Interpretable anomaly detection using extended isolation forest with adaptive thresholds

To address the common issues of insufficient interpretability in intelligent anomaly detection for rotating machinery, as well as the inefficiency and practical limitations caused by the reliance on a large number of labeled samples for model pretraining, this paper presents the multi-granularity scanning Extended isolation Forest with adaptive threshold strategy. A multi-granularity scanning based on interquartile range updating is employed to screen representative local features of the input samples, constituting a multi-granularity feature matrix for anomaly detection and improving feature processing efficiency. Additionally, a randomized 2-D anomaly assessment Extended isolation Forest model is constructed, which enhances model interpretability, optimizes data segmentation paths, avoids artifacts in the Gaussian distribution of anomaly scores, and improves detection accuracy and reliability. A clustering-based adaptive threshold strategy is designed to eliminate dependency on training samples and improve the flexibility and adaptability of the model. Experimental results on bearing and gearbox datasets achieve an accuracy of more than 99.22% and an The area under the ROC curve (ROC-AUC) value approximating 1, demonstrating that the proposed model responds quickly and accurately detects anomalous samples across various operating conditions.