We propose an improved fault detection (FD) scheme based on residual signals extracted on-line from system models identified from high-dimensional measurement data recorded in multi-sensor networks.The system models are designed for an all-coverage approach and comprise linear and non-linear approximation functions representing the interrelations and dependencies among the measurement variables.The residuals obtained by comparing observed versus predicted values (i.e., the predictions achieved by the system models) are normalized subject to the uncertainty of the models and are supervised by an incrementally adaptive statistical tolerance band. Upon violation of this tolerance band, a fault alarm is triggered. The improved FD methods comes with two main novelty aspects: (1) the development of an enhanced optimization scheme for fuzzy systems training which builds upon the SparseFIS (Sparse Fuzzy Inference Systems) approach and enhances it by embedding genetic operators for escaping local minima => a hybrid memetic (sparse) fuzzy modeling approach, termed as GenSparseFIS. (2) The design and application of adaptive filters on the residual signals, over time, in a sliding-window based incremental/decremental manner to smoothen the signals and to reduce the false positive rates. This gives us the freedom to tighten the tolerance band and thus to increase fault detection rates by holding the same level of false positives. In the results section, we verify that this increase is statistically significant in the case of adaptive filters when applying the proposed concepts onto four real-world scenarios (three different ones from rolling mills, one from engine test benches). The new hybrid sparse memetic modeling approach achieved fuzzy systems leading to higher fault detection rates for some scenarios.