Bearing failure generates impulses when the rolling elements pass the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect bearing failures operated in low-rotating speeds. However, since the high sampling rates of the AE signals make it difficult to design and extract discriminative fault features, deep neural network-based approaches have been proposed in several recent studies. This paper proposes a convolutional neural network (CNN)-based bearing fault diagnosis technique. In this work, the normalized bearing characteristic component (NBCC) is used as the input of CNN, which is an effective form of representing bearing failure symptoms. In addition, importance-weight is extracted using gradient-weighted class activation mapping (Grad-CAM) for visual explanation of CNN. In the experiment result, the proposed approach achieves high classification accuracy with reasonable visualization, which shows that CNN successfully learned the components of bearing characteristic frequency for each type of bearing failure. of 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. Proposed...

Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%.