The concept of the Internet of things has become extremely popular in the last few years. This term “IoT” covers a large terrain, it includes intelligent computers, devices, and objects that collect and share enormous amounts of data. The IoT has helped in increasing automation in schools, homes, and in many industries. The application of IoT in industries is known as Industrial Internet of Things (IIoT). It helps in bringing large volumes of fast-moving data, at greater speeds, and with more efficiency than ever before. That leads to both challenges and opportunities in the manufacturing industry. Industrial IoT has multiple benefits such as:
- improved connectivity
- efficiency and extensibility
- Time and cost saving due to predictive maintenance
- Improved safety
- Operational efficiencies
- Connectivity between people, data and various factory processes
The hype surrounding the IIoT has led the way to boost in popularity of machine learning. Machine learning is used for generating useful insights using a large amount of collected data. Machine learning is valuable when you do not know the important input variables to make the right decision. And need an overall look at the entire data to generate the results using the essential factors to achieve your ultimate goal. Machine learning has various applications in IIoT. These two are being used simultaneously to generate useful results.
Cost reduction by prediction of data trends in industrial applications
The use of ML and IIoT together help in collecting data from multiple sensors in or on machines, then this data is compared so that the slightest changes can be detected. These data trends help in making a prediction and hence formulation of a model can be done using them. This analysis is done in within seconds and the results are displayed on the technician’s smartphone immediately.
Clustering of data
Another benefit of using machine learning on the IIoT generated data is a classification of such data. Such as:
- Random Forest classification
- Support Vector Machines
- k-Means clustering or k-nearest neighbors
- Binary classification
- Logistic regression
These classifications can easily help us in saving industrial time.
Detection of problems in your equipment can help you in repairing them on time. Using ML we can detect the outliners in a data set and identify the anomaly. Statistical approaches to anomaly detection involve Gaussian distributions or Z-score metric for parametric distribution.
TensorFlow, Keras, Apache Spark and several other machine learning libraries are available for developing IoT solutions. Using these you need not implement all the algorithms from scratch.
Few problems are also linked with the use of machine learning and the industrial internet of things. Such as it is not easy for physical models to learn from new data, and not all physical laws have been built into large complex systems. But the world with industrial machine learning is better than without it.
For unprecedented access to your industrial working industrial IoT platform is the key solution. They help to enterprise data for monitoring, connectivity, analytics, and delivery direct connection to IoT platforms helps in creating world-class industrial automation solutions. Reliable companies must be hired for providing these services in order to ensure safety and security of your data.