Emerging Technologies: Big Data Analytics for Predictive Maintenance

Predictive Maintenance and its importance

Predictive maintenance involves identifying the state of degraded components in equipment through the use of statistical models over large data sets. Hence, most types of failures that occur after a certain degradation process from a normal state to an abnormal state do not occur instantaneously, which is the basis for the prediction of equipment condition.

Big Data Analytics Transforming Predictive Maintenance with Condition based monitoring System

Acondition-based monitoring systemprovides organizations with valuable insight into their systems and is essential for collecting vital data. Predictive maintenance solves a major problem with conventional maintenance strategies, such as planned preventative maintenance. With condition-based monitoring and predictive maintenance, organizations can gain a real-time view of operations and meaningful insights that can help them better manage equipment life cycles.

Applications of Big Data Analytics used in Predictive Maintenance

Predictive maintenance requires machine learning techniques to explore Big Data, extract the relevant data points, and identify meaningful and useful insights.

There are ways in which big data analytics can help you predict equipment failure and reduce downtime cost.

• Transportation and Logistics:Derailments caused by wheel failures account for half of all train derailments. As wheels deteriorate, rails break prematurely and accidents occur due to derailments. Predictive maintenance with big data analytics can be used to monitor wheel performance and detect defects to prevent wheel failures.

• Energy: Wind turbines with generator motors are extremely expensive to fix when they fail. Using Big Data analytics, energy companies can prevent turbine failures and minimize downtime. Predictive models also help companies provide insight into the factors contributing to failure, so technicians are better able to identify the major causes.

• Aviation: The requirement for Big Data Analytics is even greater in this industry since the cabin crew cannot fix tech faults during flights without physical access to the device and the testing environment. Some of the areas where predictive maintenance can play a major role is in the following:

1. The replacement of aircraft engine parts is one of the most critical maintenance problems in aviation, and Big Data Analytics can be very helpful here.

1. Big Data Analytics and Data Science help identify interconnections between factors. One can predict how long a device will last and when it will need to be replaced or repaired. This depends on the type and location of a plane, a specific equipment may perform differently.

Benefits of incorporating Big Data Analytics in Predictive Maintenance

• Big Data analysis not only determines when components need to be replaced before a failure occurs, but also provides an important reference point.

• Service engineers can figure out how to avoid the situations in which these failures occur in order to reduce both replacement expenses and downtime.

• Maintenance time can be optimized to avoid breakdowns and unplanned downtime