How Predictive Maintenance Solutions Provide the optimal Time for Maintenance

How predictive maintenance solutions provide the optimal time for maintenance

Companies across a variety of industries rely on machines: pumps, engines, elevators, turbines, etc. Some are more complex than others, but they surely have one thing in common – degradation of material. With each cycle of operation, components are losing their original physical parameters. Regular checks, diagnostics, maintenance, or even replacement, are an important part of machine operations.


Ideally, you can avoid any failure of your machines, thus being pro-active rather then reactive is for many businesses the only option. Also, acting at the right time has real financial implications. Imagine two extreme situations:

  • Maintenance planned too late: the device would reach the point of failure and cause operations disruption. Every minute of downtime can bring big losses.
  • Maintenance planned too often or too soon: expenses for time and material spent is higher than the situation requires. Also, should a component be replaced completely, then the invested capital won’t be utilized to its full potential.


How predictive maintenance solutions provide the optimal time for maintenance



Benefits of Predictive Maintenance

Predictive maintenance provides a number of benefits that preventative maintenance does not, such as:


Reduction downtime

Reduction of unnecessary downtime

Predictive maintenance, as contrast to preventative maintenance, tries to only pause a machine when anomalies are discovered (vibrations, defect rates, etc.). As a result, there are fewer unplanned production outages.



Reduction maintenance costs
Reduction of maintenance costs

Data-driven maintenance allows teams to better plan workers and maintain inventory (parts, lubricants, etc.) on an as-needed basis. It also makes it easier to find parts early on, before a disaster strikes.



Shorter maintenance events
Shorter maintenance events

Because there is specific data that shows teams exactly where a problem is developing, maintenance can be targeted. This means solving the problem faster.




Needs based scheduling
Needs-based scheduling

Maintenance can be targeted because there is particular data that indicates teams exactly where an issue is developing. This implies that the problem will be solved more quickly.




Promotes learning
Promotes learning

Unlike preventative maintenance, a predictive plan allows for the collection and analysis of event data. This allows teams to learn from downtime. To avoid recurrences, they might refine their plans for upgrades and innovations.




Predictive Maintenance made easy with TIM

Predictive maintenance solutions can provide the optimal time for maintenance. Thanks to the data coming from sensors and AI/ML, it is possible to get advice, almost in real-time, on what is the best time to take action.

TIM can build automated ML models from time series data and predict the time remaining (Remaining Useful Life, RUL) or classify whether the device is already in a window (zone) of possible failure within a certain period of time (cycles). Data from machine sensors are often sampled in seconds, or even milliseconds. TIM can work with data sampled in any sampling rate starting from milliseconds.

Also, effort and time required to set up TIM for production use is reduced to a fraction of what would be typically required. TIM, by design, automates most of the steps required for set-up and operations, and offers a robust ML solution.



Learn more about TIM InstantML →



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