What is predictive maintenance, how it is done? Reduce maintenance costs and increase production with AI and machine learning.
The question "What is predictive maintenance?" frequently arises in the maintenance processes of industrial facilities. Unlike traditional maintenance methods, predictive maintenance, supported by sensors, machine learning algorithms, and artificial intelligence, anticipates failures in advance, thereby reducing production costs and increasing efficiency.
Maintenance strategies have gained great importance in today's industrial processes. Improvements in maintenance and repair have become factors that directly affect the productivity and cost factors of businesses. Traditional maintenance strategies generally consist of planned and periodic checks and repairs. Although this strategy meets our expectations in the industry, it is only capable of covering periodic inspection and post-failure maintenance.
With developing technologies, a more effective and innovative maintenance strategy emerges: Predictive Maintenance. What is predictive maintenance, a strategy implemented through the collaborative efforts of different disciplines in the maintenance and repair field, and one that will continue to develop?
Predictive Maintenance uses sensor technologies, artificial intelligence, machine learning, and big data analytics to monitor and analyze the instantaneous performance of machines, storing potential errors encountered in a database. This allows a possible failure to be prevented before it even occurs. This strategy reduces unplanned downtimes and maintenance/repair costs in production while increasing on-site efficiency.
Maintenance strategies in businesses support long-term operation, sustainability, and an increase in efficiency. It has also been observed that they reduce cost items within businesses. While the stages at which maintenance strategies are applied differ according to the variety of system failures, there are also strategies applied at specific intervals.
Although there are many maintenance strategies, the one emphasized here is Predictive Maintenance. Researchers generally evaluate maintenance strategies under three main headings.
Corrective maintenance requires intervention in the event of a failure. When the system issues a warning, necessary measures are taken, and the fault is rectified. When the system is in a nominal operating state, no intervention is made. Corrective maintenance is a simpler activity compared to other types, but when repair is required, the cost increases. Furthermore, if potential damage cannot be prevented, it carries the risk of leaving uncontrolled damage in the system. Considering these possibilities, corrective maintenance may resolve immediate problems but can cause major damage to the business in the long run.
Preventive maintenance is a technique performed according to a determined schedule before a failure occurs in the process. It extends the life of the equipment within the system. It is also a sustainable approach. It is implemented according to a planned calendar, and no intervention is made outside of these periods within the scope of preventive maintenance.
Predictive maintenance focuses on measuring when maintenance actions are necessary before a failure occurs. It is concerned with condition monitoring and fault detection.Prediction tools such as machine learning algorithms and statistical techniques are used for fault detection. Based on the equipment's past conditions and monitoring data, features reflecting the equipment's health status can be extracted to create a data-driven model for condition prediction and fault diagnosis.Predictive maintenance is a sustainable approach; thanks to active control mechanisms, it prevents potential damage before it happens and aims to master all fault types within the system by collecting the actions taken in a database.
Predictive maintenance utilizes different scientific disciplines, applications, and approaches during implementation. Machine Learning and Artificial Intelligence contribute significantly to the applicability of predictive maintenance. Fundamentally, it can also be evaluated in terms of transforming systems into a real "living" form. Jardine, Lin, and Banjevic have grouped the approaches used in maintenance into three main classes:
Artificial Intelligence (AI) encompasses technologies that enable machines to think similarly to human intelligence. In predictive maintenance, AI can analyze massive amounts of data quickly and make predictions about the future states of machines. AI not only monitors the condition of machines but also assists humans in making decisions regarding them.
Machine Learning is an AI method that allows computers to think like humans. Using data obtained from machines, machine learning discovers connections between this data and makes predictions for the future. For example, a machine's vibration data can be compared with past failure records to predict when a specific part will fail.
In conclusion, when predictive maintenance, machine learning, and artificial intelligence come together, a highly effective method emerges for ensuring machines operate efficiently. This allows businesses to know when their machines require maintenance, resulting in fewer disruptions in production processes and lower costs.
The most critical part of a Predictive Maintenance application is ensuring its setup is systematically well-planned. Otherwise, outputs reflecting increased efficiency may not be obtained. The steps to be followed for integrating predictive maintenance into the system are as follows:
