Predictive Maintenance Using Machine Learning

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.

What is 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.

Predictive maintenance using Machine Learning

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.

Machine Learning Classes in Predictive Maintenance

The integration between machine learning and predictive maintenance is classified under two classes, as follows:

• Supervised class: It refers to the class that stores information in the database, used for failure predictions. This class is suitable for specific applications where failure events can be predicted between two maintenance cycles.
• Unsupervised Class: It refers to the class which does not have any information related to maintenance requirements.This class is suitable for applications where failure events cannot be predicted between two maintenance cycles. Hence, unsupervised class is used as the best alternative for such scenarios.

Why use Predictive Maintenance?

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.

Machine Learning Techniques for Predictive Maintenance

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:

• Classification approach: This prediction approach predicts the feasibility of any failure in upcoming n-steps. Further, it also tells us whether there are any possible failures in the remaining steps.

The classification approach provides the outcomes in Boolean (True/false) format and provides more accurate prediction with fewer data.

• Regression approach: This prediction approach predicts the time of impending failure in the respective system. This is also known as Remaining Useful Life (RUL).
  Unlike the classification approach, the regression approach consumes more data to predict the outcomes, and further, it also delivers detailed information about the impending failure. Nowadays, most industries adopt these machine learning-based predictive maintenance techniques to detect system failures and prevent them in advance.

Applications of Predictive maintenance using Machine Learning

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.

• Manufacturing and IoT: Predictive maintenance is widely used in manufacturing industries to supervise the production procedure through timely detection of faults and eliminate them before they malfunction using IoT. Hence, it increases the overall efficiency of the manufacturing process.
• Automotive and Vehicles: Various technologies connect vehicles to the sensor already enabled in the vehicle by manufacturers or dealers. These sensors collect all information and produce a massive amount of data directly retrieved by manufacturers or dealers, who warn us about any possible failure and corrective measures to prevent them before any malfunction.
• Utility Suppliers: Predictive maintenance techniques help utility suppliers to performbetter internal work, such as predicting early traits of supply, demand issues, outage issues, etc.
• Insurance: Various banking and financial institutions use predictive maintenance techniques to predict accurate analytics on disastrous weather conditions.

Predictive Maintenance companies

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:

1. Infrabel

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:

• Power consumption meters
• Temperature sensors
• Cameras
• On-premise database
• Machine learning engine

Reported Benefits:

• 7,000 Km of rail lines are checked automatically,
• Increase staff safety

2. Komatsu Ltd.

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:

• Vibration sensors
• Pressure sensors
• Oil condition sensors
• Ultrasonic sensors
• Temperature sensors
• Cameras
• Azure SQL database
• Machine Learning Engine

Reported benefits:

• Reduced production delays
• Reduce maintenance downtime
• Reduced cost of spare parts and supplies
• Improved fuel consumption

3. Mondi

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:

• Pressure sensors
• Velocity sensor
• Oracle database
• Machine learning engine
• Temperature sensors

Reported benefits:

• Reduced machine downtime
• Less wasted raw materials and also saved 50,000 Euros/years.

4. Chevron

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:

• Pressure sensor
• Temperature sensor
• Vibration sensors
• Seismometers
• Azure IoT Hub/Edge
• Machine learning engine

Reported benefits:

• Reduced maintenance cost
• Reduced maintenance cost
• Elimination in breakdowns

Advantages of Predictive Maintenance using machine learning

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:

• It helps to reduce average maintenance costs by 50%.
• It helps to reduce the chances of unexpected failures in machines by 55%.
• It decreases total overhaul and repair time by 60%.
• It also reduces spare parts inventory by 30%.
• It increases machinery Mean time between failures by 30%.
• It helps to increase uptime by 30%.

Conclusion

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.