A method of training a neural network processor for providing diagnostic information of a controlled liquid chromatography pump unit comprising an execution of the following sequence of steps: a) simulating (S1) a pump controller generating one or more pump control signals (PC) in accordance with a pump controller state; b) simulating (S2) a pump fault simulation signal (PFC) representative for the presence or absence of a pump fault; c) executing (S3) a simulation model of a pump unit, the simulation model providing one or more simulated sensor signals (PS) representing operational parameters of the simulated pump unit in response to the one or more simulated pump control signals (PC), the simulation model selectively simulating a pump fault in response to the pump fault simulation signal (PFC); d) supplying (S4) the neural network processor with a combination of input signals comprising the one or more simulated sensor signals (PS) and one or more signals (OS) indicative for said pump controller state and/or one or more signals indicative for the supplied one or more simulated pump control signals (PC); e) the neural network processor computing (S5) one or more output signals (NO) in response to the supplied combination of input signals (PS, OS; PC, OS, PS; PC, PS); f) computing (S6) a loss function by comparison of a diagnostic state as indicated by the one or more output signals (NO) and a diagnostic state as indicated by the pump fault simulation signal (PFC); g) training the neural network processor by feeding back (S7) a loss (LS) computed with the loss function, wherein during one or more executed sequences the pump fault simulation signal (PFC) indicates that a predetermined pump fault is to be simulated and during one or more executed sequences the pump fault simulation signal (PFC) indicates that the predetermined pump fault is to be absent in the simulation. a) simulating (S1) a pump controller generating one or more pump control signals (PC) in accordance with a pump controller state; b) simulating (S2) a pump fault simulation signal (PFC) representative for the presence or absence of a pump fault; c) executing (S3) a simulation model of a pump unit, the simulation model providing one or more simulated sensor signals (PS) representing operational parameters of the simulated pump unit in response to the one or more simulated pump control signals (PC), the simulation model selectively simulating a pump fault in response to the pump fault simulation signal (PFC); d) supplying (S4) the neural network processor with a combination of input signals comprising the one or more simulated sensor signals (PS) and one or more signals (OS) indicative for said pump controller state and/or one or more signals indicative for the supplied one or more simulated pump control signals (PC); e) the neural network processor computing (S5) one or more output signals (NO) in response to the supplied combination of input signals (PS, OS; PC, OS, PS; PC, PS); f) computing (S6) a loss function by comparison of a diagnostic state as indicated by the one or more output signals (NO) and a diagnostic state as indicated by the pump fault simulation signal (PFC); g) training the neural network processor by feeding back (S7) a loss (LS) computed with the loss function, wherein during one or more executed sequences the pump fault simulation signal (PFC) indicates that a predetermined pump fault is to be simulated and during one or more executed sequences the pump fault simulation signal (PFC) indicates that the predetermined pump fault is to be absent in the simulation. <EOS>