Predictive Maintenance Compatible with RES (Renewable Energy Sources)
From an expert’s perspective
Emilia Basta, Eco-energy systems engineer
Is predictive maintenance actually possible? Predictive maintenance (PdM) is responsible for determining the probability of failure of equipment or machines. It uses data and analysis to diagnose possible irregularities so that maintenance can be carried out before a failure occurs. This is to prevent installation downtime, ensure equipment reliability and reduce maintenance costs.
Predictive maintenance steps
Predictive maintenance is based on several basic and complementary steps. The first one involves collecting data. Data is collected from sensors installed in installations or equipment to monitor performance, how it is used and the overall condition of the equipment. Typically, this data includes information on temperature, humidity, fluid and oil analyses, etc.
In the second step, the data is analyzed. This is done using advanced analytical techniques such as machine learning and statistical models to identify patterns and trends that indicate when equipment is likely to fail.
After the analysis, the next step can be taken, which is maintenance planning. Maintenance is not planned according to a fixed schedule, but the optimal time to carry it out is determined. This allows you to minimize downtime and extend the life of the equipment.
Predictive maintenance is also used to monitor equipment remotely. This allows you to identify potential problems and plan possible repairs. Predictive maintenance can also be used to identify the root cause of equipment failure. In this way, detected failures can be used to improve equipment design and prevent future failures.
Predictive maintenance and renewable energy
Obtaining energy from renewable sources, as in the case of other devices
and energy storage facilities, may be subject to failures. Renewable energy sources are unstable due to weather variability and have limited availability.
It is not possible to obtain energy from wind or geothermal energy in every region. Such restrictions are usually determined by general conditions in the surrounding area. However, all disadvantages can be eliminated by appropriately maximizing energy production and adapting it to a given energy source.
Predictive maintenance combined with renewable energy ensures maximum energy production efficiency and adapts to the specific needs of these types of systems. Any failure of equipment used to use energy, or interruption in its supply, may lead to costly repairs or expensive replacement of the entire installation or its components.
By using predictive maintenance, you can identify areas of your system that are underperforming and develop strategies to improve performance
and reduce costs throughout your system. This ultimately allows for a more efficient and cost-effective system that is environmentally friendly and sustainable
for future use.
Wind turbines
In the wind energy industry, research is still being carried out on detecting wind turbine failures. Scientists from the Technical University of Munich use a combination of data from a wind turbine and vibration sensors. In this way, they are
able to detect early signs of failure that might otherwise go unnoticed.
Incorporated into turbine designs around the world, sensors monitor the health of wind turbine components in real time, providing early warning of potential problems and enabling proactive maintenance.
This solution is based on advanced analytics, combining data from multiple sources to provide an accurate and comprehensive assessment of the condition of wind turbines. Sensors are placed at points where temperature, vibration, pressure and operational efficiency are measured.
Thanks to such control, it is possible to precisely identify potential problems
and warn operators in advance, reducing installation downtime and, consequently,
lowering the cost of wind energy.
Photovoltaic panels
Solar panel predictive maintenance is a modern approach to maintaining solar energy systems . Scientists from the University of Southern California are working on a system that allows predicting the moments when a solar panel may fail. The system would remember emergency states and prevent them from occurring
in the future.
Analyzing the condition of solar panels would predict when solar panel components may require servicing, thereby enabling preventive maintenance and reducing the likelihood of unexpected outages in energy production.
As a consequence, such activities would allow for better efficiency of the entire solar energy system, and would also help reduce the maintenance costs of the installation and improve its reliability. Maintenance would be more efficient and potential problems could be eliminated before they become costly repairs.
Geothermal sources
Apart from wind, water and sun, geothermal energy is becoming more and more popular . It is an increasingly popular alternative to traditional energy sources. Predictive maintenance in this case can help reduce downtime, improve safety
and increase system reliability.
In the case of geothermal sources and obtaining energy from them, it is necessary at the very beginning to identify the main elements of the system and assess their condition. We are talking
about mechanical elements, including infrastructure elements such as pumps, valves, turbines, but also electrical elements, cabling motors and systems used to control the entire process.
Once signs of wear are assessed, preventive maintenance technologies can be applied to monitor the health of the system. The installed sensors are used to detect changes in temperature, pressure and vibration. This helps identify potential problems before they become serious, costly problems.
If any discrepancies are detected, predictive maintenance is designed to replace parts, perform repairs, or take other steps to ensure that the system operates properly and operates at maximum efficiency. With an appropriate predictive maintenance plan, you can maximize your payback time and ensure trouble-free operation of your geothermal systems.
Hydroelectric power plants
Hydropower constitutes an important share of the renewable energy sector. Predictive maintenance allows you to control the operation of this type of power plant and prevent any failures that could lead to costly plant downtime.
Sensors in hydropower plants monitor vibrations, temperature, level of lubrication of components and water flow, as a result of which the risk of irregularities in its functioning is reduced to a minimum. These types of inspections also help improve safety conditions by detecting problems before they become unsafe.
In the case of water turbines, it is particularly important that sensors are able to detect any changes in flow rate within the turbine and help adjust turbine settings to ensure maximum efficiency.
The role of artificial intelligence
The use of artificial intelligence in predictive maintenance operations in the renewable energy field is gaining popularity in the industry due to its potential to reduce costs and improve efficiency. Tasks traditionally performed by humans are becoming automated. Artificial intelligence can reduce operational costs and improve the accuracy and speed of maintenance operations.
By analyzing data from sensors, thanks to artificial intelligence, it is possible to precisely and quickly detect system problems. This enables early response to possible failures, downtime and improves system efficiency. In optimizing energy production, artificial intelligence helps operators maximize efficiency while minimizing energy costs.
Artificial intelligence can also be used to improve security. It is
able to monitor environmental conditions, temperature changes, air quality and other potential threats to human life. This allows for effective protection of employees against dangerous working conditions.
In renewable energy, thanks to artificial intelligence, it is possible to reduce operating costs by minimizing downtime, shortening repair time, performing maintenance efficiently and safely, and maximizing renewable energy yield.
Predictive maintenance in practice
One company using predictive maintenance in its factory is BMW. It focuses on the use of sensors, data analysis and artificial intelligence. An approach based on time or well-established rules has been replaced by optimal readiness of production equipment. This avoids costly production downtime, but also makes an important contribution to sustainable development and resource efficiency.
Predictive maintenance uses a modern cloud platform . This allows you to receive early warnings about possible failures and component wear. The monitored production devices are connected to the cloud through a gateway and transmit data regularly, usually once a second.
The cloud platform also allows you to control the software as needed. You can turn it on and off to adapt to changing requirements as quickly as possible. High standardization of the entire system allows for easy implementation in new locations and quick development of existing or new solutions.
During the production of electric motor housings, systems are so automated that they can detect anomalies using simple statistical models and, in more complex cases, using predictive artificial intelligence (AI) algorithms.
In case of irregularities, production workers are informed in advance
about the required maintenance work.
During the welding process, where approximately 15,000 welds are made a day at BMW, the software collects data from welding clamps around the world and transmits it to the cloud to detect possible faults in advance during production. Then they are analyzed using algorithms and assessed for compliance with the standards adopted by the company.
At the BMW Group plant in Regensburg, conveyor control devices transmit data 24 hours a day, including: electrical currents, temperatures and positions to the cloud platform. The data is analyzed on an ongoing basis, and it is possible to create AI models based on the data about the conveyors. The models then detect anomalies and are thus able to provide clues to technical problems.
The benefits of predictive maintenance
As the costs of renewable energy sources decrease and the demand for renewable energy increases , companies are looking for ways to optimize their operations and reduce costs. One way to do this is to use machine learning to optimize maintenance schedules in renewable energy systems.
In practice, predictive maintenance helps ensure maximum system performance and minimizes the risk of equipment failure. This can easily reduce costs by ensuring that maintenance is only performed when needed. It is also important to reduce time and money spent on maintenance while reducing the risk of equipment failure.
Predictive maintenance can significantly reduce maintenance and downtime costs, improve equipment reliability and performance, and increase overall organizational efficiency. This type of maintenance can also be used in a variety of industries, including manufacturing, oil and gas, transportation and logistics, and many others.