Avoid Unplanned Downtime with Predictive Robot Data

Production success relies on manufacturing equipment as much as it does on employees.

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Imagine walking through your shop and hearing a voice coming from one of your robots saying, “Robot 5 will need new grease and batteries next Saturday, and please make sure you give it the good grease this time.” While this may sound a bit like science fiction, the truth is that such fortune-telling robots are here.  

Production success relies on manufacturing equipment as much as it does on employees. This equipment includes the robots shops rely on to perform their programmed workload. Today’s robot technology has become increasingly effective, providing flexible solutions to increase throughput, improve process quality and reduce application cycle times. Additionally, robots contribute to employee safety when tasked with handling dangerous and highly repetitive jobs. However, when a robot is not working, it can cause a critical breakdown in workflow. 

You may have heard the saying, “Take care of your car, and it will take care of you.” Similarly, an industrial robot (like most capital equipment) needs proper care to be a dependable contributor to your shop’s production. The good news is that robots require limited periodic maintenance, and they are becoming more self-aware.

To prevent unplanned downtime, look for robots that incorporate condition-based maintenance in addition to time-based maintenance that is scheduled at a specific interval of time. Condition-based maintenance uses information from the machine to help estimate when maintenance will be required. 

For example, grease can be analyzed on a new robot to determine what “new” looks like as well as for its metal content to help estimate the life expectancy of the robot’s working components. Other information like torque and vibration can also be captured and analyzed against baselines and control limits to help predict when periodic maintenance or overhauls will be required. Some robots also use predictive condition-based maintenance information to advise customers as to when it is appropriate to push back the expense and time of some periodic maintenance. 

Remote robot (and machine) monitoring ties in well with both condition- and time-based maintenance. It provides time and condition information that enables companies to maximize uptime and leverage robotic automation to be more competitive in the marketplace. 

Developments in remote monitoring include using the Web to capture the time- and condition-based information. With today’s robot generation, more condition-based maintenance information is increasingly being captured and reported from the robot in real time. Having access to this data is paramount to avoiding unplanned downtime. 

A typical manufacturing plant features many different types of equipment with rotating pieces, including fans, gears, pumps and motors. Predictive information based on speed and degree of imbalance of a rotating item can cause vibration that shakes the equipment. The extent and duration of vibration can eventually cause equipment failure. Knowing this extent or the tolerance of these rotating items to vibration can help predict when an item needs to be maintained, repaired or replaced. Robots are similar to these other types of manufacturing equipment in that motors are used to drive the movement of robotic arms. 

As the need for predictive information increases, OEMs will continue to make equipment “health” information available. Thankfully, machines will provide warnings and advisements based on available data, not on a crystal ball. So keep an ear open for fortune-telling robots asking for a little bit of TLC.

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