Unsupervised Anomaly Detection for Aircraft PRSOV With Random Projection-Based Inner Product Prediction

Pressure-regulated shutoff valves (PRSOVs) are critical components of aircrafts’ environmental control system (ECS) to regulate the carbon pressure, ensuring it within a safe and comfortable range. However, due to the harsh and dynamic operational environment, PRSOVs are susceptible to various anomalies, which may lead to severe flight incidents such as cabin depressurization. To this end, this article proposed an unsupervised anomaly detection approach for PRSOV with random projection-based inner product prediction (RPDP-AD). It leverages distance information in a randomly projected space to construct supervisory signals. This enables our model to encode distribution patterns and data structures of normal samples from unlabeled data. Our model is optimized to minimize the prediction errors in the random projection space. We introduced a distribution difference metric to minimize the discrepancy between learnable mappings and random mappings in low-dimensional space. Anomalous samples will have greater distribution differences, which allow for unsupervised anomaly detection (UAD). This article explored the connection between neural networks and random projection theory for industrial anomaly detection. To further support its effectiveness, we also provide a theoretical analysis of inner product preservation property via Johnson–Lindenstrauss theorem. Experiments were conducted on a PRSOV dataset including seven types of anomalies with different random projection methods. Results demonstrate that our method outperforms other data-driven counterparts.