How IIoT is Transforming Predictive Maintenance
The objective of predictive maintenance is to foresee potential machine failure with the help of machine learning algorithms provided with vast quantities of data.
Maintenance can be one of the most important activities in plant operations. Maintenance teams have to perform regular inspections and adjustments to ensure machines operate under optimal conditions. This is essential for efficient and cost-effective factory operations.
Regular maintenance reduces the chances of unscheduled downtimes, although it cannot eliminate the possibility entirely. Unscheduled downtime causes disruptions in the plant and across the supply chain. It is highly desirable to reduce the number of unscheduled downtimes to a minimum. This is possible with the help of predictive maintenance.
Predictive maintenance uses a vast amount of data on machine operations to predict potential machine failure. It can also give insights into how the failure will occur. This information can be used to perform root cause analysis to identify the underlying problem that will cause the potential fault. Predictive maintenance can be performed with this knowledge to prevent the fault from occurring.
Predictive maintenance is an essential tool in modern plant operations. One of the key requirements for effective predictive maintenance is a vast quantity of historical and real-time data of machine operations. In the industry 3.0 era, when computers began running traditionally manual operations, this required regular collection and recording of operational data. This was a cumbersome task for the maintenance team. But this changed in industry 4.0 with the use of IIoT devices.
Figure 1. Smart sensors are those which are designed to collect and transmit data for most effective cloud computing - especially for predictive maintenance. Image used courtesy of Banner Engineering
IIoT and Automation
The Industrial Internet of Things (IIoT) includes sensors and actuators that are enabled with internet connectivity. This means sensor data can be collected and delivered to cloud computing infrastructure over the network in real-time. This powers data collection that captures moment-to-moment operational characteristics of machines.
This brings a massive boost to automation efforts in plants. The real-time data can be used to create automated workflows that make use of the data. Centralized computing power in the form of cloud computers can perform the automated control of plant operations. The changes required according to machine operations can be algorithmically programmed to reflect the machine operations. IIoT plays a key role in improving automation in factories.
IIoT and Predictive Maintenance
The massive data collection enabled by IIoT is advantageous for predictive maintenance just as for automation. The historical and live data from the machines can be used to determine the chances and causes of machine failure. The data on previous machine failures can also be used to corroborate the predictive maintenance algorithms.
Machine learning algorithms are employed for predictive maintenance. We cannot gain insights efficiently from manually going through data. It is impossible due to the amount of data required to predict machine failure. It is also cumbersome to execute complex statistical models required to make predictions. This necessitates the use of modern technological tools like machine learning and deep learning.
The data collected from machines with IIoT sensors are relayed over a network to the cloud data lake. This can be structured as well as unstructured data from different types of sensors. This data is filtered and used to train machine learning models to perform predictions. The sheer scale and performance of predictions made with the help of data fed with IIoT devices are unparalleled. The following brief sections describe some meaningful ways in which IIoT is changing predictive maintenance for good.
Figure 2. Production environments rely on manual and digital control interfaces, as well as remote control and monitoring from cloud networks. Image used courtesy of FANUC
Before the use of IIoT for predictive maintenance, it relied on the experience of maintenance professionals and their gut instinct. The gut instincts of highly experienced professionals are often right. But these instincts can be quantified with the accuracy of the data-driven approach made possible by IIoT sensors. The data from sensors and statistical modeling with machine learning helps to make informed and data-driven decisions.
As with any prediction, the outcome of predictive maintenance is not always perfect. There are some variances in the predictions. But with continuous real-time data fed by IIoT devices, the prediction models can be improved. Machine learning is based on the idea that models can be improved with more and better data, hence the name ‘learning’. It also plays out for predictive maintenance. The data collected with IIoT devices can be used to optimize predictions. This improves the accuracy of predictions over time, in turn increasing the reliability of the predictions by the model.
Return on Investment
Any commercial project must undergo financial scrutiny to prove the viability of the project. Implementation of predictive maintenance also has to deliver a return on invested capital. IIoT and the data collated makes predictive maintenance models accurate and reliable. This means it can effectively reduce unscheduled downtimes and supply chain disruptions. This ensures that machines are productive assets round the clock and working efficiently. Not only is this usually a quick ROI on the IIoT system itself, but also critical data for the justification of future capital investments.
Having a warning system for plant operations is beneficial to operations managers of the plant. The warning always need not be of machine failure or other catastrophic events. It can also be small instances that need attention from the operations team. The continuous sensor data collected by IIoT devices help to create automated alerts. These alerts can be for any scenario chosen by the operations team. The necessary conditions for generating alerts can be algorithmically programmed. Automated alerts are delivered as the conditions are met.
IIoT system implementation does not always mean that the system can change its own process parameters for optimization. Many companies would still choose to require manual adjustments, but automated alerts can provide recommendations on possible optimizations. These can be reviewed and manually implemented, if deemed appropriate.
Figure 3. Monitoring of automation system data can predict costly downtime, leading to a cost-effective work environment. Image used courtesy of TI
It is not just sufficient to predict machine failure, the maintenance team also has to perform preventive action. Performing analysis of machines from scratch is time-consuming. Predictive maintenance with IIoT data not only predicts potential failure, but pinpoints or eliminates reasons for potential failure. This helps to ease the investigation into the potential failure and remediation can be done swiftly. Predictive maintenance also supports maintenance teams in performing root cause analysis when required.
The objective of predictive maintenance is to foresee potential machine failure with the help of machine learning with vast quantities of data. IIoT supports predictive maintenance by delivering continuous real-time data from the machines to the cloud infrastructure over the internet. This helps in delivering more accurate predictions and delivering a high return on investment for the undertaking. Predictive maintenance works best in plants that are tightly integrated with IIoT devices.