Predictive Maintenance Using Machine Learning - Javatpoint
Predictive maintenance is one of the most important techniquesfor monitoring future system failures and schedule maintenance. Although system failure is a very general issue that can occur in any machine, predicting the failure and taking steps to prevent such failure is most important for any machine or software application. In the present situation, when we are entirely dependent on machines and computers, system failure affects the entire lifecycle to a great extent. System failure also leads to substantial business losses if we talk about organizations. Still, suppose we accurately predict these failures by adapting corrective actions or predictive measures. In that case, we can easily avoid such failures and prevent the entire system from breaking down, and here Predictive maintenance comes into the picture. In this topic, "Predictive maintenance using Machine Learning," we will learn about Predictive Maintenance systems (PMS), how this system is used to predict failures, various corrective measures to avoid such failures, machine learning techniques for predictive maintenance, advantages of adapting machine learning in predictive maintenance, etc. So, let's start with a quick introduction to Predictive Maintenance.
Predictive maintenance is a techniqueto monitor the performance of a structure or a piece of equipment during operation. It is the method of data collection over time to monitor the state of equipmentto detect anomalies or possible defects in equipment, so these defects can be fixed before the failure occur.
The primary objective of predictive maintenance is to find patterns that can help predict and ultimately reduce the failures of machines. Vibration analysis, oil analysis, thermal imaging, equipment observations, etc., are a few common examples of predictive maintenance.
Although predictive maintenance is a corrective measure to reduce system failure when it comes along with machine learning, it enables you to run automated data processing on a sample dataset or your dataset. Predictive maintenance by using Machine Learning tries to learn from experience or old data and use live data to detect the patterns of system failures.
Predictive Maintenance with Machine Learning need to perform three main tasks, which are shown in given image:
The general predictive maintenance method also has some issues, but machine learning can resolve various challenges associated with maintenance activities, such as unpredicted failures. Therefore, it is advantageous for optimizing maintenance work and avoiding severe consequences during unplanned downtime periods.
The integration between machine learning and predictive maintenance is classified under two classes, as follows:
Predictive maintenance primarily focuses on detecting upcoming possible failures in the system as well as it also ensures that we will not have to run maintenance frequently. Hence, it also saves money as well as time. Nowadays, companies are using Predictive analytic Software for equipment supervision that helps prepare maintenance and schedule repairs to maintain the good condition of the equipment. Therefore, predictive maintenance enhances the overall equipment effectiveness or OEE.
Predictive maintenance methods with machine learningcan resolve various challenges associated with maintenance activities, such as unpredicted failures. Thus, this kind of integration is worth exploring to optimize maintenance work and avoid severe consequences during unplanned downtime periods.
Although predictive maintenance is solely crucial for machines,it gets much more effective when combined with machine learning. Predictive maintenance with Machine learning helps machines or systems predict various types of machine failures and reduce themthroughvarious specific techniques. These techniques involve collecting data over time with sensors to monitor failures.
Firstly, a sensor is added to respective machine systems to monitor, and then it stores time-series data for respective operations. The collected data, fetched by sensors for predictive maintenance, shows a time series containing timestamps and sensor readings. Further, this timestamped data enables the ML-based application to predict the failure accurately with precise timing.
There are mainly two machine learning-based predictive maintenance approaches as follows:
The classification approach provides the outcomes in Boolean (True/false) format and provides more accurate prediction with fewer data.
Predictive maintenance is primarily used to detect upcoming system failures and prevent them using appropriate corrective measures. Using machine learning with predictive maintenance, we can analyze a massive volume of data and detect all possible failures that may lead to various financial and business losses. There are several predictive maintenanceapplications with machine learning, including manufacturing plants, power plants, railways, aviation, oil & gas industries, logistics & transportation, etc.
Predictive maintenance is widespread among production companies. It is also too expensive and unsuitable for components that can be down for hours or even days without harming the production cycle. Some famous companies are using predictive maintenance technologies to develop their business. These are as follows:
Infrabel is a Belgium government-owned public limited company that deals with building Belgium's railways network and infrastructure.
It is currently dealing with building tracks, switches, bridges, tunnels, overpasses, and signals. Further, it is also working to monitor tracks, railway ties, and overhead lines.
The technology used to run Predictive Maintenance:
Reported Benefits:
Komatsu Ltd. is a well-known Japanese manufacturing company that deals with construction, mining, forestry, and industrial equipment. Further, industrial machines and surface and underground mining equipment are under monitoring.
The technology used to run Predictive Maintenance:
Reported benefits:
Mondi is well known global leader in packaging and paper. It provides services across the globe, including Europe, North America, and Africa.
The technology used to run Predictive Maintenance:
Reported benefits:
Chevron is one of the most famous American multinational energy corporations dealing with oil and gas. However, pipelines system and oil wells are still under monitoring.
The technology used to run Predictive Maintenance:
Reported benefits:
Although we have already discussed the primary objective of predictive maintenance using machine learning, there are also various other advantages of adopting predictive maintenance techniques. These are as follows:
Predictive maintenance is primarily used in the manufacturing and Automotive industries, but it is not limited to these two industries. Predictive maintenance reduces the maintenance costs of the systems, but it also helps reduce unexpected failures, overhauls, and repair time by approximately 60%. Further, it increases the machine's or device's uptime. Most manufacturing leader industries are using and understanding the importance of predictive maintenance using Machine Learning for monitoring the complex and expensive systems; thus, future industries will entirely rely on it.
Moreover, building a machine learning model for predictive maintenance does not follow a single approach. The strategy to build the model will ultimately depend on maintenance tasks and specific challenges. For a different type of failure, we may need a different ML model.
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