Predictive maintenance analytics

How Big Data and Analytics Are Transforming Predictive Maintenance

Machines generate and store an astronomical amount of data. For instance, a single aircraft engine can generate a terabyte of data daily.
The speed and ease with which large amounts of data can now be retrieved and then analyzed means that relevant insights can be obtained quickly and applied to challenges. This is the case for predictive maintenance in industrial and commercial sectors. Big Data and predictive analytics are transforming predictive maintenance, and by default, helping to transform Industry4.0 organizations.

Condition-based monitoring of equipment or components with solutions like ScoutCam’s image-based AI platform provides organizations with important visibility into their systems and is key to the collection of vital data. Along with predictive maintenance, which has become rooted in Big Data and predictive analytics, it addresses a key deficiency of conventional maintenance strategies, such as planned preventative maintenance: the seemingly random failures that occur during the useful working life of machine components or equipment in between scheduled upkeep.

Organizations that implement condition-based monitoring and predictive maintenance can gain a real-time view of operation conditions and meaningful insights that allow them to better manage the life cycle of their equipment. These maintenance approaches incorporate Big Data and analytics to prevent equipment failures and minimize maintenance costs, unexpected downtime, disrupted operations and the inefficient use of resources. On average, predictive maintenance can improve productivity by as much as 25 percent, cut maintenance costs by 25 percent and reduce breakdowns by 70 percent according to a study published by the Institute of Mechanical Engineers.

Condition-based Monitoring and Predictive Maintenance Requires Big Data

Multiple factors necessitate the inclusion of Big Data and predictive analytics in predictive maintenance systems, whether in the energy, transportation, aviation and other industries:
● Machines and their components are generating more volumes and varieties of data than ever before. This means that there is a wealth of operational data that can be used to provide important insights into the health of equipment.

● Technology has advanced so that sensors like ScoutCam’s Camera-as-a-Sensor™
are available to create new data types and insights. ScoutCam can provide AI-based alerts and insights from areas that are in extreme environments and that are typically inaccessible by larger visual devices or by human inspection. It also provides flexible deployment capabilities for remote monitoring while creating and collecting necessary visual data never seen before.

● The speed at which data transactions take place have increased, aided by the increase digitizing of data and the widespread use of IoT platforms. They can be instantly connected to maintenance platforms, providing real-time data.

Predictive Maintenance Relies on Relevant Data

With these factors in place, the manual analysis of the data has become impractical. Data analytics for predictive maintenance require the use of machine learning techniques to explore the Big Data, extract the relevant data points and identify meaningful and useful insights.

ScoutCam’s technology analyses the volume of data it gathers and creates to uncover the hidden relationships within the data. Its AI-based power analytics provide insights from the data, detecting failure patterns and other findings that can be used to predict when and how a breakdown will occur. The mixture of Big Data, predictive analytics executed by ScoutCam and maintenance provides valuable transparency and foresight capabilities for maintenance engineers.

Predictive maintenance relies on historical and real-time data to formulate accurate predictions. Obtaining relevant data from operation-critical components in a timely manner and quickly analyzing it and applying the insights derived from the analysis is how organizations will continue to use condition-based monitoring and predictive maintenance to yield the following positive changes in their maintenance strategies.

Optimized Predictive Maintenance Processes

The vast amount and wide variety of Big Data that is collected and analyzed is crucial to the insights that inform what maintenance measures should be applied to a certain situation. It eliminates uncertainty and guess work in maintenance by highlighting which maintenance issues should be prioritized and resolved first.

It eliminates the over-reliance on scheduled maintenance activities, including the scheduled and often unnecessary replacement of components that does nothing to improve the operation of equipment. Predictive maintenance based on the Big Data collected during condition-based monitoring with tools like ScoutCam’s micro-visualization technology means that maintenance activities are performed based on the actual condition of the equipment.

Maintenance engineers are also able to control when they can maintain equipment or machinery and opt for times when it will cause the least inconvenience. For example, it is more convenient to perform maintenance on a train that has been parked in a station overnight just for that particular purpose than it is to repair one that has unexpectedly malfunctioned during operating hours on a distant track.

Replacement Component Availability

When the need arises to replace a component on a critical piece of machinery, the ideal situation would be to have the replacement component and the necessary tools on hand. However, there are many factors that have to be considered when obtaining necessary components. There are often lead times for getting the component from manufacturers. Shipping logistics and costs also have to be considered. Without careful planning based on applicable data, maintenance engineers often have to rely primarily on the use date of components as a rule for purchasing replacement parts. This approach places them at a disadvantage when unexpected breakdowns occur.

Predictive analytic models can be customized to forecast potential equipment failures. Data is gathered using sensors and is processed by machine learning algorithms that identify certain patterns in usage that indicate a breakdown is imminent. With this knowledge, maintenance engineers can estimate when and how components will fail and take the steps to address the problem before it occurs by having the necessary component already on hand, having been procured based on insights extracted from machine data.

The visualization capability of ScoutCam’s image-based AI platform can be particularly useful in helping maintenance engineers determine what replacement components are needed and more critically, when. For example, in many cases for wind turbine maintenance, a gearbox would have to be opened to identify which gears or bearings have been broken so that replacements can be ordered. However, ScoutCam can be installed in a gearbox to monitor during operations, providing the necessary visualization that allows maintenance engineers to determine in real-time the condition of the components and initiate the ordering of the replacements.

Extended Working Life of Equipment

The analytics conducted on Big Data not only determines when components have to be replaced before a breakdown occurs, it also offers important insights that can be applied to using components for as long as possible. For example, data analysis will reveal frequent modes of component failure. Maintenance engineers can determine how they can avoid the situations in which these breakdowns occur. This is an important aspect of predictive maintenance that helps to reduce both replacement expenses and downtime.

ScoutCam Helps Collect Big Data for Predictive Analytics

Condition-based monitoring and predictive maintenance have become the go-to approaches to implementing maintenance in Industry4.0 organizations because they can optimize maintenance timing. This means that the right maintenance measures can be applied at just the right time to avoid breakdowns and unplanned downtime.

In the Industry4.0 era, Big Data, predictive analytics and maintenance have become the center of an organization’s ability to deter failures and breakdowns. ScoutCam’s image-based AI platform provides organizations with the visualizing capability to monitor the condition of components in difficult-to- access areas of equipment, allowing them to gather critical data necessary for predictive analytics.

Get in touch with us to learn how ScoutCam can help your organization with its condition-based monitoring and predictive maintenance needs.

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