop predictive models.

Hoe does having a lot of data improve the safety of industrial processes?

The use of sensors in factories and other work environments to collect data on machines and equipment helps prevent accidents. Big data identifies potential defects and issues, thus taking proactive measures to ensure the safety of employees. Furthermore, big data-based solutions also collect data on the environment, such as air quality, sound levels, and temperature, to improve workplace safety.

Another way big data contributes to workplace safety is through predictive maintenance. Using this technology, machine and equipment performance can be monitored in real-time to determine when maintenance is needed. By performing maintenance in advance, you can prevent problems and reduce the risk of accidents.

Big data can also be used to identify and analyze safety risks. By collecting and analyzing data on accidents, incidents, and other safety events, big data helps determine the best solutions and improvements in safety policies and work processes.

Finally, it can be used to monitor and analyze employee performance. This allows companies to determine what training and education are needed to improve safety performance. In this way, big data contributes to a safer work environment for all employees.

When you collect so much data, what then?

Collecting all the data is just the first step in using big data in industrial automation. After data collection, it is essential to process and analyze it to obtain valuable insights. This involves the following steps:

1. Clean the data by eliminating or correcting incorrect, incomplete, or ambiguous data.
2. Since big data often comes from various sources and formats, integrate the data into a uniform format to get a complete picture of the data.
3. Apply various data analysis techniques to identify patterns and trends. These analyses may include machine learning models, statistical analyses, or pattern recognition algorithms.
4. Present the analysis results in a clear and understandable manner using data visualization tools.
5. Interpret the identified patterns and trends and use them to make decisions and take action to improve processes and increase efficiency.

Big data and Ignition

Ignition is a software platform for industrial automation and HMI/SCADA applications that can be used for collecting and analyzing big data. Ignition utilizes an open architecture that allows for data collection from various devices and systems, regardless of brand or vendor. This enables the platform to easily gather data from different machines, sensors, and systems in the factory and integrate them into a uniform format.

Ignition also provides various advanced analytics tools for big data, including machine learning, data visualization, and advanced reporting features. These tools can be used to identify trends and patterns and gain valuable insights into equipment performance, production lines, and factories.

Another advantage of Ignition is its scalability. The platform can be used for both small factories and production lines as well as large, complex manufacturing environments that generate vast amounts of data.