  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 the normal behavior of conductivity from historical data of a real-world
industrial cooling system and reconstructs the input data, identifying
anomalies that deviate from the expected patterns. The framework also employs
calibration techniques to ensure the reliability of its predictions. Evaluation
results demonstrate that with the attention mechanism in ABCD a 57.4% increase
in performance and a reduction of false alarms by 9.37% is seen compared to
without attention. The approach can effectively detect risks, the risk priority
rank mapped to maintenance, providing valuable insights for cooling system
designers and service personnel. Calibration error of 0.03% indicates that the
model is well-calibrated and enhances model's trustworthiness, enabling
informed decisions about maintenance strategies
