This paper investigates the application of fault diagnosis (FD) approach for improving performance of compressors within exact operating point determination. Detecting of sensor fault or failure status is more important in the compressor for safety-critical application. No work has previously been reported on the use of the FD system within a compressor surge-suppressing system. Therefore, the main contribution of this paper is presenting different and complementary techniques for surge-suppressing studies via sensor FD. By data acquisition from a nonlinear Moore–Greitzer model, a neural network (NN) and innovation complex decision logic provide residual generation and evaluation blocks in an analytical redundancy FD system, respectively. The proposed FD deals with the most-common sensor faults and failures in seven different scenarios according to their nature, such as bias, cutoff, loss of efficiency, and freeze.