What is Maintenance 4.0? The Future of Predictive Maintenance
As Industry 4.0 organizations continue to gravitate to more advanced modes of operation, it is only logical that maintenance reaches another level of maturity as well. Maintenance 4.0 is prioritizing the use of connected technologies and innovations like ScoutCam’s visualization-based AI platform that can enable automation and data connectivity in maintenance processes.
Arriving to Maintenance 4.0
During the evolution of maintenance, achieving efficient maintenance has been hampered by a lack of relevant data and/or the capability to collect and leverage the data that was available.
As noted in one maintenance research paper, the standard mode of operation in the earliest generation of maintenance approaches entailed operating machines until they failed. This, of course, routinely resulted in unexpected downtime, causing emergency situations for maintenance teams who then had to expend the necessary resources to obtain spare parts and have them installed as soon as possible.
In the next maintenance generation, maintenance teams relied heavily on preventive maintenance or time-based maintenance and applying scheduled maintenance measures. This approach is widely used today and is also inadequate as it does nothing or very little to address the actual condition of a machine and results in either inadequate or excessive maintenance and a waste of resources. Even worse, it still results in substantial unexpected downtime.
For example, early leak detection of hydraulic fluid in an airplane is critical to preventing a brake system malfunction. However, maintenance teams that use earlier maintenance approaches are not able to detect hydraulic fluid leakage until it is too late. One of the drawbacks of relying primarily on scheduled inspections and maintenance, is that breakdowns and indications of impending breakdowns can occur between those scheduled points, even in aircraft, which are required to undergo numerous inspections before and after flights.
Another issue is that in order to detect a leakage in its earliest stages, it would be necessary to continuously monitor certain areas of the airplane while it is in operation, a task that would be impossible for a human to do without being exposed to untenable environmental conditions.
It has only been in the last few decades that maintenance teams began engaging in real-time monitoring on a large scale. However, the amount of data and its wide variety presented problems for many organizations that were unequipped to use the data to benefit their maintenance efforts. Organizations still fell well short of optimizing their maintenance efforts.
One of the main elements lacking in the previous generations of maintenance were the technologies and tools to efficiently collect and analyze the data for actionable insights.
Maintenance 4.0 is Predictive Maintenance
This is no longer the case. As the digital transformation of industries ramped up, so did the technologies and tools that could be used to collect, transmit, process and analyze large swaths of data to directly address maintenance pain points.
Organizations are able to leverage their machine data using machine learning, artificial intelligence, IoT/IIoT and cloud computing. This enables them to realize the full potential of condition-based monitoring and to effectively implement predictive maintenance.
Under Maintenance 4.0:
- The collection of massive amounts of disparate data is facilitated by IoT/IIoT-enabled devices, like ScoutCam’s visual sensor technology, that provide maintenance teams the capability for accessing and analyzing machine data at anytime via the cloud.
- Machine learning algorithms, AI and predictive analytics have become the mechanisms by which maintenance measures are applied, allowing maintenance teams to turn insight into appropriate actions.
- Maintenance teams are able to more easily determine the root causes of machine failure and what maintenance measures are needed remedy the issue and prevent it from recurring.
Implementing Maintenance 4.0 with ScoutCam
With this degree of optimized maintenance, organizations have been able to reduce or eliminate unplanned downtime, maximize operational efficiency by having machines in optimal operation conditions longer and lower costs.
Let’s return to the airplane maintenance example. In a system that adheres to Maintenance 4.0 principles, aviation maintenance teams are able to conduct condition-based monitoring using ScoutCam’s Camera-as-a-Sensor™ technology. High resolution image technology can identify the earliest indications of a leak.
When that occurs, the AI processing, which is conducted in the cloud or on-site, will ensure that a notification of the occurrence is issued. Existing historical data and the data that was collected during condition-based monitoring are processed in algorithms and predictive analytics to ascertain the conditions that contributed to the leak and to better understand what to do to prevent the same occurrence. Armed with this information, the maintenance teams can immediately begin planning maintenance downtime so that it causes the least disruption.
It is worth mentioning here that ScoutCam’s technology supplements the effectiveness of condition-based monitoring and predictive maintenance by providing these capabilities for areas of machinery that are typically inaccessible by humans.
The Future of Maintenance Requires ScoutCam
New technologies have redefined how Industry 4.0 organizations, including those in aviation, energy, UAV, wind turbine and mobility can care for their machines. ScoutCam’s visualization sensor solution is one such technology that is helping to accelerate actionable insights that optimizes maintenance and improves machine reliability.
Contact us today and equip your maintenance team with the tools it needs.