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Case Study 10: Data Reconciliation

Excerpt

Values of data from online sensors are corrupted by sensor drift, which could be due to ambient conditions (temperature, humidity, etc.), dust accumulation, element wear, corrosion, cumulative vibration damage, etc. It could also be that the sensor was not perfectly calibrated, which leads to incorrect values. These aspects mean that the value is biased, is in error, and has a systematic error.

Values of data from online sensors are corrupted by sensor drift, which could be due to ambient conditions (temperature, humidity, etc.), dust accumulation, element wear, corrosion, cumulative vibration damage, etc. It could also be that the sensor was not perfectly calibrated, which leads to incorrect values. These aspects mean that the value is biased, is in error, and has a systematic error.

45.1Description and Analysis
45.2Exercises
45.1Description and Analysis
45.2Exercises

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