With the emergence of WBANs to aid healthcare solutions, the dependence on sensors for decision-making and continuous monitoring applications increased. Dependence on sensors makes fault diagnosis critically important due to the real-time constraints of the applications and the degree of accuracy required. This requirement for accuracy in WBANs, in turn, prioritizes the need to analyze data discrepancies observed at the server end of the applications. The use of ML algorithms to classify faults to a particular class offers little help when maintenance activities are executed or scheduled. Thus, determining the root cause of the failures and the affected faulty components of the application becomes difficult. The automated healthcare solution based on knowledge graphs provides an extensive outlay for detecting and isolating faults, bringing ease in the maintenance and rectification of failures. We present a knowledge graph approach to model, understand, and diagnose faults by providing a structured representation of the network’s entities, relationships, and data flows. With the adoption of our mechanism, reliability engineers and software quality managers can gain insight into the error factors, components affected, and related discrepancies in WBANs.