The Powerful Advantages of IoT-based Predictive Maintenance for Manufacturing Industries

Mar 11, 2022
4 min read

Manufacturers have always looked out to practice new approaches for enhanced maintenance and protection for all of their machinery and equipment. Among these latest methods, IoT-based predictive analysis and maintenance have shown remarkable results recently. Since its beginning, predictive maintenance has transformed the ancient and traditional maintenance methods into extremely advanced and efficient ways. In contrast to conventional maintenance schedules, predictive maintenance schedules specific tasks only when needed, rather than conducting routine maintenance on all equipment. IoT performs predictive maintenance through sensors, gateways, and management systems to predict equipment failures and prevent them from occurring. Predictive maintenance has become a central part of business strategy for many organizations. Among the industries benefiting from predictive maintenance are oil and gas, the food and beverage sector, manufacturing firms, and IT services. This article describes how IoT-based predictive analytics can be used to predict maintenance within a manufacturing environment. Manufacturing companies involve heavy machinery and equipment, which can become a million-dollar loss in case of any breakdown.

Before you read further, it is necessary to have a clear concept of what predictive analytics and predictive maintenance denote. Predictive analytics uses historical data to identify likely future outcomes based on statistical modeling and machine learning techniques. In manufacturing sites, IoT is used to collect machine information (such as temperature, voltage, current, and vibration) and wirelessly transmit the collected data to a cloud-based service platform in real-time. There is adequate bandwidth to handle large amounts of data when using wired and wireless connectivity solutions. This data allows exerts to analyze and reach predictive solutions. On the other hand, IoT-based predictive maintenance or PdM identifies patterns and predicts equipment failures by monitoring its condition during normal operation. It uses five basic components to carry out these resolutions. These components include predictive analytics, predictive maintenance software, sensors, data communication, and central data storage. Predictive analytics, along with machine learning, will notify management of potential challenges before they arise, making the entire process faultless. Reducing production interruptions brings money savings, as well as improved reliability in delivering products on time.

Benefits of IoT-based Predictive Maintenance

It is seen that manufacturers who have adopted predictive maintenance have witnessed a wide range of benefits. These benefits include:

Reduced Maintenance and Material Cost

The data utilized in predictive maintenance allows you to perform optimal inspection and maintenance of machinery without any occurrence of breakdown, which could have cost a tremendous amount of money and time. Studies, therefore, confirmed that the cost of maintenance operations is reduced by 20-50% with the application of predictive maintenance.

Reduced Machine Failure

IoT-based predictive maintenance provides data that allows you to customarily monitor the working conditions of any machinery. This regular observation avails in identifying components that require repair or replacement, thus preventing the degradation of the entire workflow. It is proven that the implementation of predictive analytics in manufacturing sites has reduced the number of unexpected equipment failures by approximately 60%. Predictive maintenance has, in turn, resulted in the incremented service life of machines.

Reduced Repair Time

Maintenance done using predictive methods reduces the time required for repairs and replacements. The unplanned time utilized due to the breakdown of machines results in the overall loss of companies. Regular analysis of the condition of the machines is necessary to identify the specific problems on each. Each repair can be planned by maintenance personnel in this way. An IoT-based predictive maintenance program identifies damage taking place on equipment along with other machinery. Predictive maintenance works on a formula of estimating the mean time between failures. This meantime allows experts to determine the exact number of hours required to perform repair or replacement without further allowing the machinery to promote high maintenance costs.

Enhanced Operator’s Safety

With predictive maintenance, the risk of destructive machinery failure reduces, and with it, the rate of injury or death of a person operating such machinery goes down.

Increased Production

IoT-based predictive maintenance has helped manufacturers gain a raise in production with the help of a process parameter monitoring method. It improves the operating efficiency and productivity of manufacturing and processing plants. A reduction of machine downtime results in increased output and a higher overall equipment effectiveness score.

Increased Life of Parts

An early prediction of machinery or parts failure is directly proportional to the increased service life of industrial plants and machinery. It is crucial to maintain equipment and other plant systems by following a condition-based predictive maintenance program. A predictive maintenance program increases machine service life by five years after installation. Besides extending the life of plant equipment, this method reduces the severity of the damage, preventing defects propagation. One benefit of predictive maintenance is that it can automatically estimate the mean time between failures (MTBF).

It determines when replacing machinery is more cost-effective than continuing to absorb costly maintenance costs. The MTBF of any parts or machinery reduces every time a major repair or reconditioning occurs, and predictive maintenance plays a crucial role in it.

Competitive Advantage

As a result of the increased sense of predictability and confidence of predictive maintenance, company branding can be improved while customer satisfaction increases.

Conclusion

In a nutshell, predictive maintenance lifts manufacturing businesses to maintain a balance in keeping costs down and increasing asset availability. It improves the overall operation of manufacturing sites, and with techniques based on data collection, these methods further reduce the annual operating costs. The increasing use of predictive maintenance is confirmed to provide continuous resolutions and action measures necessary for addressing the systems before they fail, break, or run erroneously. A PdM-based business operation is the next evolution in how humans use machines for production. Manufacturing sites and processing plants will continue to boost their efficiency by utilizing predictive maintenance in the future to meet all competitive and challenging requirements.