Industry 4.0 can use the insights gained from analyzing big data to improve operational efficiency through predictive maintenance and asset optimization. But the key is to feed the correct data into the appropriate model and prediction algorithm at the correct time. However, data analysts often find that the data used is incomplete or unreliable, and must try to fill in the missing data. To improve data quality management, companies should start by collecting data in a trusted environment.
According to reports, although technologies such as machine learning or data simulation can improve bad data, they cannot solve the original problem of bad data sources. In addition, these solutions are often too complex to apply to certain use cases. And any data quality management work should start with collecting data in a trusted environment.
New devices will be authenticated when they are added to the networked factory system. Then, they are authorized to transmit data over the network and use security protocols such as TLS or SSL to protect the data flow on the network. But over time, the dynamic nature of the industrial environment means that the state of the equipment must be continuously updated.
For example, unless active checks to verify the continued authenticity of the device or regular password updates, the device may have been compromised. The original authentication of the trustworthiness of the device as a data source is no longer valid, and a method for continuously updating the authentication is required.
Data generation and communication from these devices are also critical. Many sensors only communicate events or change specifications. Take the temperature sensor as an example. If the temperature in the production environment is kept within a tight and effective range, the sensor may not transmit data for a long period of time, and it is impossible to confirm whether the sensor connection is lost or malfunctioning, so a method is needed To verify the correct operation of the sensor itself.
Finally, a trusted device is a device that operates in its correct operating state. This means that the device is running and has been properly authenticated. For more complex equipment, such as lenses, there must be a complete set of measurement methods.
For example, if the lens is collecting visual data streams in a specific process or area for quality measurement, the trust metric will show that the lens is delivering a message to be tracked. If the lens is damaged, the company will know that the collected data is invalid and should not be included in the specific analysis, and must take steps again to restore the device to its trusted state.
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