Abstract: Systems and methods are provided for self-calibrating a health state of a hardware resource using a Siamese network based on a plurality of feature variables. The feature variables may include hardware failure data, performance degradation data, and power consumption data. The hardware failure data is based on machine operation records and warranty logs. The performance degradation data is based on hourly performance data and a number of client requests for performing functions. The power consumption data uses power telemetry and a processor (e.g., CPU) usage. The present disclosure uses a Siamese network with a plurality of trained neural networks in parallel to determine a correlation between incident data and reference data (e.g., representing a hardware resource in a healthy state). Use of the Siamese network enables self-calibrating a health status of servers in a cloud system without imposing stress tests or complex computations to classify the respective servers.