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MIRADA has been involved in automation since 2008 and mainly serves customers in the field of industry and energy management. In 2017, the company developed and expanded strongly in the area of IoT. Several successful projects in this area have already been implemented and strong knowledge has been built up within the company. With SCADA solutions, the company also develops large and sophisticated solutions. Alliances with manufacturers such as BECKHOFF, COPA-DATA and B&R strengthen our position in this area.

sps tirol

SPS

PLC systems
robotic programing
manufacturing processing

IoT Tirol

IoT

condition monitoring
predictive maintenance
cloudbaste smart-metering

SCADA HMI tirol

SCADA

contorl system
graphical user interfaces
process supervisory managment

Condition Monitoring

Condition Monitoring (colloquially CM) Describes the process of monitoring a condition parameter in a plant or machine.
For example, vibration, temperature, air quality, etc. can be automated to detect significant change that indicates a developing fault. It is an essential component of predictive maintenance. The use of condition monitoring makes it possible to schedule maintenance or take other actions to prevent consequential damage and avoid its consequences. Condition monitoring has the unique advantage that conditions that would shorten normal service life can be addressed before they develop into a major failure. Condition monitoring techniques are typically used on rotating equipment, auxiliary systems and other machinery (compressors, pumps, electric motors, internal combustion engines, presses), while periodic inspection using non-destructive testing (NDT) techniques and fitness-for-service (FFS)[1] assessments are used for static plant equipment such as boilers, piping and heat exchangers.

 

Manufacturing Processing

Process manufacturing is a branch of manufacturing associated with formulas and manufacturing recipes and can be contrasted with discrete manufacturing, which deals with discrete units, bills of materials, and component assembly. Process manufacturing is common in the food, beverage, chemical, pharmaceutical, nutraceutical, consumer goods, cannabis and biotechnology industries. Just like the products that they produce, discrete and process manufacturing software have different focal points and solve different problems. For the same reason that the proverbial square peg does not fit in the round hole, software geared toward discrete, or even hybrid manufacturing will not work smoothly in a process manufacturing setting. With process manufacturing, the end-product is unable to be broken down to its original ingredients, for example beer or pasta sauce. Thus, the software must be able to account for these intricacies in its ability to convert and transform raw materials to finished goods. Critical aspects such as recipe formulation, forward and backward lot traceability, handling of mixed units of measure and conversion, raw material calculations, and scalable batch tickets with revision tracking and recording of manufacturing steps and production notes are specific to process manufacturers and key functionality of process manufacturing software.

SCADA

Supervisory Control and Data Acquisition (SCADA) is a control system architecture that includes computers, networked data communications, and graphical user interfaces (GUI) for supervisory process control, but also includes other peripherals such as programmable logic controllers (PLCs) and discrete proportional-integral-derivative (PID) controllers to interface with process equipment or machinery. The use of SCADA has also been considered for the management and operation of project-driven processes in the construction industry.

The operator interfaces that allow monitoring and issuing of process commands, such as controller setpoint changes, are handled by the SCADA computer system. The lower-level operations, such as real-time control logic or controller calculations, are performed by networked modules connected to the field sensors and actuators.

The SCADA concept is designed to provide universal remote access to a variety of local control modules, which may be from different manufacturers and allow access via standard automation protocols. In practice, large SCADA systems have evolved that are very similar in function to distributed control systems, but use multiple means of interfacing with the plant. They can control large-scale processes that may include multiple sites, and operate over both large and small distances. It is one of the most commonly used types of industrial control systems, despite concerns that SCADA systems are vulnerable to cyberwarfare/cyberterrorism attacks.

Alarming and HMI

In the industrial design field of human-computer interaction, a user interface (UI) is the space in which interactions between humans and machines take place. The goal of this interaction is to enable effective operation and control of the machine by the human, while at the same time the machine feeds back information that supports the operator’s decision-making process. Examples of this broad concept of user interfaces include the interactive aspects of computer operating systems, hand tools, heavy machinery controls, and process controls. The design considerations involved in creating user interfaces are related to or involve disciplines such as ergonomics and psychology.

In general, the goal of user interface design is to create a user interface that makes it easy, efficient, and pleasant (user-friendly) to operate a machine in a way that achieves the desired result (i.e., maximum usability). This generally means that the operator must make minimal inputs to achieve the desired result, and that the machine also minimizes unwanted outputs to the user.

Predictive maintenance

Predictive maintenance techniques are designed to help determine the condition of assets in operation in order to estimate when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted. Therefore, it is considered condition-based maintenance, which is performed based on estimates of an item’s degradation state.
The main promise of predictive maintenance is to enable convenient planning of corrective maintenance and prevent unexpected equipment failures. The key is „proper information about equipment life, increased plant safety, fewer accidents with negative environmental impact, and optimized spare parts handling.
Predictive maintenance differs from preventive maintenance in that it relies on the actual condition of the equipment, rather than average or expected life statistics, to predict when maintenance is needed. Typically, machine learning approaches are adopted to define the actual condition of the equipment and predict its future states.

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