Predict Your Machine Health with Makino’s MHmax Predictive Analytics
Predict Problems and Prevent unplanned Downtime
One of the biggest issues in production is when the machine stops running. Several factors can make this happen, but in any case involves a high cost for the end-user. The question arises, how to prevent unplanned downtime?
Our machine health monitoring software - Makino Health Maximizer(MHmax) - can foresee problems before they happen, so you can take steps to prevent unplanned down time. With 22 embedded sensors collecting data at the most critical points, predictive software checks for spindle health, analyzes controller data and calculates the need for alerts and warnings. You’ll know exactly how your machines are running at all times. So you can act, not react.
Why Predictive Is Better Than Reactive?
MHmax machine health monitoring software exists to allow for planned machine service before the machine unexpectedly goes down - significantly reducing downtime.
MHmax is a game-changing predictive technology specifically designed to reduce your unplanned downtime. Only MHmax offers a tailored high-value sensor package paired with Makino’s proprietary machine learning software to monitor key machine functional areas that are critical to the health of the machine.
Why You Need MHmax
Real-time Data for Real-time Troubleshooting
Operators and maintenance teams receive data on symptoms to do real-time trouble shooting to address symptoms before they become problems
In one consolidated report, you’ll view all data and information you need to make informed decisions and schedule preventive maintenance in the smartest, most efficient way possible.
Enables Maintenance and service teams use of sub-system data to have clear definition on what problem to fix to trouble shoot and make a repair quickly
Enables maintenance staff to manage a larger number of machine units with fewer staff by putting machine condition data at their fingertips
Avoids the cost of preventative maintenance “might break soon so replace it now” part uncertainty
