What Is The Role Of IoT In Predictive Maintenance?

IoT has become a fundamental part of every industry. It provides the connectivity and flexibility necessary for organizations to be functional in the Industry 4.0 age. Along with Big Data, AI and machine learning, IoT has also become a vital aspect of condition-based monitoring and predictive maintenance.

The Role of IoT in Condition-Based Monitoring and Predictive Maintenance

It is impossible to have effective predictive maintenance without having accurate and up-to-date data related to the condition and performance of machines. In fact, the ability to continuously monitor equipment while collecting and analyzing large data sets directly contributes to the success of an organization’s predictive maintenance efforts.

With the right devices and technologies, IoT helps create the infrastructure necessary for continuous and reliable data collection and analysis. It facilitates condition-based monitoring that provides real-time insights into the state of a specific piece of machine or component, enhancing the effectiveness and accuracy of predictive maintenance.

Machine data collection within IoT infrastructures is typically performed with sensors. The data is then stored and analyzed in the cloud or an on-premise server. Maintenance teams can conduct predictive maintenance analysis to forecast machine failure and decide on which actions to take to prevent breakdowns and their impact.

IoT-Based Predictive Maintenance with Odysight.ai

Let’s provide a specific example with Odysight.ai’s Camera-as-a-Sensor™. The size and resilience of Odysight.ai’s micro visual technology allows it to be used for condition-based monitoring and inspection in areas of equipment that are difficult to access and/or located in extreme environmental conditions. When the technology is deployed, data is continuously collected and analyzed on a secured cloud.

Deep analysis and machine-learning AI algorithms are then used to discover correlations and patterns in the data that are not readily apparent, identify fault conditions and derive actionable insights. The algorithms are able to learn from the data, becoming more accurate and improving their prediction capabilities.

Organizations within aviation, transportation, energy and more, are afforded a comprehensive view of the status of a machine. This not only allows them to accurately forecast machine or component failure before it occurs and to have all of the right elements in place to mitigate downtime, but they can also optimize the machine for better performance.

Benefits Of IoT-Based Predictive Maintenance

As mentioned earlier, IoT enhances predictive maintenance. This leads to improved benefits:

● Reduction of maintenance expenses. The ability to predict when a machine will fail means that steps can be taken well in advance to avoid breakdowns. Organizations save costs on emergency replacement of components and scheduling of the necessary personnel due to unexpected machine failure. They can also schedule any necessary downtime to replace failing components so that it has the least financial impact on the organization and does not disrupt overall operations.

● Extended equipment work life. Continuously monitoring equipment and detecting issues before they materialize allow organizations to optimize their machines for better performance. Maintenance and repair tasks can be prioritized and scheduled as needed. All of these factors extend the length of time a machine will operate at peak efficiency.

● Quicker and More Informed Decisions. Using IoT devices gives maintenance teams access to real-time machine data and analytics, allowing them to make on-the-spot and informed maintenance decisions, remotely if necessary. Using the insights obtained from the data, maintenance teams can readily address machine performance issues.

Predictive Maintenance IoT Using Odysight.ai®’s Platform

When using IoT-based predictive maintenance strategies and tools, the actual condition of a machine is taken into account and maintenance tasks are performed only when it is necessary. This approach to maintenance has proven to be more efficient than the heavy reliance on inspection and component replacements according to a predetermined schedule.

Outcomes such as reductions in overall maintenance costs by as much as 10 percent and improved equipment uptime and availability by as much as 20 percent will compel more and more organizations, especially those in Industry 4.0 markets to transition from reactive, preventative measures to condition-based monitoring and predictive maintenance to avoid disruptions to operations that stem from unforeseen machine breakdowns.

In Industry 4.0 markets, IoT-based predictive maintenance can be applied to specific applications.

For example:

● Odysight.ai’s visual sensor technology provides the visualization needed to assess brake pads and gather data to determine the braking capabilities of railway cars.

● Organizations in the wind energy sector can use Odysight.ai’s micro-visualization solution as part of an IoT-based condition-based monitoring and predictive maintenance strategy to remotely and safely monitor and collect data from elevated wind turbine nacelles.

● Bearings in airplanes, trains and UAV’s can be monitored for malfunctions and deformations while the locomotives are in operation.