# Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
## 1 Introduction  
Rolling bearings are one of the common transmission components of rotating mechanical systems. Rolling bearings can operate safely and normally and play an important role in the smooth operation of the entire rotating mechanical system [1; 2; 3]. However, due to the harsh working environment and the influence of high temperature and high pressure during operation, rolling bearings are also the most easily damaged transmission components in rotating machinery [4; 5]. Condition monitoring of rolling bearings is of great significance in reducing economic losses and preventing accidental accidents [6; 7]. The failure of rolling bearing does not occur instantaneously but gradually develops into the stage of frequent failure after the early stage of failure and finally enters the stage of serious failure [8; 9; 10]. In the early failure stage of rolling bearings, the failure is relatively small and has little impact on the overall operation of the mechanical system [11; 12]. If the fault can be found and repaired in time at this stage, it is very important to improve the operation of the entire mechanical system, improve the production efficiency, and ensure the safety of the operator [13; 14]. Therefore, it has very significant research significance for the early fault detection of rolling bearings. The method based on signal processing is a common detection method for early faults of rolling bearings [15; 16; 17]. [PERSON] [18] improved the variable mode decomposition and proposed an improved early fault detection method of rolling bearings based on the improved variable mode decomposition Teager energy operator. [PERSON] [19] proposed a deep learning early fault detection algorithm for rolling bearings, which first constructed a three-channeldataset by combining time/frequency/time-frequency domain information. However, since some redundant information is included in the feature extraction process, the data dimension is high, and the computational efficiency is low. Early fault detection methods based on deep learning can compress the signal and reduce the data dimension [20,21]. However, its model becomes more computationally intensive, and the training speed of the system decreases in large network structures. The principal component analysis method is a common fault detection method in the multivariate statistical method, which can effectively remove the noise and redundant information in the signal. The model building process is simple, and the calculation efficiency is high [22, 23, 24]. Many methods are now extended by principal component analysis methods. In the early fault stage of rolling bearings, the fault characteristics are relatively weak, which leads to an increase in the difficulty of early fault detection of rolling bearings. [PERSON] [25] proposed an early fault detection method based on Deep PCA (Deep Principal Component Analysis, Deep PCA). This method effectively detects early failures. However, the Deep PCA method proposed by [PERSON] extracts the fault features through signals, such as temperature, voltage, and current, does not analyze the vibration signal, and cannot accurately and timely detect the early faults of rolling bearings. [PERSON] [26] et al. proposed an early fault detection method for rolling bearings based on Deep Empirical Mode Decomposition PCA (DE-PCA). This method decomposes the vibration signal through the empirical mode decomposition method and then decomposes the data deeply to extract the early fault characteristics of the rolling bearing. However, when the vibration signal data of the rolling bearing is relatively large, the number of calculations increases in the process of empirical mode decomposition, which affects the early fault detection efficiency of the rolling bearing. At the same time, the empirical mode decomposition method may have the phenomenon of modal aliasing. Mode aliasing is caused by the signal interruption.