An ensemble of statistical techniques and artificial intelligence methods is proposed for the detection of abnormal conditions (anomalies) in safety-critical applications. Stacked Autoencoders (SAEs), the Local Outlier Factor (LOF) method and Gaussian Mixture Models (GMMs) are considered in the ensemble. The outputs of these algorithms (i.e., properly defined anomaly scores) are normalized within an original probabilistic framework and combined for the robust and conservative identification of anomalies in datasets: different combination functions are employed, including "averaging", "maximization" and "AKPV", which averages the scores of the top three anomaly detectors. SAEs are also used as efficient tools for dimensionality reduction in the overall framework.