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The Future of Predictive Maintenance Analytics
A large-scale shift is occurring in maintenance that has organizations transitioning from traditional, reactive policies to condition-based monitoring and predictive maintenance. This can be attributed to two factors: Maintenance teams are recognizing the importance of understanding the current condition of their machines, and they have the capabilities and tools to plan for improvements based on determining what will be necessary to maintain their machines while making them even more efficient. Both of these factors are made possible with analytics.
Today, it is almost impossible for an organization to have a successful maintenance program that yields the desired results and ROI without data analytics. It is central to the effectiveness of maintenance, and has made both condition-based monitoring and predictive maintenance mainstays in Industry 4.0 organizations. With predictive maintenance analytics, like the advanced AI analytics that is part of ScoutCam’s technology, maintenance has to be treated as a process in which facts and analysis are the basis for decisions.
Predictive Analytics for Predictive Maintenance
With the advanced analysis of both historical and real-time data, organizations can create informative snapshots of their machines’ performance and forecast when a component is most likely to fail. This capability of harnessing both types of data is one of the reasons predictive maintenance is much more effective than preventive maintenance (which relies solely on historical data). Analytics in predictive maintenance provide maintenance teams with the advantage of determining maintenance responses by giving them the ability to leverage machine data to better plan and predict.
By using a data-driven, analytical approach to maintenance, maintenance teams are tackling the root causes of machine breakdowns. This means that predictive analytics can help determine why gears in a wind turbine gearbox have suddenly become misaligned or why hydraulic fluid leaks are occurring in an airplane. Identifying and addressing the root causes can prevent the wear and tear that will ultimately result in machine failure.
The Right Technologies for Predictive Maintenance Analytics
Predictive maintenance is facilitated by modern Industry 4.0 technologies, including IoT, machine learning, artificial intelligence and cloud computing, typically as part of integrated solutions. These technologies have played a most critical role with regard to increased adoption of predictive maintenance by making predictive analytics more accessible than it has been before. According to one estimate, using these technologies helps organizations to make predictions 20 times faster and more accurately than they would using threshold-based monitoring systems.
Condition-based monitoring tools, particularly those that are IoT- or IIoT-enabled, monitor machine health and detect anomalies, sending data to the cloud for processing. The IoT feature is an important element here because in order to effectively monitor the condition of machines, the data has to be provided automatically and in real time. The tools used can include devices such as meters, gauges or sensors.
ScoutCam’s Camera-as-a-SensorTM solution, which allows organizations to overcome the challenges of obtaining high quality data in normally inaccessible areas, is one example. In the aviation industry, for instance, ScoutCam’s solution can detect and monitor the minuscule deformations in bearings that are early signs of surface fatigue. Aviation maintenance teams can also use it to inspect the crankshafts of aircraft.
For train maintenance, ScoutCam’s technology enables the real-time monitoring of brake pad wear, among others. Maintenance teams are not only able to obtain the visualization to hard-to-access areas of machines that they would not be able to otherwise obtain without expensive and time-consuming disassembly, they also benefit from the real-time analytics conducted by machine learning AI models to predict times to failure and send notifications warning of component anomalies.
Machine learning is used to quickly process large volumes of structured and unstructured datasets to extract relevant and actionable insights. It uses models to identify patterns that have historically resulted in equipment failures, calculate the remaining working life of components and determine the best time to schedule a necessary repair. Artificial intelligence is used to make predictive maintenance “smarter” by employing algorithms for predictive calculations so that they become progressively more reliable and adds a higher degree of predictability to an organization’s maintenance program.
The use of data analytics to detect anomalies to prevent unexpected maintenance issues is not an entirely new practice. For example, the aviation sector has employed anomaly detection for maintenance purposes for some years.
However, the condition-based monitoring and analytical technologies and tools that are available now and that facilitate maintenance predictions using historical and real-time data have evolved to such a degree that they have made predictive maintenance the most logical and beneficial approach to maintenance. The more these technologies are leveraged, the more advancements will be made in how well machines are maintained.
Condition-Based Monitoring and Predictive Maintenance Analytics Improves an Organization
The proactive approach to maintenance also provides Industry 4.0 organizations with a way to help alleviate pressing concerns such as:
- Operational costs that are being even more heavily scrutinized due to competition
- Supply chain complications that are making resources (i.e. access to replacement components) more scarce
- Constant pressure to improve machine effectiveness and lower maintenance costs, both of which are essential to successful operations
The success of an organization’s maintenance efforts have an impact that extends well beyond the maintenance department. Machine maintenance impacts machine reliability, and both factors impact an organization’s competitiveness and profitability. Using advanced data analytics for predictive maintenance helps an organization to ensure that the appropriate maintenance actions are taken at just the right time to prevent unexpected machine downtime. This allows organizations to:
- Improve operations by being able to accurately forecast inventory and managing resources efficiently
- Lower overall organization costs by minimizing the time equipment is maintained, the production hours lost to maintenance and the cost of spare components
- Increase the working life of machines by preventing breakdowns and by detecting the signs of machines and systems issues in the very early stages
In industries in which it has been a foregone conclusion that improving machine performance and productivity while simultaneously lowering operating costs and overhead is a nearly unachievable goal, condition-based monitoring and predictive maintenance provide an avenue for organizations to make a tangible and positive difference in how they operate.
Organizations that ensure that their condition-based monitoring and predictive maintenance systems feature advanced analytics will be able to efficiently identify pending machine issues and obtain actionable insights that benefit the entire organization.
ScoutCam offers such tools with its visualization-based AI-platform that provides acute visibility into difficult-to-access areas of machines and that can do so in extreme conditions. To see our solution in operation, take a look at our video gallery.