In the industrial sector, unscheduled downtime can cost businesses up to $260,000 per hour and result in missed deadlines, revenue losses, and production stops. While traditional approaches struggle to provide real-time insights and adapt to changing conditions, Predictive Maintenance (PdM) offers promise. IIoT is a solution to these problems.
Predictive maintenance: what is it?
The art and science of anticipating equipment breakdowns before they happen is known as predictive maintenance. It entails evaluating an asset to estimate when it could break, planning preventive maintenance, and identifying hazards and ways to avoid them. Reactive maintenance, on the other hand, fixes machinery after it breaks down. Predictive maintenance is not a novel idea, though. Since the invention of complicated machinery, manufacturing companies have been employing scientific ways to predict when equipment will break. This has created a ceiling effect, whereby the amount of data that organizations are gathering exceeds the capacity of conventional PdM practices.
The Potential of IIoT globally
IIoT is very important to the Internet of Things, potentially boosting the world economy to $14.2 trillion by the end of 2030. Sensor packages, analytics software, and remote diagnostic tools are some of its distinctive products. These instruments shorten reaction times and improve the accuracy of predictive maintenance. Predictive maintenance in the past depended on certain sensing tools or manual data collection. IIoT enables real-time data collection, analysis, and modelling, leading to a deeper knowledge of machinery efficiency.
Advantages of predictive maintenance
1. Lower Expenses
Organizational expenses have decreased by 12% as a result of the Internet of Things (IoT) integration with predictive maintenance, according to PwC research. This is because the time and effort required for repairs are decreased since the system can anticipate possible faults and act before they become more serious. The World Economic Forum suggests that foresight can save up to 30% on maintenance costs, prolong machine life, and reduce downtime by up to 50%. Furthermore, well-maintained machinery uses less energy, saving industrial establishments a substantial 5% to 20% on energy costs. Predictive maintenance with IIoT integration optimizes many activities and results in immediate cost savings from fewer repairs, opening the door for a more efficient and economical business model.
2. Enhanced Efficiency
Efficiency may be greatly increased by using Industrial Internet of Things (IIoT) technology in predictive maintenance. Smart sensors can track the condition of equipment, identify abnormalities, and send data to centralized systems. Based on past data, machine learning algorithms may be integrated with advanced IIoT systems to anticipate possible errors. By anticipating the need for equipment maintenance and scheduling it for off-peak times or when other machinery becomes available, predictive analytics enabled by IIoT may guarantee continuous operations. This approach optimizes processes and lowers downtime by up to 9%, increasing machine uptime and yielding significant increases in productivity and profitability.
3. Extended Life of the Machine
Predictive maintenance techniques enabled by IIoT technology can increase machinery lifespan by up to 20%. IIoT sensors can continually monitor machinery, gathering information on vibration, humidity, temperature, pressure, and vibration. Timely treatments and catastrophic failures are avoided by this early diagnosis. IIoT systems can anticipate future wear patterns thanks to advanced analytics and machine learning, which allows for proactive replacements or modifications. This proactive strategy ensures that machinery runs at peak performance for an extended amount of time by reducing its load.
Conclusion
Predictive maintenance relies heavily on IIoT since unanticipated downtimes may be extremely expensive. Businesses may prevent and mitigate equipment breakdowns by utilizing real-time data analytics. A new age of profitability, sustainability, and efficiency in industrial processes is brought about by this integration. Using IIoT-enabled predictive maintenance will improve sustainability, spur growth, and influence manufacturing’s future. It is vital for competitiveness.
