Everything posted by Filip L
Dear, I have computed a number daily averaged values via the signal from condition tool using the average operator and the daily bounding condition. They are positioned as discrete data at the start of the interval. Some days have missing values for some of the variables due to start-up, sensor faults, ... Others days don't have data at all due to turnarounds, .... (see figure below) Now I want to export all variables together to xls on a common grid with a 1 day interval for a longer period (e.g., 2 years). Using the export button allows to select: 1. Automatic gridding period 2. Custom gridding period (here selected as 1day) 3. Ungridded original time stamps. Option 1 and 2 result in the warning "This signal could not be automatically gridded because it is not interpolated. You can either turn gridding off or set maxInterpolation to a value greater than zero." and empty data columns. Option 3 gives full data columns but for each variable with its own time stamp columns which requires matiching the timestamps afterwards. This is quite a job because of the number of tags and can be error prone. Any advice how to tackle this question for exporting these values on a common custom grid? Many thanks in advance! Filip
Thanks, it is good to know that this can be done by converting it to a continuous signal! Alternatively, I found out that when you have equally spaced (discrete) values (e,g, daily averages coming from using signal from condition using a periodic condition) that you can easily code the finite difference scheme yourself allowing to go broader than only the adject points or to use scheme of higher accuaracy order, e.g., ($signal - $signal.move(+7d))/(7d) This may be a little bit less noisy although differentiation inherently amplifies the noise 🙂. So removing outliers and filtering the original signal is surely advisable.
I am investigating a fouling case study for a batch process. During a batch run the heat exchangers fouls, which can be seen in a decreasing heat transfer coefficient. At the end of the batch the heat exchanger is cleaned without taking it out of service. As result the heat transfer coefficient is again higher. However, with every batch a little bit of extra fouling remains. This can be seen by calculating the average heat transfer coefficient over 1 batch and comparing these for a number of consecutive batches. Finally, the heat exchanger has to be taken out of service and thoroughly cleaned to remove all fouling. What I did so far was using the signal from condition tool to compute the average heat transfer coefficient for one 1 batch and put this value each time as a discrete value in the middle of the batch. Now I want the compare the average value for multiple batches and compute for instance the delta between to adjecent batches, differencing/differentiating the signal to get the slope of the decrease, ... . How can this be done with discrete samples / values? Many thanks for your help.