Data collection of equipment in the production line of factories

Author:

Mohsen Shamsi

Category:

Articles

Study time: 5 minutes

Data collection from the equipment in the production line of factories is called a process during which data and information related to the performance of various equipment and machines in the production line are collected and analyzed. These data can include parameters such as temperature, pressure, speed, operating time, production rate, energy consumption, maintenance and repair status, and other information related to the efficiency and performance of the equipment.

Data collection is usually done using sensors, measuring devices, and automated data collection systems. This data is transferred to a database or monitoring system and then used to analyze and improve production processes, identify problems, predict failures, and optimize equipment performance.

Advantages of data collection from production line equipment:

Productivity increase: By analyzing data, production processes can be optimized and productivity can be increased.

Anticipate failures: Data capture helps in early detection of problems and predicts failures, which can reduce maintenance costs.

Improving product quality: With more accurate and better control of production processes, the quality of the final product is also improved.

Better energy management: By analyzing energy consumption, consumption can be optimized and related costs can be reduced.

Reduced downtime: By having more accurate information about the condition of the equipment, unexpected downtime can be reduced.

In general, data collection from production line equipment is one of the key tools in the implementation of intelligent production systems and Industry 4.0 factories.

Collecting data from equipment in the production line of factories is a key process for managing, optimizing and increasing productivity in industrial production. This process includes the collection, storage, analysis and use of data obtained from various equipment and machinery in production lines. In the following, we will discuss various aspects of this process in more detail.

Objectives of data collection from production line equipment:

A) Productivity improvement: Data acquisition allows factories to increase productivity by identifying bottlenecks and defects in production processes. This is done by analyzing the collected data and making optimal changes in the processes.

b) Reducing breakdowns and maintenance costs: Using data obtained from sensors and monitoring devices, factories can identify early signs of breakdowns. This data helps predict and schedule repairs, resulting in reduced maintenance costs and emergency downtime.

c) Increasing the quality of products: data collection of vital parameters such as temperature, pressure, speed and other environmental factors helps factories to maintain the quality of products stable and at the desired level. Analyzing these data helps to improve production processes and quality control.

d) Energy management: By analyzing energy consumption data, factories can optimize consumption patterns and reduce energy costs. This also helps to achieve sustainability goals and reduce environmental impact.

Data collection methods:

A) Use of sensors: One of the main methods of data collection is the use of different sensors that are installed at key points of machinery and equipment. These sensors can measure parameters such as temperature, pressure, flow, vibration and other physical factors.

b) Industrial control systems: Control systems such as PLCs (Programmable Logic Controllers) and DCSs (Distributed Control Systems) play an important role in data collection and processing in production lines. These systems can collect data from sensors and measuring devices and send them to management systems.

c) Industrial Internet of Things (IIoT): Using IIoT technologies, equipment can be connected to Internet networks and send data to data management systems in real-time. This enables factories to collect and analyze large amounts of data in real time.

Data collection tools and technologies:

A) SCADA systems: (Supervisory Control and Data Acquisition) or monitoring and data collection systems are important tools for industrial data management. These systems collect data from sensors and measuring devices and transfer it to a central database, from where the data can be analyzed and displayed.

b) Industrial databases: The data collected from the equipment are stored in industrial databases. These databases are typically kept in secure, high-access environments to protect data and provide fast and efficient access to it.

c) Data analysis software: after data collection, different software are used for data analysis. These softwares can help identify patterns, trends and possible problems. Software such as MATLAB, Python, or specialized industrial software such as OSIsoft PI or Wonderware are used for this purpose.

Applications of data collection:

A) Predictive Maintenance: Data analysis can help predict failures before they occur. By identifying failure patterns and using machine learning algorithms, optimal timing for maintenance and repairs can be determined.

b) Optimizing energy consumption: Analyzing energy data allows factories to manage and optimize energy consumption at different points of the production line. This helps to reduce operating costs and achieve sustainability goals.

c) Real-time quality control: continuous data collection of production processes allows real-time quality control. In this way, any deviation from the quality standards is immediately identified and corrected.

d) Improvement of production processes: Data analysis helps to identify opportunities for improvement in production processes. This includes reducing downtime, improving workflow, and optimizing resource utilization.

Data collection challenges and obstacles:

A) Complexity of technology: The use of data collection systems requires advanced technologies and specialized human resources. Correct installation and configuration of sensors, control systems, and analysis software requires high technical knowledge.

b) Data security: production data is sensitive and vital information for factories. Protecting this data against cyber attacks and unauthorized access is an important challenge.

c) Management of large volume of data: continuous data collection from equipment leads to the production of a huge volume of data. Managing these data, storing, and analyzing them requires advanced and powerful infrastructure.

d) Implementation costs: Implementing data acquisition systems requires significant initial investment, which may be challenging for some factories.

conclusion

Collecting data from the equipment in the production line is a vital process to optimize production and increase productivity in factories. Using advanced technologies and data analysis, this process helps factories to increase their competitiveness in the market by predicting problems, reducing costs, and improving quality. However, success in this field requires proper management of challenges and obstacles.