In the Industry, collection of IoT data, specifically that from manufacturing processes is very important. Apart from the quantity of the data collected, the quality of information from various machines is also equally vital for analysis, and to make decisions.
IIoT puts a lot of stress on the usefulness of predictive analytics based on big data. According to the Forbes magazine, big data offers the volume, speed, and variety of information about important effects that traditional methods of empirical research and the human eye is unable to capture. Therefore, big data becomes the primary step towards generating valuable insights from evidence-based interventions. From a theoretical and practical perspective, big data not only helps to predict outcomes, but it also helps in explaining them, especially in understanding the underlying causes.
Companies usually build plug-n-play adapters for controls, thereby enabling them to capture hundreds of data points directly from PLCs. Although this generates vast quantities of data for analysis, and a large part of it will be helpful as deep data, there will always be some part of the data that will remain useless, as will some results.
By taking the analysis down to a more granular level, deep data can eliminate irrelevant information and focus on the streams for a certain course of investigation. Analyzing deep data offers more accurate overall predictive trends.
Data from a specific sensor on a machine offers a snapshot within a designated timeframe. Sensor data monitors specific situations, such as vibrations that signify to an operator the state of operation of the machine—on versus off. However, all sensor data may or may not be useful during a review or analysis.
On the other hand, PLCs can collect large amounts of data, and when combined with sensor data, allows the operator to gather a full picture of the machine status at any time. This data can help to monitor inputs to and outputs from a machine, and based on programming, can make logical decisions when necessary.
Older machines with legacy controls and those with no controls need additional integration/hardware support for capturing data. While auxiliary hardware can capture digital and analog IO, adding sensors can generate additional data points.
The ability to capture deep PLC data and data from sensors that monitor specific items that the PLC cannot reach forms the basis of high-quality analytics and results—all the more reasons for the necessity of sensor as well as PLC data.
For instance, while a sensor may provide information on the vibration limits of a certain machine or parts thereof, the PLC data from the machine may include parameters signaling an impending fault. Therefore, the PLC data offers the ability to control the operation of or sequence of activity of a nearby a machine. When the sensor data signals one or more parameters are beyond the programmed limits, the operator can respond quickly, and need not wait for analysis.
Therefore, using both sets of data from sensors as well as from PLCs offers more information to the user than either on their own do. This allows the operator greater flexibility for avoiding expensive downtimes and maintenance issues.
