Predictive Maintenance has been transforming the landscape of manufacturing industries and the world is ready to experience more. By building an atmosphere where manufacturers can avoid costly, unexpected, and untimely equipment breakdowns- predictive maintenance is greatly enhancing efficiency and productivity by ensuring that the machines work at or near its full potential at all times and also by reducing the time spent online. Using technologies such as smart sensors, machine learning, and an interconnected network of machines, maintenance and operation costs can be minimized and the loss of productive hours will no longer be the stumbling blocks.

In the past, manufacturers had three crucial options for handling the lifecycle of their equipment:

• Organize preventive maintenance
• Uninvited machine failure
• Guess when an intervention could possibly limit the failure

Traditionally, manufacturers had to remove their types of equipment from the production hours and determine these emergency performance hurdles. Moreover, as per the research, the unexpected downtime is costing the industrial manufacturers approximately $50 billion a year. Well, it is no surprise that industry giants would preferably be searching out for a more viable solution.

Why Predictive Maintenance?

Popularly known as Industry 4.0, we are in the middle of what is being reckoned as the fourth industrial revolution. Predictive maintenance leverages the power of the Internet of Things (IoT) to entirely transfigure how things are made. In the earlier days, administering an equipment lifecycle predominantly meant running the machines until they failed, preventative maintenance, or making a guesstimate as to when an intervention might counter failures.

However, now with predictive maintenance, manufacturers and producers are not left to making educated and probable guesses. Rather, they monitor the continuous performance of machines under favorable & normal conditions and search out for subtle differences & dissimilarities that might not have been prominent through traditional inspections. These variations in performance serve as alarms of where the troubles would hit hard. Maintenance providers and operators will, therefore, be able to plan their downtime better, order parts, and slash disruption. These steps have proved to reduce the maintenance costs by 30-40% and reduce downtime by up to 50%.

Also Read 4 Ways Machine Learning Is Transforming The Manufacturing Industry

Improved Analytics

IoT has allowed the manufacturers to collect and analyze data on their machinery and pieces of equipment in real time, thereby, preventing costly & lengthy stoppages or delays. The manufacturers are now able to utilize the measurable data received from IoT sensors to monitor & track the conditions of their machinery. The tools that are used to evaluate the data make use of advanced algorithms, seamless connectivity, and machine learning to determine the problems and offers the opportunity for preventative measures.

With all the benefits and advantages that predictive maintenance has to offer, the manufacturing sector is already awakening to its applications. Some of the already delivered benefits include:

• Fine tuning the plans for production
• Forecasting maintenance events
• Extending the life cycle of equipment and machinery
• Customizing component-wise schedules for maintenance

All in all, digital transformation, automation, and data-driven decision making is steadily changing the face of manufacturing. By 2019, 75% of the manufacturing giants will be upgrading their operating models with analytics-based situational awareness and IoT in order to increase speed time and reduce risk to the market. In a similar context, I believe that predictive maintenance will eventually take over the traditional methods and completely reform the manufacturing industry in the not-so-distant future.

What are your thoughts? If this article was not enough to quench your thirst for predictive maintenance or want to know more about it, let us know. We will be happy to assist.