The power of predictive maintenance : improving efficiency and reducing costs in manufacturing
Introduction
The cost of maintaining industrial equipment during the manufacturing process represents approximately 60-70% of the total production cost . Therefore, effective and timely maintenance of machines can successfully solve problems, while increasing the availability of machine tools, which is where predictive maintenance can play a crucial role.
Predictive maintenance is a maintenance strategy that uses data analysis and machine learning techniques to predict when equipment is likely to fail, so that maintenance can be performed before the failure occurs.
The predictive maintenance market has been growing significantly in recent years. Indeed, in 2020 the market was estimated to be worth US$4.5 billion in 2020 and is expected to reach US$64.3 billion by 2030, a growth rate of 27.4% per year according to NextMSC. This strong growth can be explained by the constant improvement of technologies related to predictive maintenance, such as machine and artificial intelligence.
I - What is predictive maintenance ?
Predictive maintenance is a type of maintenance strategy that utilizes data analysis and machine learning algorithms to predict when equipment or machinery is likely to fail. It is a proactive approach to maintenance that allows companies to identify potential issues before they become major problems.
Predictive maintenance typically involves the use of sensors and other monitoring tools to collect data about the performance and condition of equipment. This data is then analyzed using statistical and machine learning algorithms to identify patterns and anomalies that may indicate a potential failure.
By analyzing this data, companies can identify when maintenance should be performed, rather than relying on a fixed schedule or waiting until a failure occurs. This can help companies to avoid unplanned downtime and reduce the need for expensive emergency repairs.
II - How remote predictive maintenance works ?
Here's a general overview of how predictive maintenance works:
1 - Data Collection
The first step in predictive maintenance is collecting data about the performance and health of a machine. This can involve using sensors, meters, and other monitoring devices to collect information about variables such as temperature, pressure, vibration, and more.
2 - Data Analysis
Once the data has been collected, it's analyzed using various techniques such as statistical analysis, machine learning, and artificial intelligence. The goal is to identify patterns, anomalies, and other indicators that could indicate potential problems with the machine.
3 - Predictive Analytics
Using the results of the data analysis, predictive analytics software can generate alerts and notifications when potential issues are detected. These alerts can be sent to maintenance personnel, who can then schedule repairs or other maintenance activities before the issue becomes a major problem.
4 - Maintenance Planning and Execution:
With predictive maintenance, maintenance activities are planned and executed proactively based on the results of the data analysis and predictive analytics. This can involve scheduling routine maintenance activities such as cleaning, lubrication, and inspections, as well as more complex repairs or replacements.
5 - Continuous Improvement and Optimization
As data is collected and analyzed over time, the predictive maintenance program can be optimized and improved to achieve better results. This can involve fine-tuning the data collection and analysis process, improving the accuracy of predictive analytics, and implementing new tools and technologies as they become available.
III - Predictive maintenance technologies
One of the key components of a successful predictive maintenance program is the use of advanced tools and technologies to collect and analyze data about machine health. Here are some of the most commonly used tools and technologies in predictive maintenance:
Vibration Analysis
This technique involves measuring and analyzing the vibrations of a machine to detect any changes or abnormalities that could indicate potential problems. Vibration analysis is particularly effective for detecting issues with rotating machinery, such as motors, pumps, and fans.
Infrared Thermography
Infrared cameras are used to detect changes in temperature that could indicate potential problems with electrical components, such as loose connections, faulty components, or overloaded circuits. This technique is particularly useful for detecting issues in hard-to-reach areas or for monitoring large areas at once.
Acoustic Emission Testing
This technique involves listening to the sounds emitted by a machine to detect any abnormalities that could indicate potential problems. Acoustic emission testing is particularly effective for detecting issues with high-pressure vessels, piping, and other equipment that emits high-frequency sounds.
Oil Analysis
This technique involves analyzing the chemical composition of lubricating oil to detect any contaminants or changes in composition that could indicate potential problems with a machine's components. Oil analysis is particularly effective for detecting issues with bearings, gears, and other components that rely on proper lubrication.
Ultrasonic Testing
This technique involves using high-frequency sound waves to detect changes in the thickness or density of a machine's components that could indicate potential problems. Ultrasonic testing is particularly effective for detecting issues with pipes, tanks, and other components that are subject to corrosion or erosion.
Other tools and technologies used in predictive maintenance include eddy current testing, magnetic particle inspection, visual inspection, and more. The key is to use the right tools and techniques for the specific type of equipment and application.
IV - Benefits of predictive maintenance
Reduced Downtime
By identifying potential issues before they occur, predictive maintenance can help minimize unplanned downtime. This means less lost productivity, fewer delays, and a more efficient operation overall. By reducing downtime, companies also save money, as unplanned downtime costs as much as $50 billion a year to industrial manufacturers.
Lower Maintenance Costs
Remote predictive maintenance can help companies reduce their maintenance costs by allowing for proactive repairs and replacements. This means that maintenance activities can be scheduled when they are most convenient and cost-effective, rather than waiting for a breakdown to occur.
Improved Safety
Predictive maintenance can help improve safety by detecting potential hazards before they cause an accident or injury.
Longer Equipment Lifespan
By addressing issues before they become major problems, predictive maintenance can help extend the lifespan of equipment. Predictive maintenance typically increases machine life by 20 to 40 percent. This means that companies can get more value from their investments and avoid premature replacement costs.
Better Data-Driven Decision Making
Predictive maintenance relies on data analysis and analytics to identify potential issues. This means that companies can make more informed decisions about when and how to perform maintenance activities, leading to better overall results.
V - How to leverage iot remote maintenance with predictive maintenance ?
IoT remote maintenance is a process that leverages the capabilities of the Internet of Things (IoT) to enable remote monitoring, control and maintenance of devices and systems.
Leveraging IoT remote maintenance with predictive maintenance involves combining the insights provided by IoT devices with remote maintenance capabilities to address potential issues before they escalate into serious problems.
IoT sensors and devices can collect data on a wide range of parameters, including temperature, pressure, vibration, and more. This data can be used to identify potential problems and predict when maintenance is needed.
Leveraging IoT remote maintenance with predictive maintenance can provide businesses with a range of benefits, including increased uptime, reduced maintenance costs, and improved efficiency. By combining these technologies, businesses can stay ahead of potential issues and ensure that their equipment is always operating at peak performance.
Conclusion
In conclusion, predictive maintenance is a powerful tool that can help companies anticipate potential equipment failures and keep their operations at peak efficiency.
Predictive maintenance is expected to become increasingly powerful thanks to technological advances such as intelligent computers, and the integration of cognition into computer systems as outlined in this article from the University of Patras.
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