Cavitation is harmful to water turbines and may cause operation delays of several weeks. The real-time detection of cavitation risk is increasingly important, and even narrow cavitation-free power ranges can be utilised in load optimisation. Higher derivative signals x (3) and x (4) calculated from acceleration signals are very suitable for detecting impacts. This paper introduces a generalised moment τ σ M p α which is defined by three parameters: the sensitivity of the moment improves when the order p of the moment increases, especially when short sample time τ is used. In this study, sufficently good results were obtained with moments where the order of derivation α =4 ,p ≈ 4, and τ =3s. These moments detect the normal operating conditions, which are free of cavitation, and also provide a clear indication for cavitation risk at an early stage. Sufficiently long signals are required for producing reliable maximum moments and data for analysing short-term cavitation. On-line cavitation monitoring is feasible with this approach since the analysis does not need high frequency ranges and the sample times are very short. The moment can be analysed first, and it is then possible to obtain the cavitation index if the moment value exceeds the threshold. Data compression is very efficient as the detailed analysis only requires the feature values of the appropriate samples.