# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 1 Introduction  
Rotating machinery is the most commonly used type of machine in modern industry, civilian and military applications, such as compressors, steam turbines, automobiles, industrial fans, and aircraft engines [1; 2; 3; 4; 5; 6; 7]. Due to the high service load, harsh operating conditions or inevitable fatigue, faults may develop in rotating machinery [8; 9; 10]. If the fault cannot be diagnosed in a timely way, it may cause a shutdown of the whole system and even catastrophic failure. Therefore, it is significant to detect faults early and assess the fault level as early as possible to avoid catastrophic accidents and ensure the safe operation of the machinery [11; 12; 13; 14; 15; 16]. Many researchers are committed to developing early fault diagnosis (EFD) techniques to monitor the health conditions of rotating machinery [17; 18; 19; 20; 21]. At present, many EFD techniques have been successfully applied in the modern industries, such as the vibration-based EFD method [22], current-based EFD method [23], acoustic emission-based EFD method [24], sound-based EFD method [25], torque-based EFD method [26], and rotating encoder-based EFD method [27], etc. Among these sensor signals, vibration-based diagnostic method is the most commonly researched one because vibration signals can directly represent the dynamic behavior of rotating machinery [28; 29; 30]. However, the early fault signal is often too low due to the fact that small localized damage may generate small periodical impulses. Meanwhile, the measured vibration signals at the early stage are interfered by strong noises [31]. For example, Figure 1 shows a real whole life bearing run-to-failure process. As seen in the figure, there are three stages in the whole life including the normal operation stage (I), early fault stage (II), and failure stage (III). Two vibration signals taken from the severe fault stage (phase III) and early fault stage (phase II) are shown in Figures 2a and 2c, respectively. Meanwhile, their corresponding Hilbert transform (HT) spectra are illustrated in Figures 2b and 2d, respectively. It can be found that the serve fault vibration signal has obvious periodical impulses (as shown in Figure 2a) and the fault frequency and its harmonics can be clearly detected in the frequency domain (as shown in Figure 2b). However, the sign of the periodic impulse is totally marked by the noise in the early fault vibration signal (as shown in Figure 2c). Meanwhile, its corresponding fault frequency, which is totally embedded in the background noises, cannot be detected (as shown in Figure 2d). Therefore, the measured vibration signals of weak fault damage are often interfered by strong background noise and it is difficult to detect the localized damage at the early stage. To diagnose early faults as soon as possible, the feature extraction of vibration signals is very important in real engineering applications. Recently, the advanced signal processing-based weak feature extraction method has been becoming a hot research topic. However, the measured vibration signals of rotating machinery often present nonlinear and non-stationary characteristics. Moreover, the  
Figure 1: Amplitude of the anomaly measure versus the time point for a real bearing of whole life. Figure 2: Two vibration signals under serve and early fault conditions and their corresponding Hilbert transform spectra: (**a**) the waveforms of vibration signal under serve fault condition, (**b**) the envelope spectrum of serve fault signal, (**c**) the waveform of vibration signal under early fault condition, (**d**) the envelope spectrum of early fault signal. vibration signals contain strong background noises during the early fault stage, making it difficult to extract fault information.