As Industry 4.0 becomes a reality, companies that
Introducing more robots into the production environment, benefiting from increasingly
more efficient maintenance regimes. As with any asset, these
Critical machines operate at peak efficiency when operators have access
have to predictive maintenance tools. The latest predictive
analytics solution provided with Mitsubishi Electric industrial robots
uses artificial intelligence (AI) as a key feature
for optimization.
Both standard industrial and collaborative robots can
creating new opportunities for streamlining production and assembly operations.
However, robots, like other machines, require maintenance support to
to deliver optimal performance
Predictive maintenance only works with good data
To predict when a device is likely to fail
predictive maintenance algorithms process and analyze data that
have been collected from various sources, to create a model that is useful and
provides useful insight into the status of the robot.
AI systems are the most useful tools for identifying patterns
to recognize, make predictions and give practical advice about
take actions. By using a range of technologies, AI demonstrates a
unparalleled ability to process large amounts of data to discover patterns
in the data and generate predictive models.
These can help to reduce wear and consumption
to accurately calculate various robot parts or to identify trends
identify conditions that suggest a part is about to fail.
Examples of relevant information that predictive maintenance based on AI
can use, include the operating conditions of the machine, the
average life of components, the frequency of specific
robot motion patterns or real-time data from the drives.
The raw data obtained can be clear to the
AI system, with its ability to interpret the numbers, but the raw data
is not so easy for humans to understand. Visualization is therefore a
important aspect of AI-based predictive maintenance as it
information generated by the model in an accessible and clear manner
manner presents to production and maintenance employee.
This leads to knowledge that makes meaningful decisions and fast
enables actions without the need for specialized skills or
training in data mining. A direct result of this is efficient
maintenance schedules that maximize or intervene in equipment use
before any malfunctions occur.
Take the step towards maintenance 4.0
Those who use industrial robots from Mitsubishi Electric
users now have direct access to such solutions, because the
AI features are embedded in the latest MELFA SmartPlus software, for its
intelligent robots from the FR series. The system is built into the
robot controller and provides three main functions.
The calculation of the consumption rate determines when
robot parts, such as gears, bearings and belts, will likely need to
be replaced. When maintenance is required, the system can provide clear
send notifications.
The second function provides maintenance simulations. By the same
combining data used by the consumption model, it can
AI system estimates the robot's lifespan and provides a maintenance schedule
offer that optimizes maintenance costs and takes into account the
operating conditions and activities performed by the robot.
This feature allows end users to perform robot maintenance
understand, plan and optimize even before the machine is on the
factory floor is installed. This gives them the confidence that their
robot investment is worth it.
Finally, the AI system provides a centralized platform
for robot management. The data from SmartPlus can be loaded into multiple
cloud-based analytics solutions and will work with higher-level enterprise systems to
to combine their data with maintenance data from the robot controller. On
In this way, the solution can produce highly reliable predictive models.
Sometimes it's easy to forget that Industry 4.0 and Big Data is not only intensifying processes, but also supporting them to the maintenance activities. By AI-based predictive maintenance on By applying their robots, industries can increase efficiency and productivity of their automated systems to their full potential.
