What is the main characteristic of the 'Delta with value deadband' method?

The 'Delta with value deadband' method primarily focuses on recording changes that exceed a specified threshold or deadband. This means that only significant changes in the data are captured, which helps reduce the amount of logged data by filtering out minor fluctuations that may not be relevant for analysis.

This approach is particularly useful in environments where data can be noisy or when the analysis involves trends over time. By establishing a deadband, the method ensures that only changes that are meaningful—those that represent a real shift in the data values—are recorded. This can be beneficial for optimizing storage space and improving the efficiency of data processing since it eliminates unnecessary data points that would otherwise clutter the dataset and may not provide additional insights.

In contrast, methods that record every change would capture all variations, regardless of their significance, leading to larger volumes of data. Recording changes within a defined range would also not align with the primary characteristic of the deadband method, which is focused on the notion of exceeding a threshold. Continuous recording, while it allows for capturing all data points over time, does not incorporate the deadband concept that filters for relevance.