In the following, I will use technical terms without explanation as it gives the freedom to describe the project in a shorter form for those who already know. The thesis is about condition monitoring of large diesel engines from acoustic emission signals. The experiments have been focused on a specific and severe fault called scuffing. The fault is generally assumed to arise from increased interaction between the piston and liner. For generating experimental data destructive tests with no lubrication, oil has been carried out. Focus has been on modeling the normal condition and detecting the increased interaction due to the lack of lubrication as a deviation from the normal. Linear instantaneous blind source separation is capable of picking out the rel-evant hidden signals. Those hidden signals and the estimated noise level can be used to model the normal-condition, and faults can be detected as outliers in that model. Among the investigated methods the Mean field independent component analysis with diagonal noise covariance matrix models is best at modeling the observed signals. Nevertheless, this does not imply that this is the best model to detect the outliers. Another contribution of this work is the analysis of the angular position changes of the engine related events such as fuel injection and valve openings, caused by operational load changes. With inspiration from speech recognition and voice effects the angular timing changes have been inverted with the event alignment framework. With the event alignment framework it is shown that non-stationary condition monitoring can be achieved.