Rolling Element Bearing Fault Detection and Diagnosis with One-Dimensional Convolutional Neural Network
Faculty of Mechanical Engineering, University Teknologi Malaysia（马来西亚理工大学机械工程学院）；Institute of Noise and Vibration, University Teknologi Malaysia（马来西亚理工大学噪声与振动研究所）
Barathan Pubalan, Muhammad Arif Aiman Jidin, Mohd Syahril Ramadhan Mohd Saufi, Mohd Salman Leong, Muhammad Danial bin Abu Hasan
备注：
Published in International Journal of Business and Technology Management, Vol. 7, No. 9, pp. 485-498, 2025. Special issue: 13th International Conference on Engineering Business Management 2025. Author version
发表信息：
International Journal of Business and Technology Management, Vol. 7, No. 9, pp. 485-498, 2025
Rolling element bearings are critical components in rotating machinery, and their condition significantly influences system performance, reliability, and operational lifespan. Timely and accurate fault detection is essential to prevent unexpected failures and reduce maintenance costs. Traditional diagnostic methods often rely on manual feature extraction and shallow classifiers, which may be inadequate for capturing the complex patterns embedded in raw vibration signals. In this study, a compact one-dimensional convolutional neural network (1D CNN) is developed for automated bearing fault diagnosis using raw time-domain vibration data, eliminating the need for manual feature engineering. The model is trained and evaluated on two established benchmark datasets: the Case Western Reserve University (CWRU) dataset and the Paderborn University (PU) dataset. The CWRU data were segmented based on four distinct motor load conditions (0 HP to 3 HP), with each load scenario trained and tested independently to ensure strict separation and prevent data leakage. The CNN achieved high average test accuracies of 99.14%, 98.85%, 97.42%, and 95.14% for 0 HP, 1 HP, 2 HP, and 3 HP, respectively. On the PU dataset, known for its naturally induced faults and greater operational variability the model achieved a robust average testing accuracy of 95.63%. These results affirm the model ability to generalize across datasets and varying operating conditions. Further improvements were observed through hyperparameter tuning, particularly window length and training epochs, underscoring the importance of tailored configurations for specific datasets and load conditions. Overall, the proposed method demonstrates the effectiveness and scalability of 1D CNNs for real-time, data-driven bearing fault diagnosis, offering a reliable foundation for condition monitoring in industrial applications.
Vibration-based condition monitoring techniques are commonly used to detect and diagnose failures of rolling bearings. Accuracy and delay in detecting and diagnosing different types of failures are the main performance measures in condition monitoring. Achieving high accuracy with low delay improves system reliability and prevents catastrophic equipment failure. Further, delay is crucial to remote condition monitoring and time-sensitive industrial applications. While most of the proposed methods focus on accuracy, slight attention has been paid to addressing the delay introduced in the conditi…
Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions…
Fault diagnostics and prognostics are important topics both in practice and research. There is an intense pressure on industrial plants to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering potential faults in its early stages. Intelligent fault diagnosis is a promising tool due to its ability to rapidly and efficiently processing collected signals and providing accurate diagnosis results. Although many studies have developed machine leaning (M.L) and deep learning (D.L) algorithms for detecting the bearing fault, the re…
The Intelligent Fault Diagnosis of rotating machinery currently proposes some captivating challenges. Although results achieved by artificial intelligence and deep learning constantly improve, this field is characterized by several open issues. Models' interpretation is still buried under the foundations of data driven science, thus requiring attention to the development of new opportunities also for machine learning theories. This study proposes a machine learning diagnosis model, based on intelligent spectrogram recognition, via image processing. The approach is characterized by the employme…
The scope of data-driven fault diagnosis models is greatly extended through deep learning (DL). However, the classical convolution and recurrent structure have their defects in computational efficiency and feature representation, while the latest Transformer architecture based on attention mechanism has not yet been applied in this field. To solve these problems, we propose a novel time-frequency Transformer (TFT) model inspired by the massive success of vanilla Transformer in sequence processing. Specially, we design a fresh tokenizer and encoder module to extract effective abstractions from …
This paper presents a non-contact fault diagnostic method for ball bearing using adaptive wavelet denoising, statistical-spectral acoustic features, and one-dimensional (1D) convolutional neural networks (CNN). The health conditions of the ball bearing are monitored by microphone under noisy conditions. To eliminate noise, adaptive wavelet denoising method based on kurtosis-entropy (KE) index is proposed. Multiple acoustic features are extracted base on expert knowledge. The 1D ResNet is used to classify the health conditions of the bearings. Case study is presented to examine the proposed met…
Shanghai University（上海大学）；University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）
Bearing fault identification and analysis is an important research area in the field of machinery fault diagnosis. Aiming at the common faults of rolling bearings, we propose a data-driven diagnostic algorithm based on the characteristics of bearing vibrations called multi-size kernel based adaptive convolutional neural network (MSKACNN). Using raw bearing vibration signals as the inputs, MSKACNN provides vibration feature learning and signal classification capabilities to identify and analyze bearing faults. Ball mixing is a ball bearing production quality problem that is difficult to identif…
Predictive maintenance, i.e. predicting failure to be few steps ahead of the fault, is one of the pillars of Industry 4.0. An effective method for that is to track early signs of degradation before a failure happens. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of full spectrum of the vibration data caused by the machines and data visualization technologies. This scheme is featured by no training data required and by quick start after installation. First, we propose to use full spectrum (as high-dimensional data vector) with no …
An enhanced statistical feature fusion approach using an improved distance evaluation algorithm and weighted K-nearest neighbor for bearing fault diagnosis
Unsupervised domain adaptation (UDA) has shown remarkable results in bearing fault diagnosis under changing working conditions in recent years. However, most UDA methods do not consider the geometric structure of the data. Furthermore, the global domain adaptation technique is commonly applied, which ignores the relation between subdomains. This paper addresses mentioned challenges by presenting the novel deep subdomain adaptation graph convolution neural network (DSAGCN), which has two key characteristics: First, graph convolution neural network (GCNN) is employed to model the structure of da…
Towards a more realistic evaluation of machine learning models for bearing fault diagnosis
Department of Electrical and Electronic Engineering, Federal University of Santa Catarina（电气与电子工程系，圣卡塔琳娜联邦大学）；Department of Mechanical Engineering, Federal University of Santa Catarina（机械工程系，圣卡塔琳娜联邦大学）
Faults related to hydrodynamic bearing can imply in high maintenance costs when late-detected and even to the total shutdown of the system. Thus, techniques of early fault diagnosis have high relevance to the reliability of rotating machinery. However, a common fault caused by inadequate bearing oil supply has not yet received appropriate attention. This paper presents a new approach to model and identify starved or excessive oil supply in hydrodynamic bearings. The developed identification technique is a model-based process that uses the rotor vibration signal to access the bearing lubricatio…
China University of Mining and Technology（中国矿业大学）；Xuzhou Medical University（徐州医科大学）
Traditional intelligent fault diagnosis of rolling bearings work well only under a common assumption that the labeled training data (source domain) and unlabeled testing data (target domain) are drawn from the same distribution. However, in many real-world applications, this assumption does not hold, especially when the working condition varies. In this paper, a new adversarial adaptive 1-D CNN called A2CNN is proposed to address this problem. A2CNN consists of four parts, namely, a source feature extractor, a target feature extractor, a label classifier and a domain discriminator. The layers …
Although data-driven fault diagnosis methods have been widely applied, massive labeled data are required for model training. However, a difficulty of implementing this in real industries hinders the application of these methods. Hence, an effective diagnostic approach that can work well in such situation is urgently needed.In this study, a multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method, which combines semi-supervised learning with improved deep embedded clustering (IDEC), is proposed to jointly explore scarce labeled data and massive unlabeled data. In the fir…
Knowledge Distillation and Enhanced Subdomain Adaptation Using Graph Convolutional Network for Resource-Constrained Bearing Fault Diagnosis
Department of Electrical and Computer Engineering, Tarbiat Modares University, Iran（电子与计算机工程系，塔里比亚特·莫达雷斯大学，伊朗）；Faculty of Engineering and Technology, University of Mazandaran, Iran（工程与技术学院，马赞德兰大学，伊朗）；Department of Medicine, Baylor College of Medicine, USA（医学系，贝勒医学院，美国）
Physics-Informed Deep Learning and Partial Transfer Learning for Bearing Fault Diagnosis in the Presence of Highly Missing Data
Faculty of Electrical and Computer Engineering, Tarbiat Modares University（塔里克·莫达雷斯大学电气与计算机工程学院）；Faculty of Technology and Engineering, University of Mazandaran（马赞德兰大学技术与工程学院）
Data-driven fault diagnosis methods often require abundant labeled examples for each fault type. On the contrary, real-world data is often unlabeled and consists of mostly healthy observations and only few samples of faulty conditions. The lack of labels and fault samples imposes a significant challenge for existing data-driven fault diagnosis methods. In this paper, we aim to overcome this limitation by integrating expert knowledge with domain adaptation in a synthetic-to-real framework for unsupervised fault diagnosis. Motivated by the fact that domain experts often have a relatively good un…
Full spectrum and holospectrum are homogenous information fusion technology developed for the fault diagnosis of rotating machinery, which is extensively exploited in the analysis of the orbits of rotor-bearing systems. However, they are not adapted for non-stationary signals, nor can they be used for fusion analysis of vibrations of multiple bearing sections. By drawing inspiration from the multivariate variational mode decomposition (MVMD) and the complex-valued signal decomposition, we propose a method called multivariate complex variational mode decomposition (MCVMD). It can simultaneously…
Aligarh Muslim University（阿里格尔穆斯林大学）；Okayama University（冈山大学）
Rolling bearings are the most crucial components of rotating machinery. Identifying defective bearings in a timely manner may prevent the malfunction of an entire machinery system. The mechanical condition monitoring field has entered the big data phase as a result of the fast advancement of machine parts. When working with large amounts of data, the manual feature extraction approach has the drawback of being inefficient and inaccurate. Data-driven methods like the Deep Learning method have been successfully used in recent years for mechanical intelligent fault detection. Convolutional neural…
Huazhong University of Science and Technology（华中科技大学）；Wuhan University of Science and Technology（武汉科技大学）；Zhejiang University（浙江大学）；Imperial College London（帝国理工学院）
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is p…
Fusing Spectral Correlation Density Imaging with Deep Learning for Intelligent Fault Diagnosis in Rotating Machinery
Department of Electrical and Information Engineering（电气与信息工程系）；Department of Mechanical and Manufacturing Engineering（机械与制造工程系）；Faculty of Engineering, University of Ruhuna（鲁胡纳大学工程学院）
Due to the existence of dataset shifts, the distributions of data acquired from different working conditions show significant differences in real-world industrial applications, which leads to performance degradation of traditional machine learning methods. This work provides a framework that combines supervised domain adaptation with prototype learning for fault diagnosis. The main idea of domain adaptation is to apply the Siamese architecture to learn a latent space where the mapped features are inter-class separable and intra-class similar for both source and target domains. Moreover, the pr…
Georgia Institute of Technology（佐治亚理工学院）；University of California, Berkeley（加州大学伯克利分校）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
Most of the data-driven approaches applied to bearing fault diagnosis up to date are established in the supervised learning paradigm, which usually requires a large set of labeled data collected a priori. In practical applications, however, obtaining accurate labels based on real-time bearing conditions can be far more challenging than simply collecting a huge amount of unlabeled data using various sensors. In this paper, we thus propose a semi-supervised learning approach for bearing anomaly detection using variational autoencoder (VAE) based deep generative models, which allows for effective…
Intelligent fault diagnosis has been increasingly improved with the evolution of deep learning (DL) approaches. Recently, the emerging graph neural networks (GNNs) have also been introduced in the field of fault diagnosis with the goal to make better use of the inductive bias of the interdependencies between the different sensor measurements. However, there are some limitations with these GNN-based fault diagnosis methods. First, they lack the ability to realize multiscale feature extraction due to the fixed receptive field of GNNs. Secondly, they eventually encounter the over-smoothing proble…
FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments
School of Energy and Power Engineering（能源与动力工程学院）；State Key Lab of Structural Analysis, Optimization and CAE Software for Industrial Equipment（结构分析、优化与工业设备CAE软件国家重点实验室）；Provincial Key Lab of Digital Twin for Industrial Equipment（工业设备数字孪生省级重点实验室）；College of Arts and Sciences, Clayton State University（艺术与科学学院，克莱顿州立大学）；Shenyang Blower Works Group Corporation（沈阳风机集团有限公司）
Luleå University of Technology（吕勒奥理工大学）；SKF-LTU University Technology Center（SKF-吕勒奥理工大学技术中心）
Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and large number of aging wind turbines. In particular, predictive maintenance planning requires the early detection of faults with few false positives. Achieving this type of detection is a challenging problem due to the complex and weak signatures of some faults, particularly the faults that occur in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using …
Multi-Dimensional time series classification and prediction has been widely used in many fields, such as disease prevention, fault diagnosis and action recognition. However, the traditional method needs manual intervention and inference, and cannot realize the figurative expression of multi-Dimensional data, which lead to inadequate information mining. Inspired by the strong power of deep learning technology in image processing, we propose a unified time-series image fusion framework to transform multi-modal data into 2D-image, and then realize automatic feature extraction and classification b…
Lab-scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning
Sepuluh Nopember Institute of Technology（印度尼西亚 Sepuluh Nopember 理工学院）；National Institute of Advanced Industrial Science and Technology（日本国立先进工业科学技术研究院）
