Data dimension reduction (DDR) is all about mapping data from high dimensions to low dimensions, various techniques of DDR are being used for image dimension reduction like Random Projections, Principal Component Analysis (PCA), the Variance approach, LSA-Transform, the Combined and Direct approaches, and the New Random Approach. Auto-encoders (AE) are used to learn end-to-end mapping. In this paper, we demonstrate that pre-processing not only speeds up the algorithms but also improves accuracy in both supervised and unsupervised learning. In pre-processing of DDR, first PCA based DDR is used for supervised learning, then we explore AE based DDR for unsupervised learning. In PCA based DDR, we first compare supervised learning algorithms accuracy and time before and after applying PCA. Similarly, in AE based DDR, we compare unsupervised learning algorithm accuracy and time before and after AE representation learning. Supervised learning algorithms including support-vector machines (SVM), Decision Tree with GINI index, Decision Tree with entropy and Stochastic Gradient Descent classifier (SGDC) and unsupervised learning algorithm including K-means clustering, are used for classification purpose. We used two datasets MNIST and FashionMNIST Our experiment shows that there is massive improvement in accuracy and time reduction after pre-processing in both supervised and unsupervised learning.

Cross-domain aero-engine fault detection is challenging because vibration signals collected under different operating conditions exhibit substantial distribution shifts. Existing diagnostic models mainly focus on feature extraction or global domain alignment, often overlooking fault class separability near decision boundaries. This study proposes a perturbation-enhanced deep contrastive learning bidirectional gated recurrent unit framework (DCL-BiGRU) for unsupervised cross-domain fault detection. A shared BiGRU encoder captures bidirectional temporal dependencies, while deep contrastive learning improves intra-class compactness and inter-class separation. A controlled perturbation strategy strengthens robustness to noise and feature variation, and maximum classifier discrepancy guides uncertain target samples toward reliable source-supported class regions. Experiments on two laboratory datasets and one practical aero-engine dataset show accuracies of 95.62% and 92.48% for two transfer tasks, with a mean accuracy of 94.05%. The method outperforms DCL, BiGRU, TCA, JDA, MNN, and DANN approaches, confirming improved domain alignment, class discrimination, and diagnostic robustness under practical conditions.