ABCD: Trust enhanced Attention based Convolutional Autoencoder for Risk Assessment

arXiv:2404.16183v1 Announce Type: new
Abstract: Anomaly detection in industrial systems is crucial for preventing equipment failures, ensuring risk identification, and maintaining overall system efficiency. Traditional monitoring methods often rely on fixed thresholds and empirical rules, which may not be sensitive enough to detect subtle changes in system health and predict impending failures. To address this limitation, this paper proposes, a novel Attention-based convolutional autoencoder (ABCD) for risk detection and map the risk value derive to the maintenance planning. ABCD learns …

abstract anomaly anomaly detection arxiv assessment attention autoencoder cs.ai cs.lg detection efficiency equipment health identification industrial monitoring risk risk assessment rules systems trust type