Internet of Things (IoT) based Predictive Maintenance solutions are helping in revamping the traditional industrial maintenance processes.
Advancements in IoT technologies have enabled machine-to-machine (M2M) communication and collection of relevant data. Predictive maintenance solutions leverage such data and IoT technologies, to help companies reduce costs of maintenance by adopting a proactive approach. This approach is proving to be a value-add solution. This is because, IoT enabled Predictive Maintenance solutions help shop-floors, assembly lines and other industrial or enterprise set-ups to avoid sudden machine failures and related operational delays
How an IoT enabled predictive maintenance solution works?
Following are the main components of an IoT (Internet of Things) based solution:
- IoT based Sensor Nodes/Network
IoTsensors integrated with the industrial assets send steady stream of data captured from the machines and industrial environment. This data is then stored and processed in a Cloud backend. - IoT Gateway
IoT Gateway as a hardware device or a virtual software code, acts as a communication bridge between IoT Sensor Network and Cloud Server. IoT gateway devicehas a layered architecture. We have listed the layers to help you get a better understanding of IoT gateway:
Hardware Platform: The hardware platform is selected based on the complexity of IoT application(s) that need to be deployed. Hardware platform defines the processing power & memory specifications of the IoT Gateway.
Operating System: Selection of an OS depends on the existing legacy systems. It is always a best practice to choose the OS which is compatible with the existing systems to save costs and seamless integrations.
Analytics Engine: This layer converts the raw data collected from the sensors to actionable insights
Integrated Application development platform and Device Drivers: This layer supports development and/or addition of new devices, applications or systems to the IoT network - Cloud Backend
Cloud backend has to receive the tons of data from the IoT gateway, store, process and send it to the user interface (app / web dashboard). Advanced IoT cloud applications are powered by machine learning and artificial intelligence. - Mobile App or Web dashboard
IoT mobile / web dashboard is the main HMI (human machine interface) component that present the data with actionable insights to the end user. Users can monitor and control the assets from anywhere in the world. IoT dashboards has graphs, charts, control switches and other widgets to visualize the data from the physical world.
Building a business-case for investment in an IoT enabled Predictive Maintenance Solution
IoT predictive maintenance solution helps you to Reduce Machine Downtime, increase Asset Availability and increase Customer Satisfaction. Below are some real-world success stories that can help to build a business-case for your organization
1. Predictive Maintenance in action at VR Group
VR groups, a railway company in Finland, has decided to shift the maintenance from a reactive approach to predictive maintenance using IoT powered systems. They have installed sensors at fault points and connected to analytics systems to monitor the systems. The analytics systems converts the raw data into actionable, analytics powered report. Predictive analytics has helped VR groups to maximize the maintenance events frequency and cut down the maintenance work by one-third and make it as cost effective for the business.
2. Predictive Maintenance in a Mining Company
Advisian has partnered with a large iron ore mining company in Australia to implement predictive IoT solutions at the mines, processing units and logistics. The solution helped the mining company to monitor and keep in check the asset’s health, predict breakdowns and ensure proactive maintenance based on the collected historical data. With the help of this continuous monitoring solution, the mining company was able to reduce equipment downtime and save cost.
3. Predictive Maintenance in Wind Power Company
A major wind power operator has partnered with Roland Berger to implement Predictive maintenance service to support remote surveillance. The wind power operator implemented the IoT predictive maintenance solution and analyzed the assets using an asset tracking software. This provided to be an efficient method to reduce operational and maintenance costs and increase revenues. This solution was able to monitor the health condition of the assets and alerts the team with predictive alerts and helped them to avoid breakdowns and significant loss in business
4. Predictive Maintenance in Petrochemical
Dynogram has implemented predictive maintenance solution in one of its oil and gas customer’s unit. The advanced solution stored data in central repository and enabled efficient remote monitoring. The predictive solution compared the real time data with the historical data to identify potential equipment failure. This helped the oil and gas company to reduce resource maintenance without the need to replace permanently damaged resources. The solution has helped the company to efficiently shoot down the maintenance cost.
Conclusion
Predictive maintenance is a must for industrial units for the efficient use of resources and cut down the cost on resource maintenance. Have you implemented IoT predictive maintenance solution in your company? What are the benefits your company has realized using the predictive solution? Please share your experience.
