This report presents a hybrid model- and data-based method for detecting and isolating faults in the ink channel of a printer using the self-sensing capability of piezo actuators. Grey-box system identification is used to identify the parameters of the model of the ink channel. The model is used to construct the fault detection (FD) filter. The FD filter uses the piezo self-sensing signal of the printer as an input and puts out a residual signal, which is approximately zero when the system is healthy. Several methods to design the FD filter denominator are proposed. If the energy of the residual exceeds a threshold, the system is detected as faulty. The fault isolation (FI) filter uses linear regression, utilizing the residuals of the FD filter, to generate a probability vector. The entries of this vector correspond to the possible faults, and the highest entry is used to isolate the fault. Both filters are tested for a simulated and experimental dataset. For both datasets, the FD filter is shown to perform appropriately, as does the FI filter for experimental data. For simulated data, the FI filter is compared to other methods. The FI filter performs best when only regarding isolations with a high certainty.