# Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals
## 1 Introduction  
Bearings are vital components of heavy rotating machines that reduce friction between a rotating shaft and fixed components such as bearing housings. It is known that 45-55% of failures of rotating machines are caused by bearing faults [1]. Hence, it is important to detect the arising bearing faults at the early stages to prevent the secondary failure of the manufacturing equipment. In the past decades, many bearing fault diagnosis techniques have been developed based on acoustic emission (AE). AE is the process of the generation of transient elastic waves from sudden cyclic fatigue, fraction, impacting, etc. [2; 3; 4; 5]. Regarding bearings, the acoustic waves can be generated when the rolling elements of the bearing hit the cracked surface on the inner race, outer race, and rolling element. The advantage of AE-based analysis is its capability of detecting very low-energy signals caused by bearing failures at an early-stage or during slow-speed operation [2]. However, since the sampling rate used for AE signal collection is usually higher than 1 MHz, it is difficult to analyze the AE signal because of the tremendous amounts of data in the collected time-series (due to high signal sampling rates) and computational time required for analysis. Model-based feature extraction is one of promising approaches to overcome these issues because it converts big raw data instances into small feature vectors. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is introduced to extract informative features in several papers [6; 7; 8]. In these studies, the MOMEDA has been utilized to extract the fault period impulse component as features, which is the demodulated signal. Other papers developed deep neural network (DNN)-based bearing fault diagnosis methods [9; 10; 11; 12; 13; 14]. DNN-based bearing diagnosis methods are powerful tools to extract informative features by learning feature representations from a large amount of raw data. Recently, some papers compared the performanceof convolutional neural network (CNN) and DNN-based approaches. From these papers we could conclude that the CNN-based techniques are much better than DNN-based methods in terms of fault diagnosis performance [3, 12, 15, 16]. Although DNN or CNN-based methods have achieved high classification accuracy, there are still two issues that must be resolved to make these methods highly applicable to real applications. The first issue is that the trained neural network, in general, can be only reliable on the specific machine since the patterns of the raw signals strongly depend on the operating conditions of the machinery such as load, installation, external vibration, etc. The second concern is that the trained feature representation is uninterpretable due to the black box-like operation of the neural networks.  
This paper proposes a new CNN-based rolling element bearing fault diagnosis approach to resolve the aforementioned problems. To address the first issue, the proposed method utilizes the normalized bearing characteristic components (NBCC) as the input data of CNN rather than raw AE signal itself. Since the bearing characteristic frequencies are induced by appearing bearing failures, NBCC is a more effective representation for diagnosing the bearing failure symptoms. To resolve the second issue, this paper applies the gradient-weighted class activation mapping (Grad-CAM) to visualize important regions in NBCC. According to the literature, Grad-CAM is a promising method that provides visual explanations of the classification result of a CNN in object detection and recognition [17].  
The remainder of this paper is organized as follows. Section 2 introduces the proposed methodology for diagnosing rolling element bearing faults using AE signals. In Section 3 the bearing fault simulator used for collecting AE signals is presented. The fault diagnosis results demonstrated and discussed in Section 4. Finally, Section 5 contains the concluding remarks.