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Five data acquisition strategies

Did you just see something extraordinary in the recorded data or was
it a data anomaly in measurement, communications, or data aggregation?

Did thing It that This might I just makes isn’t extraordinary see be the that? us aquestion video say, Everyone “Wait…. or or toaask unbelievable. sports seen about What?!” replay some- data acquisition (DAQ) systems or from data agorithms. Data collection should teach, not amaze and confuse. Causation cannot be derived just because a data set correlates to another. Actionable data is the goal. Data needs to help the return on investment

  1. Frequency of data

Data collection rates correlate to dollars; there isn’t much debate there. Sending data to the cloud is easy to understand. Mbps, data message counts, and terabytes cost dollars. Even with an on-premise solution, a network to support data collection and storage for data processing is needed. Both translate to cost.

It makes sense to ask “How often do I need an update?” Recording ambient environmental conditions on a millisecond level is probably overkill, but collecting vibration data once a second won’t give useable data either. Data must be collected at a rate that translates to representative data sets. Application requirements will point to the correct sampling rate.

  1. Accuracy of data

Sensor accuracy is important to collecting data that represents the real-world conditions. Is it necessary to know the ambient humidity to a 1/100th of a percent or the actuator position to 1 micron? If a temperature sensor has an accuracy of +/- 5° C, is that good enough? The data collected needs to represent the application correctly to draw accurate conclusions.

When measuring part dimensions to check quality, the sensor needs to exceed the tolerance accuracy of the part being measured. If ambient temperature may affect the process, an accuracy proportional to the expected temperature fluctuation may be needed to know if it has an influence or not. Some application knowledge will help in selecting sensors that are accurate and economical for the data collection system.

  1. Resolution

Data resolution is related to how well a recording device can read the sensor data. Data point size can make a difference between usable data and junk. The sensor might accurately detect data, but if the controller can’t read the data at that accuracy over the fluctation range, it doesn’t matter.

  1. Synchronized data

Some data points collected might need to be tightly synchronized to other data points. This can be important in high-speed data collection. Consider a monitored vibration value that corresponds to the position of an actuator. It might be good to know actuator position that corresponds to a specific out-of-tolerance vibration data point in a machine cycle. For measurement accuracy, the position and vibration data must be collected so they correspond to each other in a way that reflects the actual behavior in time. This could be accomplished by having one controller read both values at the same time, or by time-syncing two controllers so the time-stamps of both sets of data are synchronized. If data sets cannot accurately be correlated repeatably, an accurate measurement isn’t possible.

  1. Application knowledge

Application knowledge is the key to collecting the right data and turning it into actionable data. From the examples above, it’s easy to imagine different ways to collect data that can lead to misunderstandings. But even if data is accurate and represents the real-world conditions, is it relevant?

Allen Tubbs is product manager, and Benjamin Menz is a data scientist, with Bosch Rexroth Corp., a CSIA member, a CFE Media content partner. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media,

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