First, the concept of complex network synchronous state was introduced in this paper. Then a sensor network varying with time was defined by taking the data measured by sensors as nodes, and the dynamic mechanics of the sensor network was quantitatively described with mathematical analysis method. Finally, the mathematical definition, the calculation method and the physical meaning of the sensor network synchronous state were given. The above theoretical derivation show synchronous state can globally assess the health of the sensor network. The couple matrix A=(a_ij)_N×N of complex network was defined by the distance relevance of the measured data. And the left eigenvector (ξ₁,ξ₂,...,ξ_N)corresponding to the zero feature of the matrix was used to character the local details of sensor network nodes. Then, a node fault diagnosis algorithm was derived based on sensor network synchronous state. A complex network which consists of 100 sensors was experimentally simulated. We collected the measured data in 60 s during the stable operation with each length of 5000, and there were 3 sensors in intermittent gain fault state to verify the effectiveness of the proposed method. The simulation results show that the proposed method can not only track the work state of the whole sensor work and monitor the faults of each node in real-time, but also distinguish the abnormal data caused by the change of external objects or by sensor faults through combining the relevance between the node faults. The proposed algorithm can provide a feasible research idea of assessing the global state of the sensor network and monitoring the partial fault of network nodes, and it's hoped the algorithm can provide references to researchers in related fields.