Towards fault detection of the operation of dairy processing industry tanks using Electrical Resistance Tomography

In this study, Principal Component Analysis (PCA) has been applied for the purpose of reducing the high-dimensionality of the ERT-produced data from such situations to lower dimensions holding most of the information. The reduced set of information is then used for the detection of whole and skim milk inhomogeneity, aeration and external object detection. A Matlab program has been developed which would use the above mentioned tools (ERT and PCA) to detect each of the mentioned faults in an opaque vessel which could be otherwise difficult to detect.