# Multi-Scale Convolutional Recurrent Neural Network for
## 1 Introduction  
Manufacturing competitiveness has been an important factor in the era of global competition and in the period of the fourth industrial revolution [1, 2]. Product quality is important to secure manufacturing competitiveness. If the equipment that generates a product breaks down, it can have substantial impacts on the quality of the product and result in a significant loss for the company. For this reason, the predictive maintenance of facilities is indispensable [3, 4, 5]. Most equipment failures occur in rotating equipment, and bearing damage is the biggest cause of rotating equipment failure [6, 7, 8]. Bearings are largely divided into two types: One is a rolling bearing that converts a sliding contact into a rolling contact by inserting a ball, roller, or needle roller between the shaft and the bearing to reduce friction. The other is a sliding bearing that performs sliding friction with the shaft and bearing surface contact. As the most frequently used rolling bearings have rolling contact, they have less friction and less friction loss than sliding bearings do, and their starting resistance and heat generation are also small, allowing high-speed rotation. However, as the rolling element and the raceway are in point or line contact, they are weak against impacts and have the drawback of easy noise generation [9, 10, 11, 12]. Rolling bearings can fail at any time due to fatigue, even if they are operated under ideal design conditions. If the damage to rolling bearings can be predicted in advance, it is possible to prevent failure and stop the rotating machine, thereby securing the operation reliability of the machine, and this can have a positive effect on the quality of the product [13, 14]. It is beneficial to find the cause of bearing damage early, but it is difficult to ascertain the true cause of bearing damage. However, it is possible to understand the accident situation of the bearing from the signal from the vibration of the bearing and to detect the initial defect of the bearing [15]. Recently, many studies have been conducted to monitor and diagnose defects based on signals from bearings [16, 17, 18, 19, 20]. [PERSON] et al. [21] transformed the vibration signal into an FFT and identified it using a 3D plot so that the trend of bearing fault conditionscould be monitored. [PERSON] et al. (2021) proposed a 1D CNN algorithm that detects bearing defects better with time scale data than with time domain data. [PERSON] et al. (2020) proposed a Duffing system to solve the strong noise issue of bearings and to identify weak fault signals in the complexity and diversity of a working environment. [PERSON] et al. (2021) proposed a new early fault diagnosis framework for diagnosing faults in noisy situations based on the sliding window stack noise reduction automatic encoder (SDAE) and long and short-term memory (LSTM) for early fault detection in rolling bearings. In addition, to detect defects in a noisy environment, a method of detecting defects by adding Gaussian noise to the bearing data set has been studied ([PERSON] et al., 2020; [PERSON] et al., 2020). It is important to receive accurate vibration signals to learn these vibration signal-based data. Most studies detect defects based on bearing data sets extracted in a neat environment. These studies lack the ability to extract differential features from complex structures. The recognition accuracy is low in strong noise situations mainly for the following reasons: (1) When there is noise, the vibration signal measured by the bearing is nonlinear and contains a large amount of noise through strong defects. If there is a large amount of noise, several problems occur while running the algorithm, and the accuracy of defect detection may be low. (2) The vibration signal of the bearing has temporal characteristics. In the case of a CNN-based model, spatial features of data can be extracted, but temporal features cannot be extracted. In addition, CNN feature extraction is also limited in strong noise situations.