Smart Lifecycle Management Isn’t One-Size-Fits-All

Like the veracity of the 3,000-mile oil change, some people view maintenance schedules with skepticism. Today’s synthetic oils can keep car engines running perfectly well and safely for up to and beyond 5,000 miles between oil changes, but the old 3,000-mile standard still lingers.

One-size-fits-all maintenance standards really don’t fit at all. In regards to capital equipment, some organizations take a run-to-failure approach; others are fastidious about not only the repair process but also preventive maintenance.

A key to getting the most from the maintenance, or lifecycle management, of a robot (or any piece of equipment) is customizing lifecycle management to suit your operations. The cost of system upgrades, robot rebuilds or preventative maintenance may seem like money better spent elsewhere in a facility, unless you look critically at your specific situation and fully understand your cost of unplanned downtime.

It’s becoming easier to determine when downtime might occur through the development of predictive maintenance tools. Sensors and infrared imagers can now provide better information about the health of most machine’s components. Predictive analysis like this can help prevent unplanned downtime and reduce maintenance costs.  For example, analyzing the iron content of a robot’s grease will indicate how much its mechanical drives are wearing. As the iron content increases, the drives are approaching a point of failure. A lack of iron content may signify that the robot can be greased every 18 months instead of every 12 months.

Predictive tools are ushering in a smarter, more cost-effective way to undertake lifecycle management. Industry is moving from purely analytical tools to a hybrid set of devices that analyze and communicate with maintenance managers, providing remote monitoring. A simple example involves review of robot alarm data which includes information on the torque levels of various mechanical components. This information alerts the manager to the extent of the incident that signaled the alarm, as well as insight to forecasting the life expectancy of the robot’s components.

 Predictive tools, including remote monitoring capabilities, add value, but these tools have to fit a customer’s situation and their maintenance philosophy.

Yaskawa is in the process of launching technology that will help customers set up a network that links robots in a plant. By linking robots in a data-sharing network, managers will be able to understand the condition of their robots relative to other robots and determine if and when maintenance or a robot rebuild might be in order. With this type of arrangement, businesses have to ask themselves the following:  Do I capture data about my robots on a private network, or do I outsource this to a robot expert who becomes the OnStar® for the robot? When outsourcing, it is up to the customer to make the information accessible. It’s an internal company discussion, but I suggest companies set up a private network to collect the data. With the right set of data, savvy maintenance managers can calculate the cost of maintenance against unplanned downtime and factor in the value of system upgrades, robot rebuilds or preventative maintenance. Even more important than capturing the information, is having the process and practices in place to respond to the information.

When thinking about the value of lifecycle management for robots, don’t forget resale value. Much like used cars, there’s a market for previously owned robots. The robots that have been properly maintained and include well-documented maintenance records will bring a higher price.

If you haven’t already, analyze your robotics situation and cost of unplanned robot downtime. Customize your lifecycle management approach to fit your organization, or ask an expert to help.