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Machine Fault Diagnosis Using Audio Sensors Data and Explainable AI Techniques-LIME and SHAP
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作者 Aniqua Nusrat Zereen Abir Das Jia Uddin 《Computers, Materials & Continua》 SCIE EI 2024年第9期3463-3484,共22页
Machine fault diagnostics are essential for industrial operations,and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions.Machine learni... Machine fault diagnostics are essential for industrial operations,and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions.Machine learning models,especially those utilizing complex algorithms like deep learning,have demonstrated major potential in extracting important information fromlarge operational datasets.Despite their efficiency,machine learningmodels face challenges,making Explainable AI(XAI)crucial for improving their understandability and fine-tuning.The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study.The technique is applied to evaluate different machine-learning algorithms.Extreme Gradient Boosting,Support Vector Machine,Gaussian Naive Bayes,and Random Forest classifiers are used alongside Logistic Regression(LR)as a baseline model because their efficacy and simplicity are evaluated thoroughly with empirical analysis.The XAI is used as a targeted feature selection technique to select among 29 features of the time and frequency domain.The XAI approach is lightweight,trained with only targeted features,and achieved similar results as the traditional approach.The accuracy without XAI on baseline LR is 79.57%,whereas the approach with XAI on LR is 80.28%. 展开更多
关键词 Explainable AI feature selection machine learning machine fault diagnosis
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Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis
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作者 Kwok Tai Chui Brij B.Gupta +1 位作者 Varsha Arya Miguel Torres-Ruiz 《Computers, Materials & Continua》 SCIE EI 2024年第1期1363-1379,共17页
The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation fo... The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains. 展开更多
关键词 Deep learning incremental learning machine fault diagnosis negative transfer transfer learning
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Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments
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作者 Feiyang Cao 《Journal of Electronic Research and Application》 2024年第4期108-114,共7页
In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machin... In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects. 展开更多
关键词 Intelligent manufacturing machine tool fault diagnosis Predictive maintenance Big data machine learning System integration
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The Research on Hybrid Intelligent Fault-diagnosisSystem of CNC Machine Tools
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作者 WANG Runxiao ZHOU Hui +1 位作者 QIN Xiansheng JIAN Chongjun 《International Journal of Plant Engineering and Management》 2000年第4期129-135,共7页
After analyzing the structure and characteristics of the hybrid intelligent diagnosis system of CNC machine toolsCNC-HIDS), we describe the intelligent hybrid mechanism of the CNC-HIDS, and present the evaluation and ... After analyzing the structure and characteristics of the hybrid intelligent diagnosis system of CNC machine toolsCNC-HIDS), we describe the intelligent hybrid mechanism of the CNC-HIDS, and present the evaluation and the running instance of the system. Through tryout and validation, we attain satisfactory results. 展开更多
关键词 CNC machine tools hybrid mechanism intelligent diagnosis machine fault
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An Interpretable Denoising Layer for Neural Networks Based on Reproducing Kernel Hilbert Space and its Application in Machine Fault Diagnosis 被引量:4
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作者 Baoxuan Zhao Changming Cheng +3 位作者 Guowei Tu Zhike Peng Qingbo He Guang Meng 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2021年第3期104-114,共11页
Deep learning algorithms based on neural networks make remarkable achievements in machine fault diagnosis,while the noise mixed in measured signals harms the prediction accuracy of networks.Existing denoising methods ... Deep learning algorithms based on neural networks make remarkable achievements in machine fault diagnosis,while the noise mixed in measured signals harms the prediction accuracy of networks.Existing denoising methods in neural networks,such as using complex network architectures and introducing sparse techniques,always suffer from the difficulty of estimating hyperparameters and the lack of physical interpretability.To address this issue,this paper proposes a novel interpretable denoising layer based on reproducing kernel Hilbert space(RKHS)as the first layer for standard neural networks,with the aim to combine the advantages of both traditional signal processing technology with physical interpretation and network modeling strategy with parameter adaption.By investigating the influencing mechanism of parameters on the regularization procedure in RKHS,the key parameter that dynamically controls the signal smoothness with low computational cost is selected as the only trainable parameter of the proposed layer.Besides,the forward and backward propagation algorithms of the designed layer are formulated to ensure that the selected parameter can be automatically updated together with other parameters in the neural network.Moreover,exponential and piecewise functions are introduced in the weight updating process to keep the trainable weight within a reasonable range and avoid the ill-conditioned problem.Experiment studies verify the effectiveness and compatibility of the proposed layer design method in intelligent fault diagnosis of machinery in noisy environments. 展开更多
关键词 machine fault diagnosis Reproducing kernel Hilbert space(RKHS) Regularization problem Denoising layer Neural network
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FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE 被引量:4
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作者 LIU Guanjun LIU Xinmin QIU Jing HU Niaoqing 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2007年第5期92-95,共4页
Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measur... Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples. 展开更多
关键词 Hidden Markov model Support vector machine Fault diagnosis
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An Expert System Based on Multi-reasoning Mechanism for Port Machine Diagnosis 被引量:1
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作者 Y. Ding G.L. Lin 《Journal of Shipping and Ocean Engineering》 2011年第2期101-108,共8页
Expert system plays an important role in port machine diagnosis, which aims at automatic equipment test for higher availability and efficiency of port operations. In this study, a port machine diagnosis expert system ... Expert system plays an important role in port machine diagnosis, which aims at automatic equipment test for higher availability and efficiency of port operations. In this study, a port machine diagnosis expert system is proposed based on multi-reasoning mechanism. Relying on the knowledge acquired from the experienced experts in the port machine engineering, the system builds a library of relative experience and a set of rules of reasoning and estimating. Multi-reasoning mechanism that simulates the decision-making process of domain experts is employed to achieve reliable diagnosis results. The reasoning machine integrates artificial neural network, uncertain decision making and decision tree, which complements each other by sustainable growing voting mechanism. The effect of this multi-reasoning mechanism is evaluated and validated by means of Matthew's Correlation Coefficient (MCC). The system incorporating the mechanism is successfully designed, implemented and applied in Shanghai Port. 展开更多
关键词 Port machine diagnosis multi-reasoning mechanism ANN uncertain decision making decision tree.
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