A distributed sensor network (DSN) is a grouping of low-power and low-cost sensor nodes (SNs) that are stochastically placed in a large-scale area for monitoring regions and enabling various applications. The quality of service in DSN is impacted by the sporadic appearance of defective sensor nodes, especially over the dense wireless network. Due to that, sensor nodes are affected, which reduces network performance during communication. In recent years, the majority of the fault detection techniques in use rely on the neighbor's sensing data over the dense sensor network to determine the fault state of SNs, and based on these, the self-diagnosis is done by receiving information on statistics, thresholds, majority voting, hypothetical testing, comparison, or machine learning. As a result, the false data positive rate (FDPR), detection data accuracy (DDA), and false data alarm rate (FDAR) of these defect detection algorithms are low. Due to high energy expenditure and long detection delay these approaches are not suitable for large scale. In this paper, an enhanced three-sigma edit test-based distributed self-fault dense diagnosis (DSFDD3SET) algorithm is proposed. The performance of the proposed DSFDD3SET has been evaluated using Python, and MATLAB. The experimental results of the DSFDD3SET have been compared with the existing distributed self-fault diagnosis algorithm. The experimental results efficacy outperforms the existing algorithms.