Predictive Maintenance With Image-Based AI

5 Steps To Predictive Maintenance With Image-Based AI

Industry4.0 organizations are seeking innovative solutions to help gather critical data that present an accurate picture of the current condition of their machines. By using ScoutCam’s image-based AI platform as a part of their condition-based monitoring and predictive maintenance systems, organizations can ascertain the health of their machines in real time and determine what maintenance steps need to be taken to prevent unplanned and costly downtime. They can exercise more autonomy over their maintenance outcome.

Industry4.0 Maintenance Requires Visualization, Analysis and Prediction

Effective maintenance has become even more important in the Industry4.0 age. The closer machines and their components approach points of failure, the less efficiently they operate. This results in complications like unsafe aircraft and trains and lowered production quality that end in breakdowns. Machines are also highly integrated, and a failure in one piece of machinery or component can result in damage to nearby components and widespread damage to an entire system.

Condition-based monitoring with solutions like the micro-visualization technology from ScoutCam is a key element of implementing effective predictive maintenance. It is part of the multi-step process that begins with obtaining the necessary data and that concludes with implementing the appropriate maintenance actions to resolve maintenance issues. Getting as much information as possible on machine events is important because the more information there is on machine events, the better the predictions will be.

1. Choose Which Machines and/or Components to Monitor

Organizations should focus on applying condition-based monitoring where it will have the most impact. While a good practice is to consider the cost of downtime and the return on investment for any equipment that are being considered, there are certain types of equipment that should be prioritized:
● Equipment that has failed frequently in the past.

● Critical assets. For wind turbine maintenance, this may be the gearbox. In aircraft, condition-based monitoring efforts may center on landing gear or the connection of the rotor with the body.

● Equipment that can be difficult to replace or source. For example, replacing certain parts of wind turbines, such as the rotor blades, require specialized equipment and can involve complicated shipping logistics.

● Equipment that is in remote locations and are extremely difficult to reach and monitor safely in person.

2. Data Collection and Anomaly Detection

All equipment or machines will inevitably deteriorate from regular use and will begin to produce anomalies. While it is not a breakdown, it is a very strong indication that the machine is not operating optimally and requires some form of maintenance to prevent a breakdown from occurring. When condition-based monitoring is used, these anomalies can be detected.
The condition-based monitoring of machines is very important in two ways:
● It establishes a baseline, helping maintenance engineers to understand what data and related conditions constitute as normal operations.

● It also shows in real-time when machines and their components deviate from those normal actions.

Technology like ScoutCam’s Camera-as-a-Sensor™ solution helps to gather data that can be used to help define what is normal and what is not. It provides high-powered micro-visualization capability for monitoring and assessing components in extremely difficult-to-access areas of equipment. This capability is accompanied by specialized trained AI models that provide AI-based alerts and insights and that execute image processing tasks, such as calculating the area size and number of threads on crankshafts. ScoutCam’s high resilience to vibrations, vacuum, cosmic and ionizing radiation makes it indispensable in gaining visualization of components that are surrounded by extreme conditions.

For predictive maintenance to be effective, the need for continuous monitoring remains whether or not the equipment is in conditions that could be hazardous to humans. This is why solutions that allow flexible deployments of condition-based monitoring, like ScoutCam’s image-based AI platform, are ideal for monitoring in distant and inhospitable settings, such as in wind turbine nacelles.

Using ScoutCam’s technology allows maintenance engineers to conduct more effective and continuous condition monitoring. It is more effective than the past method of condition monitoring, which typically relied on manual readings using handheld devices. This approach, which often occurs as part of a scheduled routine, is insufficient because any faults that arise after a scheduled monitoring session or reading will not be detected unless they result in a breakdown or until the next reading. This does not provide sufficient data about the current condition of components or potential failures, and it does not facilitate predictive maintenance.

3. Extraction of Relevant Data from Big Data

The amount of historical and real-time data can be vast, making it difficult to derive insights when analysis is conducted manually. Complicating the issue is that the data that informs effective predictive maintenance come in multiple forms from various sources.
One of the roles of machine learning in condition-based monitoring and predictive maintenance systems is the organizing and extraction of relevant data points from Big Data. This cleaning of the data is necessary for gaining meaningful insights from the data.
ScoutCam’s solution analyzes the Big Data it amasses during condition-based monitoring. Using its algorithm layer and application layer, it extracts relevant insights from the huge visual data collected or created by the cameras.

4. Find Patterns in the Relevant Data

Predictive maintenance algorithms undergo deep learning to predict relationships between relevant data points obtained during condition-based monitoring. With predictive modeling, maintenance engineers can learn the reasons behind impending faults from analysis that can define root causes. This is where key trends toward faults can be identified. Machine learning predictive models can also be created to obtain specific insights, such as how to maximize the working life of a certain component or how to increase uptime.

5. Act on the Insights

This entails leveraging the trends detected in the data patterns to predict faults before they occur. The end goal is to receive enough of a notice of a pending failure to be able to respond properly. With this foresight, organizations can optimize their maintenance strategies and efforts.

Use ScoutCam’s Image-Based AI Platform for Predictive Maintenance

ScoutCam enables condition-based monitoring and predictive maintenance in areas of machinery that are typically inaccessible to the human eye. Our image-based AI platform provides reliable advanced image analysis for multiple Industry4.0 markets, including the industrial aviation, energy, UAV and transportation sectors. Contact us to learn how our technology can help ensure that your systems are properly maintained before failure occurs.

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