IoT For Predictive Maintenance — Benefits And Challenges
The concept of predictive maintenance in industrial operations has gained considerable momentum. This is because of the proactive approach that helps maximize efficiency & minimize downtime. And at the core of its lies the Internet of Things (IoT), a network of interconnected devices and sensors that enable real-time data collection and analysis. By leveraging IoT, organizations are now equipped to monitor, analyze, and predict equipment health and performance, allowing them to take preemptive measures before costly breakdowns occur. IoT consists of sensors that can be placed or built into equipment to monitor and manage various variables that can indicate potential equipment issues and send out alerts for maintenance.
IoT in predictive maintenance involves using the data collected through the IoT sensors and devices to access equipment, assets & machinery to identify patterns and predict any potential outages, failures, or other issues related to maintenance.
With this blog, I’ll be looking at the benefits and challenges of using IoT for predictive maintenance.
Benefits of using IoT for predictive maintenance:-
● Reduce maintenance costs: All assets have several costs attached and associated with them, and the cost of any unexpected breakdown or failure can contribute significantly to the total cost of ownership. Companies can increase the life of assets and save money by being able to predict any maintenance issue or failure. This has greatly impacted asset-intensive industries and can decrease business losses because of unaccounted repairs, resulting in huge savings. IoT-based preventive maintenance uses historical data from multiple sources from various sensors and devices to make accurate predictions about asset health, its utilization, possibility of failure, enabling businesses to make informed decisions based on such information. Maintenance based on IoT data allows enterprises to systematically schedule inspection and care optimally to avoid unplanned downtime and reduce losses.
● Increase asset utilization: IoT-based maintenance helps ensure more efficient use of existing assets by forecasting maintenance-related issues or failures. It can help increase asset utilization by identifying internal or external causes for any delays. It can also help identify concerns about any equipment before they become operational and provide early warnings helping improve asset reliability, availability and performance.
● Extend asset life: Using IoT-based predictive maintenance, it’s possible to monitor, maintain and even optimize assets to get better utilization and performance. By scheduling repairs and maintenance before assets become due for maintenance or go into downtime, the life of any asset can be extended.
● Improve field crew efficiency: IoT-based monitoring enables companies to monitor their field assets’ health in real time and at scheduled intervals. A complete 360 view can help the asset’s work planning, prioritization, and scheduling. Any unplanned downtime can cause logistical issues and require reallocating field service crews and spare assets. This translates into a complete rescheduling of assets and increases the cost. IoT in predictive maintenance can help avoid such issues by improving utilization and response time.
● Safety and compliance: Predictive maintenance helps companies anticipate and address possible risks and potential issues before they make their impact felt. Data from IoT devices enables companies to take appropriate steps to mitigate risks. Analysis of data over some time can help identify potentially hazardous conditions and estimate their impact. Integrating Human Resource Systems (HRM) can allow trigger instructions for resource allocation while keeping the exposure levels below the threshold values, which would comply with existing regulations.
Challenges of using IoT for predictive maintenance:-
● High investment cost: Implementing IoT-based predictive maintenance is an expensive affair. The cost of IoT devices, training, and building a secure IoT network becomes prohibitive initially. Other costs involved are trained data scientists who would be able to analyze the data shared by the IoT devices.
● Incompatibility: In many cases, it may be possible that the assets are not compatible with the smart sensors or IoTs. This would mean the assets must be modified or altered to fit sensors into these assets. While this is not an uncommon issue, asset modification costs also increase the final cost of asset ownership.
● Integration with Business Intelligence System: A fully functional predictive maintenance strategy requires a secure IoT Network to send, receive, process and store data. Often there is a need to integrate Manufacturing Execution System (MES) or other methods and supervisory control systems with the IoT technology and software. Application Programming Software (APIs) also needs to be in place to connect all these different systems and integrate the data. These are big challenges that need to be worked out. These are the areas where skilled software companies with experience in IoT in predictive maintenance can help with smooth and seamless integration.
● Privacy and security concerns: The collection and storage of data is always a sensitive issue. Using IoT for predictive maintenance would mean collecting and storing huge volumes of data daily. Organizations are concerned about the security and privacy of their data. This is a challenge that has to be addressed and the focus has to be on securing the network.
Conclusion
From reducing equipment maintenance costs, utilizing assets with proper allocation, and extending machine life to optimizing field crew efficiency, IoT-based predictive maintenance offers many benefits. IoT has a big role, and the demand for complex and comprehensive IoT solutions will grow over time. Organizations can reap the benefits of technology by approaching experts in IoT in predictive maintenance who can offer full-package solutions.
