# Research on Residual Life Estimation Method for KMN Steel Based on Nonlinear Ultrasonic Testing
## 1 Introduction  
KMN steel is a type of centrifugal compressor rotor material, which has high strength, toughness and good corrosion resistance. With the continuous increase in the speed of large-scale centrifugal compressors, the complex service environment and long service time have increasingly higher requirements for the life of the key component of the centrifugal compressor--the impeller. Therefore, there is an urgent need for effective detection and characterization of the fatigue damage degree of the impeller. Early damage and performance degradation of materials account for 80-90% of the entire fatigue life. Conventional non-destructive testing methods can only detect macroscopic defects (cracks, inclusions, pores, etc.) in materials, and the response to early damage changes in materials and structures is generally relatively small. Moreover, it is impossible to detect small-scale damage such as micro-defects and micro-cracks, especially damage hidden inside materials. Recent studies have shown that the early performance degradation of material damage can cause the generation of nonlinear harmonics in the process of acoustic wave propagation [1; 2; 3; 4]. Therefore, nonlinear ultrasonic testing technology overcomes the shortcomings of traditional non-destructive testing methods and can effectively characterize micro-defect damage and recessive damage [5; 6]. Related scholars have conducted a large number of experimental studies on nonlinear ultrasonic detection technology and characterization methods [7; 8; 9; 10]. In 1997, [PERSON] et al. [11] conducted a non-linear ultrasonic detection study on the early mechanical performance degradation of materials. In 1998, [PERSON] et al. [12] used nonlinear ultrasonic tests to evaluate the performance degradation of materials from cracks to fractures and predict the fatigue life of the materials. In 2010, [PERSON] [13]used a nonlinear scanning system to detect non-metallic inclusions in continuum cast steel. In 2011, [PERSON] [14] found that nonlinear coefficients can be used to characterize the degradation of mechanical properties of materials under tensile loads. In 2012, [PERSON] et al. [15] used nonlinear ultrasonic technology to observe the evolution of ultra-high cycle fatigue damage in aluminum alloy welded joints in situ. In 2015, [PERSON] et al. [16] conducted non-linear, ultrasonic, in situ observations of the ultra-high cycle fatigue characteristics of aluminum alloys and found that the ultrasonic nonlinear coefficients are more sensitive to fatigue damage (such as crack initiation and growth). In addition, they found that there is a good correlation between the ultrasonic nonlinear coefficient and the stiffness and plastic strain of the specimen. In 2016, [PERSON] [17] detected fatigue cracks using sonic infrared imaging. Experimental results show that the increase of the crack length will enhance the crack heating response, and the logarithmic relationship between the response signal and the crack length has a better linearity compared to others. In 2017, [PERSON] [18] adopted the threshold segmentation, morphological processing, feature identifications as well as feature extraction and proposed a detection method with pertinence to the precision casting crack characteristics. In 2018, [PERSON] [19] applied eddy current pulsed thermography (ECPT) for quantitative crack detection based on derivative analysis of temperature variation. The experimental verification showed that the crack sizes could be quantitatively detected with errors of less than 1%. In 2020, [PERSON] [20] studied the influence of crack shape on eddy current detection signal. The general finite element analysis software is used as the simulation calculation tool to study the influence of different crack shape on eddy current detection signal size. In 2020, [PERSON] [21] proposed a crack defect detection method of lead seal of high voltage cable by means of the eddy current detection technology.