The Role of AI in Predictive Maintenance

AI has become a necessary element of predictive maintenance. With it, organizations can evaluate Big Data streams from varying sources against historical data to gain new valuable insights and to forecast equipment failure.

By using Odysight.ai®’s AI-based condition-based monitoring and predictive maintenance solution specifically, utilities, plants, and facilities in aviation, energy transportation and other Industry 4.0 markets can position themselves to detect issues before they become significant problems. This will allow organizations to substantially reduce or eliminate completely the risk of maintenance-related disruptions.

How AI in Predictive Maintenance Works

AI is able to recognize patterns in machine data using autonomous learning algorithms. IoT is instrumental in gathering, storing and processing machine data from a wide range of sources, including sensors that measure the various factors that impact the condition of equipment and management systems.

The data is then cross-checked against additional sources, such as data pertaining to manufacturer service recommendations or data that was manually collected through human inspection. The ability to handle a variety of data sources and types allows for the creation of mathematical models that can make accurate predictions based on the relevant information that is extracted from the raw data.

For example, to determine when a certain component in an aircraft is likely to fail, inputs such as corrosion levels, temperature, or vibrations measurements may be used. As the machine learning models continue to learn, the algorithms can be used to unveil important insights and make predictions about future events.

Based on the patterns identified in the data, AI can determine what actions have to be taken and have those actions, such as scheduling the appropriate maintenance task, issuing a service request, alerting a service provider, making changes to production schedules, initiated.

Organizations can use AI-based predictive maintenance to forecast equipment failure, help identify specific maintenance problems and address specific maintenance needs (e.g. reducing downtime).

Automation is the Future of Maintenance

Automation is critical to eliminating redundant maintenance activities that can contribute to resource waste and maintenance inefficiencies. It ensures that maintenance tasks will be executed properly and at the right time.

Consider a maintenance system that has incorporated Odysight.ai’s visualization AI platform. When the analysis of machine data indicates a potential pending failure of a certain component, planned downtime can be scheduled for a time during which it will cause the least disruption. Spare parts can also be ordered well in advance, leading to lower costs and storage expenses. Knowing when machine failure is most likely to occur and then applying automation to maintenance tasks improves maintenance planning.

Key Differences of Planned Preventive Maintenance vs. Predictive Maintenance

Predictive maintenance is an improvement on more conventional approaches to maintenance, particularly planned preventative maintenance. While the aim of both approaches is to maximize equipment usage for as long as possible, each addresses the issue in different ways.

AI is present in both systems, providing the same functions, such as power analytics, automation, etc. However, with planned preventative maintenance, equipment will undergo maintenance, whether or not it is truly needed. This means that even if the analysis of the data shows that there are no indications of pending malfunctions or breakage, tasks such as part replacement will take place anyway according to a set schedule.

The problem with this methodology is that there is a likelihood of equipment that will be maintained to an excessive and unnecessary degree or receive subpar maintenance. Organizations will have to contend with either a waste of resources or the consequences of unexpected machine failure.

With predictive maintenance, maintenance work is conducted as needed. Condition-based monitoring and predictive maintenance tools like Odysight.ai’s technology use machine data to provide context that can be used to visualize, analyze and predict when equipment is at risk of malfunctioning or breaking down. Unlike with planned preventative maintenance, it optimizes maintenance resources and costs and helps to prevent unplanned downtime.

How Predictive Maintenance Can Transform Industry 4.0

AI-based predictive maintenance is one of the systems that aligns with the key principles of Industry 4.0, namely the integration of AI, machine learning, and automation in processes. Organizations that have the capability to optimize their preventative maintenance systems avoid disruption by avoiding potential failures, and as a result, equipment and system downtime. This means that the resources that would typically be expended in unnecessary maintenance tasks and recovering from unexpected downtime or outages can be applied to increasing productivity and improving the quality of products and processes.

Identify Obstacles in AI-Driven Predictive Maintenance

Organizations may encounter obstacles when implementing AI-based predictive maintenance if they do not plan carefully and have the right approach to handling data.

Machine data itself, or rather the collection of machine data, can be an obstacle. The collection of data has to occur more frequently, and it has to be the right data at the most opportune moment. This is where condition-based monitoring with Odysight.ai’s AI-based platform has an important role. Instead of checking or gathering data on equipment or machines on a quarterly or yearly basis, hourly, daily, weekly or monthly checks may be required or whatever more frequent schedule is necessary to ensure that the right factors are being accurately measured at the right time.

The goal is to make sure that there are no gaps in obtaining the data that is needed for predictive maintenance. The payoffs, including decreased costs from downtime and equipment maintenance and the ROI of condition-based monitoring and predictive maintenance, makes the efforts to implement AI-based predictive maintenance worthwhile.

AI provides key capabilities that make condition-based monitoring and predictive maintenance more efficient. Odysight.ai’s image-based AI platform is a visualizing, analyzing and predictive solution Industry 4.0 organizations can use to prevent maintenance-related disruption.

To learn how our technology can address your organization’s condition-based monitoring and predictive maintenance needs, get in touch with one of our representatives.