Machine data acquisition (MDA)

A wide range of process data and product data is relevant for machine data acquisition (MDA).
This data provides important insights into the operation of machines and production processes and enables efficient monitoring, analysis and optimization.
Here are some examples of relevant machine data:

Process

1. Operating parameters: Parameters that influence the operation of the machine, such as temperature, pressure, speed, flow rate, voltage and amperage.
2. Production times: Information on production cycles, cycle times, switch-on and switch-off times and downtimes due to maintenance or faults.
3. Machine condition: Data on the condition of the machine, including operating hours, wear condition of components, performance efficiency and availability.
4. Quality parameters: Measured values for product quality, such as dimensions, tolerances, surface quality, defects or faults.
5. Production parameters: Settings and configurations of the machine during the production process, e.g. cutting speed, filling pressure, material feed or tool change.
6. Material consumption: Materials consumed during the production process, including raw materials, semi-finished products, tools and lubricants.

Product data

1. Identification data: Unique identifiers for manufactured products, such as serial numbers, batch numbers or RFID tags.
2. Dimensional data: Measurements and dimensions of the manufactured parts or products, such as length, width, height, diameter and weight.
3. Quality data: Test data on product quality, such as dimensional deviations, tolerances, surface defects, breaking strength or material density.
4. Production history: Historical data on the manufacture of products, including date of manufacture, production line, machines used and materials used.
5. Production standards: Conformity data for compliance with production standards, norms, regulations or customer specifications.

MDC data is typically collected using sensors that are attached to the machines.
These sensors continuously record data, which is then transferred to a central software platform.
Alternatively, with newer machines, the machine data can be accessed directly via an interface to the PLC or via Rest API.

A wide range of sensors can be used in MDA systems to record various parameters, including temperature, pressure, vibration and humidity sensors. The choice of sensors depends on the specific requirements of the machines and processes to be monitored. The sensors can usually be retrofitted to the machine without interfering with the basic functionality of the machine.

In some cases, data collection can also be done manually by operators or technicians entering relevant information via an MDA terminal. This can be useful when automatic collection is not possible or not practical.

When manually collecting machine data with a MDA terminal, a variety of data can be entered to capture relevant information about the condition and performance of the machines. Some examples of data that can typically be collected via a MDA terminal are:

1. Production time: The total time required by a machine for production, including start and end times and downtimes.
2. Production quantities: The number of parts or products manufactured during a given period.
3. Quality inspections: Results of quality inspections, such as dimensional deviations, surface defects or other quality characteristics.
4. Maintenance and repair work: Information about maintenance or repair work carried out on the machine, including time, type of work carried out and spare parts used.
5. Material consumption: Materials consumed during the production process, including type and quantity of materials used.
6. Production parameters: Settings and parameters of the machine during the production process, such as temperature, pressure, speed or other operating parameters.
7. Operating states: Information about the operating status of the machine, such as switch-on and switch-off times, idle times or downtimes due to faults.
8. User comments: Notes or comments from operators or technicians about particular incidents, problems or comments during operation of the machine.

This data is used to gain a comprehensive overview of the machine’s operation, identify potential problems, perform performance analysis and make decisions to optimize the production process.
Manual collection also makes it possible to document information that may not be captured automatically, helping to improve overall efficiency and quality.

Analyzing machine data is a crucial step in the machine data acquisition (MDA) process.
By systematically evaluating and interpreting the collected data, valuable insights can be gained that enable companies to optimize their production processes, reduce costs, improve product quality and increase efficiency.
Here are some important aspects of analyzing machine data:

1. Identification of patterns and trends:

• • By analyzing historical machine data, patterns and trends can be identified that may indicate recurring problems, seasonal fluctuations or long-term changes.
  • Recognizing trends can enable companies to react to changes at an early stage and take proactive measures to avoid potential problems.

2. Detection of anomalies and deviations:

• • Analyzing machine data enables the detection of anomalies and deviations from normal operating conditions.
  • By identifying deviations in good time, companies can anticipate potential problems or failures and take corrective action to minimize downtime.

3. Performance monitoring and optimization:

• • The analysis of performance indicators enables companies to monitor and optimize the performance of their machines and production processes.
  • By identifying bottlenecks, inefficient processes or resources that are not being used optimally, companies can recognize potential for improvement and take measures to increase efficiency.

4. Prediction of maintenance requirements:

• • By analyzing machine data, companies can monitor the condition of their machines and predict maintenance requirements.
  • Predictive maintenance enables companies to plan maintenance work proactively and minimize downtime by responding to the actual needs of the machines.

5. Continuous improvement:

• • Analyzing machine data provides valuable information that enables companies to implement continuous improvement measures.
  • By comparing performance data over a certain period of time, companies can evaluate the success of their optimization measures and set new targets for the future.

6. Data visualization and reporting:

• • The visualization of machine data in the form of graphics, diagrams or dashboards facilitates the interpretation and communication of results.
  • Reporting functions enable companies to pass on important findings and recommendations for action to relevant stakeholders (e.g. machine operators of the respective machine, production managers, management) and make well-founded decisions.

The analysis of machine data is therefore an indispensable part of a holistic approach to optimizing production processes and increasing the competitiveness of companies.

Te process of implementing a machine data acquisition (MDA) system requires careful planning, implementation and training to ensure that the system is effectively integrated into existing production processes and delivers the desired results. Here is a step-by-step guide to the process of implementing an MDA system:

1. Needs analysis and objectives:

• • Identification of the specific requirements and objectives for the introduction of the MDA system.
  • Clarification of expectations regarding increased efficiency, cost savings, quality improvement, etc.

2. Selection of the MDA system:

• • Evaluation of various MDA systems on the market in terms of their functions, flexibility, scalability, user-friendliness and costs.
  • Selection of the appropriate system that best meets the company’s requirements.

3. Planning and preparation:

• • Definition of an implementation schedule with clear milestones and responsibilities.
  • Provision of resources, including financial resources, technical support and training for employees.

4. System integration:

• • Setting up the hardware components, including sensors, MDA terminals and network infrastructure.
  • Installation and configuration of the software on the relevant systems.
  • Integration and setup of data exchange with other systems such as Manufacturing Execution Systems (MES) or Enterprise Resource Planning (ERP) for holistic data analysis.

5. Training of employees:

• • Training employees in the use of the MDA system, including data entry, system navigation, troubleshooting and safety guidelines.
  • Training of management and executives in the use of the analysis tools and the interpretation of the collected data.

6. Test phase and optimization:

• • Carrying out tests and pilot runs to check the functionality and performance of the MDA system.
  • Identification of weak points or optimization potential and implementation of corresponding adjustments.

7. Rollout and implementation:

• • Gradual introduction of the MDA system into production operations, starting with selected machines or production lines.
  • Monitoring and support during the implementation phase to ensure that the system functions smoothly and is accepted.

8. Continuous improvement and maintenance:

• • Establish a regular maintenance and support plan to ensure the proper functioning of the MDA system.
  • Collecting feedback from users and stakeholders to identify opportunities for improvement and future development needs.

9. Monitoring and evaluation:

• • Continuous monitoring of the performance of the MDA system in terms of its impact on productivity, efficiency, quality and costs.
  • Regular evaluation of target achievement and adjustment of the system accordingly.

By carefully following this process, the introduction of an MDA system can be successful and create sustainable added value for the company by optimizing production processes and strengthening competitiveness.