More and more companies are choosing to have predictive maintenance in manufacturing, a series of actions and techniques applied to anticipate errors. They predict if any machine will break down and avoid negative repercussions.
In this article, we will see in-depth what predictive maintenance is in manufacturing and what are the advantages of having one.
It is a method that uses tools and analysis techniques to detect anomalies in machinery and processes within a company. It makes it possible to anticipate market movements or fluctuations in energy demand.
Predictive maintenance uses data analysis to anticipate system failure and is part of the Industrial Internet of Things, known as IIoT. Today, it has become a necessity in Industry 4.0.
By implementing predictive maintenance, it is easier to control and remedy common damages in the manufacturing routine, reduce costs, and maximize productivity.
It is a strategy based on the technical follow-up of machinery. First, a complete analysis of the equipment is performed, such as its conditions, its performance level and perceptible and imperceptible signals. With this, it is possible to know the state of the machine.
Discover the series of techniques that are applied for predictive maintenance in manufacturing:
Vibration analysis is a fundamental technique in predictive maintenance. It consists of monitoring the vibration rates of the moving parts of a machine. It is widely applied in the metalworking industry, where equipment such as milling machines, lathes, CNC machines and drills are subjected to regular inspections.
The objective of vibration analysis is to identify variations in the dynamic forces of the machine, which can reveal problems such as excessive backlash, lack of lubrication, bearing or gear wear, and misalignment.
By detecting these anomalies early, it is possible to perform maintenance interventions without interrupting the normal operation of the machinery, ensuring its efficiency and prolonging its service life.
The use of ultrasound is another key technique in predictive maintenance, especially effective for detecting invisible leaks in compressed air, gas and vapor transport systems. By analyzing sound frequencies, problems that are not detectable by conventional methods can be identified.
Although its application can be more complex, ultrasound allows for locating leaks and avoiding unnecessary costs associated with energy losses or hazardous materials. This technique is essential for maintaining the integrity and safety of industrial systems.
Thermography focuses on the analysis of the temperature level emitted by machinery. Using infrared devices or sensors, thermal radiation from machine components is measured. Thermographic images show areas with anomalous temperatures, which are represented in warm tones such as red, orange and yellow.
This method makes it possible to detect problems such as overheating, abnormal friction and electrical insulation faults that might go undetected in routine visual inspections. Thermography is particularly useful because it can be applied without interrupting equipment operation, facilitating ongoing predictive maintenance.
Oil analysis is crucial for assessing the condition of lubricants and detecting the presence of contaminants, oxidation or microorganisms that could compromise the chemical properties of the oil. This is usually performed in specialized laboratories or with specific equipment, such as the ASTM color test.
By identifying signs of premature wear or contamination, preventive measures can be taken to avoid further damage to the machines, ensuring optimum performance and prolonging the life of the equipment.
Crack analysis is a technique for detecting cracks in metallic systems, such as piping, before they become serious failures. This process involves magnetizing the component and applying metal shavings or filings to the surface.
When there is a crack, a break in the magnetic field occurs, causing shavings to accumulate in the affected area, making the defect visible. This technique is efficient in identifying structural problems that could cause catastrophic failures if not addressed in time.
To track the condition of equipment and warn technicians of upcoming failures, predictive maintenance has three main components:
Installed on machines, these sensors send data on machine status and performance in real time thanks to Internet of Things (IoT) technologies. This enables communication between machines and analytics systems.
Data mining and big data applications are used to collect and analyze huge amounts of data. Cloud storage (cloud computing) allows this data to be managed and accessed efficiently.
These models are fed with processed data and use machine learning technologies to establish patterns and comparisons, make predictions of failures and schedule maintenance before they occur.
In addition to the fact that predictive maintenance in manufacturing helps to be in tune with modern industry by being agile, economical and efficient, it also has other great benefits such as:
One of the main benefits of predictive maintenance is its ability to maximize the useful life of equipment.
This allows you to optimize the use of your infrastructure investments, ensuring that equipment operates efficiently for longer and delaying the need for costly replacements.
Implementing predictive maintenance can significantly increase production line productivity. By improving machinery operating conditions and reducing repair time, interventions become simpler and more focused on equipment upkeep.
In addition, machine health data helps managers assess equipment quality and make informed decisions about the need for replacements.
Predictive maintenance allows you to analyze and prevent potential failures at their source. Many times, minor symptoms can be indicative of serious problems in the future, and without specific inspection techniques and the use of technology to monitor and integrate information, these failures could go undetected and escalate to affect productivity and cause losses.
With predictive maintenance, action is taken at the first signs of problems, mitigating failures that could have a significant impact on the organization.
Establishing a failure prevention policy through predictive maintenance can significantly reduce maintenance costs.
In addition, this strategy effectively reduces equipment downtime, as work is focused on preventing failures and interruptions in production. This saves on costly repairs and the purchase of spare parts.
The implementation of a predictive inspection routine increases the safety of operations on the production floor. The error prevention and predictability posture reduces the risk of accidents, preserving the integrity and safety of professionals.
Predictive maintenance can also help identify and fix invisible faults that reduce machine efficiency and increase energy consumption. For example, a machine suffering from overheating or a compressed air system leaking due to cracks in the piping can consume more energy than necessary.
By carefully analyzing the specific conditions of a system, machine or equipment, it is possible to identify these problems and seek solutions that avoid wasting energy, generating significant savings in operating costs.
Many types of industries use predictive maintenance in manufacturing to save costs and improve the quality of their services. Here are some examples that can help you know if it is right for you:
Outages in electricity distribution systems are extremely costly. For this reason, power companies have advanced in the installation of smart grids that allow them to know the status of assets and anticipate failures.
In addition, predictive maintenance is applied to wind turbines, making it possible to detect problems before they cause shutdowns and the consequent loss of production.
Railway networks and trains require constant maintenance to ensure their safe and efficient operation. By using IoT technologies, problem areas in the network can be identified, operating times optimized and the impact of breakdowns reduced.
Sensors installed on tracks and trains collect real-time data, allowing technicians to perform accurate diagnostics and schedule maintenance interventions before significant failures occur.
In the shipping industry, monitoring the condition of ship machinery is crucial to maintaining efficient and safe operations. Predictive maintenance allows diagnostics to be performed remotely from shore-based offices, optimizing maintenance operations and ensuring maximum vessel performance.
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