Health Monitoring of Cooling Fans Based on Mahalanobis Distance With mRMR Feature Selection

Cooling fans are widely used for thermal management in electronic products. The failure of cooling fans may cause electronic products to overheat, which can shorten the product's life, cause electronic components to burn, and even result in catastrophic consequences. Thus, there is a growing interest in health monitoring and anomaly detection for cooling fans in electronic products. A novel method for the health monitoring of cooling fans based on Mahalanobis distance with minimum redundancy maximum relevance features is proposed in this paper. A case study of anomaly detection in cooling fans is carried out. The proposed method helps to avoid multicollinearity and tracks the degradation trends of the cooling fans. The results show that the proposed approach is feasible.