Industry 4.0 has made it possible to collect data easier and faster. This data, collected from sensors and other equipment, can be used to improve many procedures, tools, and applications. One such advancement is monitoring the condition of industrial equipment with predictive maintenance (PdM).
With the rise of Industry 4.0 and embedded technology, more industrial equipment is becoming interconnected and generating large amounts of data. This data can be leveraged to predict when maintenance is necessary, which can help to avoid costly downtime and improve safety.
Predictive maintenance has become increasingly important in recent years due to the growing complexity of industrial machinery and the need for greater efficiency and productivity. It is a great tool for maintenance and improving efficiency and productivity.
But let's dig a little further: what exactly is predictive maintenance, how does it work, and why is it so important?
Predictive maintenance is a tool that uses data to analyze the condition and operation of machinery to anticipate potential problems before they occur and address them on time and in the best possible way. The result is a reduction in potential service failures of machinery, as well as reduced safety risks.
Predictive maintenance systems use data analytics and machine learning to predict when machinery maintenance is needed, avoiding unnecessary failures, breakdowns, and deterioration. The predictive maintenance process involves monitoring the equipment, analyzing the data generated, and using it to identify patterns and tendencies; to use them to predict future maintenance and repair needs. Predictive maintenance is used extensively in the manufacturing industry, but sectors such as healthcare, energy, and transportation also.
There are two main maintenance types categorized: traditional and modern.
Traditional maintenance
Typically consists of regularly scheduled maintenance based on time or usage; when a component is damaged or worn, it is removed from the process to be repaired or replaced. It is a reactive approach that consists of two types of maintenance methods, which are the following:
The results usually bring inefficiencies in performance and unnecessary costs.
Modern maintenance
On the other hand, modern maintenance is a proactive approach based on equipment performance and usage patterns. By analyzing the data the equipment generates is possible to anticipate potential problems. The types of modern maintenance are the following:
By detecting and addressing problems early, maintenance can reduce the risk of failure and improve the overall performance and lifespan of the equipment.
Implementing predictive maintenance solutions in any organization enables effective asset control to ensure asset lifecycle and monitor system operational performance in real-time. This allows the organization to leverage its existing data to predict and prepare for potential damage or downtime and address it proactively rather than simply reacting to problems through scheduled maintenance without detailed information.
As we have mentioned, one area where predictive maintenance is specifically essential is that of embedded systems built into industrial machinery. But, an embedded system being a computer system designed to perform specific tasks within device systems, can also be incorporated into a car, a medical device, etc. Or even any company that uses information technology (IT) and relies on servers, routers, or other network equipment can keep its operations running smoothly with predictive maintenance solutions.
The main aspects of predictive maintenance growth are the increase of emerging technologies that gain valuable insights; furthermore, the increased necessity to reduce maintenance costs, equipment failure, and downtime.
According to the recent report "Predictive Maintenance Global Market Report 2023" (by The Business Research Company), the global predictive maintenance market is expected to grow by 31% by 2026. This is because it reduces downtime and failures and increases the lifetime of machines. Predictive maintenance is a vital tool for any company, translating into improved performance and cost savings for the organization.
When implementing essential equipment in business operations with the right predictive maintenance solutions, you can take care of the equipment's health and obtain great benefits, such as the following:
Predictive maintenance solutions help to identify potential equipment failures before they occur to minimize downtime and increase productivity.
Companies utilizing PdM solutions can optimize their operations and resources, improving efficiency and productivity.
Predictive maintenance systems help identify potential problems so it is possible to address them on time to extend the life of the equipment, reducing the necessity for frequent replacements and reducing waste.
By predicting when maintenance is needed, companies can avoid unexpected failures and downtime, facilitating increased equipment reliability.
As PdM solutions systems can identify potential problems before they occur, this helps companies to minimize the risk of accidents and injuries in the working staff.
Because PdM allows better use of resources and optimization of maintenance schedules, a company saves overall costs.
It will depend on the type of predictive maintenance application. The general steps of the PdM systems process involve the continuous collection and analysis of data so the system can learn and identify potential problem patterns in the equipment's health and its operating procedures to act on time.
The integrated technologies involved in predictive maintenance systems are advanced technologies that enable real-time data management, which include the following:
These are devices that collect and move data from any of the connected equipment. They can be embedded directly in the equipment or installed externally.
This technology is used to process and analyze data locally, where the data is generated, instead of needing a cloud server to send all the data. This allows faster response times by reducing the latency.
AI algorithms are key for PdM solutions because they allow machine learning, which improves accuracy since it learns over time and makes decisions based on continual data analytics.
This type of infrastructure interconnects all nodes and devices in the network, which helps ensure data security and system resilience. There is no need for a central server that makes the system slower and less secure due to its single point of attack, making it easier to cyberattack.
Internet of Everything Corporation offers embedded technology solutions for any business or industry. Our Eden system is a decentralized, autonomous, and virtual system implemented with AI technology to monitor and analyze data securely and sustainably in real-time.
Eden system is designed to implement embedded solutions in real-time procedures. For enhancing business performance, productivity, and security, and providing sustainability and cost savings. It includes a wide range of embedded technologies and techniques to offer the best service:
AI is key in Eden system's techniques, such as machine learning, because it allows performing data analysis continuously for identifying patterns to enable decision-making in real-time. This is why AI is so important for predictive maintenance solutions, as it permits reacting to failures before they occur.
By using big data analytics for large amounts of data across all the connected sensors and other devices of the system, Eden can identify patterns in the system's performance. This provides insight to process performance for optimization opportunities and identifying potential issues.
This means that Eden provides the ability to bring data management and storage in real-time, distributed throughout the system devices keeping the data closer to where it is needed. This is very useful for predictive maintenance because it eliminates the step of sending data to a cloud, providing more security and efficiency.
This technology is a distributed ledger that allows secure and transparent data transactions between all the users in the network. Eden system uses blockchain to ensure a trusted and protected ecosystem for all the users in the network.
For example, a manufacturing company can implement predictive maintenance applications adopting the Eden system to monitor machinery in real-time. The machinery would have sensors installed to collect data from vibrations, temperature, energy consumption, etc. The data then is analyzed with the help of machine learning algorithms, which can identify patterns in the machine's performance to detect potential problems, but also opportunities to improve overall efficiency.
Over time, these predictive maintenance applications will learn the behavior of the machinery to detect abnormal behavior. This helps to alert maintenance staff so they can schedule maintenance and repairs in advance to avoid unplanned downtime and unnecessary production losses.
In addition to the Eden system, IoE Corp. offers the Planet Partner program to help companies improve their business, especially in sustainability, to be responsible for their environmental impact. Applying offers advanced technology, training, support, and resources to enhance their business.
Furthermore, the Planet Partner Program lets companies partner with other organizations to collaborate on new initiatives, allowing new business opportunities and better impact.
Here you can learn more about Eden System and Planet Partner Program.
Join us to start your journey to a more sustainable and innovative business future. Please, apply here.
