☆ 4.5 Review

Machine learning for fault analysis in rotating machinery: A comprehensive review

HELIYON (2023)

Related references

Note: Only part of the references are listed.

Article Engineering, Electrical & Electronic

Lightweight Multiscale Convolutional Networks With Adaptive Pruning for Intelligent Fault Diagnosis of Train Bogie Bearings in Edge Computing Scenarios

Ao Ding et al.

Summary: Intelligent fault diagnosis of train bogie bearings based on edge computing is a promising technology to ensure the safety and reliability of train operation, which can give fault diagnosis systems better real-time performance and lower communication costs. This article proposes a new multiscale lightweight network with adaptive pruning for the intelligent diagnosis fault of train bogie bearings in edge computing scenarios. Experimental results demonstrate that the accuracy and complexity of the proposed network are superior to other state-of-the-art lightweight bearing fault diagnosis networks under varying operating conditions.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2023)

Add to Collection

Article Engineering, Mechanical

Smart machine fault diagnostics based on fault specified discrete wavelet transform

Oguzhan Das et al.

Journal of the Brazilian Society of Mechanical Sciences and Engineering (2023)

Add to Collection

Review Engineering, Multidisciplinary

A review of the application of deep learning in intelligent fault diagnosis of rotating machinery

Zhiqin Zhu et al.

Summary: With the rapid development of industry, fault diagnosis plays an important role in maintaining equipment health and ensuring safe operation. This paper reviews recent research on deep learning-based intelligent fault diagnosis for rotating machinery and categorizes the existing research into five categories based on deep learning models. The paper introduces the principles, applications, and features of these solutions, summarizes the main problems, and points out future research trends and hotspots.

MEASUREMENT (2023)

Add to Collection

Article Engineering, Multidisciplinary

A novel method for rotor fault diagnosis based on deep transfer learning with simulated samples

Ling Xiang et al.

Summary: A novel transfer unsupervised learning method called CCDAN is proposed in this paper to solve the problem of data without failure samples. The CCDAN extracts transfer characteristics from simulated samples by a theory model for experimental rotor fault diagnosis. The proposed method uses a dynamical model of Jeffcott rotor with crack fault to generate simulation samples and adopts multilayer convolutional network and MK-MMD to extract similar characteristics between source and target domains. Two independent classifiers are designed to constrain the features near the decision boundary in network learning, improving accuracy and fault identifying generalization.

MEASUREMENT (2023)

Add to Collection

Article Chemistry, Analytical

Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals

Vicente Biot-Monterde et al.

Summary: Induction motors (IMs) are widely used in industrial applications due to their robustness, versatility, and performance. However, rotor bar breakage (BRB) is a common fault that occurs due to high currents during start-up. The use of soft starters is common to reduce stresses, but it complicates fault diagnosis. This paper proposes a method to automatically classify rotor health state in IMs driven by soft starters using a Convolutional Neural Network (CNN) based on Persistence Spectrum (PS) of start-up stray-flux signals.

SENSORS (2023)

Add to Collection

Article Engineering, Marine

GASEL: Genetic algorithm-supported ensemble learning for fault detection in autonomous underwater vehicles

Duygu Bagci Das et al.

Summary: This study presents the GASEL approach, which combines GA and EL, for fault detection of AUVs. The experiments demonstrate that the GASEL approach is effective in predicting the faulty state of AUVs and outperforms state-of-the-art methods.

OCEAN ENGINEERING (2023)

Add to Collection

Article Automation & Control Systems

Unsupervised fault diagnosis of wind turbine bearing via a deep residual deformable convolution network based on subdomain adaptation under time-varying speeds

Pengfei Liang et al.

Summary: This paper proposes a novel deep residual deformable subdomain adaptation framework for cross-domain fault diagnosis of wind turbine bearing under time-varying speeds. The approach enhances the feature representation by using a deformable convolution module in the traditional residual network and improves the diagnostic model by removing the adversarial training mechanism and using a local maximum mean discrepancy to align the distributions of the same fault type in different domains. Experimental results show that this method can effectively diagnose faults in the target domain and significantly improve diagnosis accuracy and efficiency compared to existing methods.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2023)

Add to Collection

Article Computer Science, Artificial Intelligence

A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost

Zhiguo Liang et al.

Summary: A novel clustering fault diagnosis method for steam turbines based on t-distribution stochastic neighborhood embedding (t-SNE) and extreme gradient boosting (XGBoost) is proposed in this paper. The method can identify faults in advance, reduce losses for thermal plants, and achieve an overall accuracy of 97% and an early warning of at least two hours in advance. Experimental results show that this method can effectively evaluate the condition and provide fault warning for power plant equipment.

ALGORITHMS (2023)

Add to Collection

Article Engineering, Mechanical

Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks

Junchuan Shi et al.

Summary: Traditional model-based fault diagnosis techniques struggle to automatically extract spatial and temporal features, while deep learning methods are capable of automatic feature extraction but struggle to extract spatial and temporal features simultaneously.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

Add to Collection

Article Automation & Control Systems

A Domain Adaptation with Semantic Clustering (DASC) method for fault diagnosis of rotating machinery

Myungyon Kim et al.

Summary: This paper proposes a new method, called Domain Adaptation with Semantic Clustering (DASC), for improving fault diagnosis performance in rotating machinery. The method learns domain-invariant and discriminative features, and reduces domain discrepancy. By defining an additional semantic clustering loss and reducing it at multiple feature levels, the DASC method learns features that make samples better semantically clustered.

ISA TRANSACTIONS (2022)

Add to Collection

Article Engineering, Multidisciplinary

Adaptive decision-level fusion strategy for the fault diagnosis of axial piston pumps using multiple channels of vibration signals

Chao Qun et al.

Summary: This study proposed a multi-sensor fusion method to improve fault diagnosis performance of axial piston pumps, using a convolutional neural network to combine three channels of vibration data for more accurate classification of faults. Experimental results showed that the method significantly enhanced the classification accuracy by adjusting the probability distribution according to the learned weight matrix.

SCIENCE CHINA-TECHNOLOGICAL SCIENCES (2022)

Add to Collection

Article Chemistry, Analytical

A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm

Feng He et al.

Summary: A new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm is proposed, showing better and more reliable diagnosis effect compared to existing machine learning and deep learning methods.

SENSORS (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

A semi-supervised transferable LSTM with feature evaluation for fault diagnosis of rotating machinery

Zhi Tang et al.

Summary: The proposed method combines the entropy gain ratio with a semi-supervised transferable LSTM network for fault diagnosis of rotating machinery under variable working conditions, achieving competitive performance with reduced demand for massive labeled samples.

APPLIED INTELLIGENCE (2022)

Add to Collection

Article Engineering, Electrical & Electronic

Anomaly detection of steam turbine with hierarchical pre-warning strategy

Kun Yao et al.

Summary: Anomaly detection of steam turbines is crucial for stable power supply by recognizing infrequent instances within sensor data. This study proposes a hierarchical pre-warning strategy that combines clustering and classification methods for anomaly detection. Experimental results suggest that gradient boosting decision tree and random forest are more precise in detecting real anomalies of steam turbines.

IET GENERATION TRANSMISSION & DISTRIBUTION (2022)

Add to Collection

Article Engineering, Mechanical

Intelligent fault detection of reciprocating compressor using a novel discrete state space

Ying Zhang et al.

Summary: This paper proposes a novel method for intelligent fault detection of reciprocating compressors based on discrete state space construction and state space transformation. By extracting high-dimensional features from raw signals and discretizing them into labels representing potential states of operating conditions, the proposed method achieves more representative information and accurate fault detection.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

Add to Collection

Article Engineering, Mechanical

Vibration-based anomaly detection using LSTM/SVM approaches

Kilian Vos et al.

Summary: This paper presents an automated algorithm for identifying abnormal mechanical behavior by using a deep learning method and support vector machine algorithm. Two anomaly detection architectures are designed based on gearbox and helicopter datasets, and a novel two-step LSTM configuration is proposed.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

Add to Collection

Article Engineering, Electrical & Electronic

Clustering-Guided Novel Unsupervised Domain Adversarial Network for Partial Transfer Fault Diagnosis of Rotating Machinery

Hongru Cao et al.

Summary: This paper proposes a clustering-guided novel unsupervised domain adversarial network to address the problem of unsupervised partial transfer fault diagnosis. The network, constructed using domain-specific batch normalization, eliminates domain-specific information and enhances alignment between source and target domains. Additionally, an embedded clustering strategy is designed to learn tightly clustered target-domain features and suppress negative transfer.

IEEE SENSORS JOURNAL (2022)

Add to Collection

Article Engineering, Multidisciplinary

Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine

Waleligne Molla Salilew et al.

Summary: This study investigates the accuracy of different machine learning classification algorithms with three different data smoothing techniques for gas turbine fault detection and isolation task. The results show that K-Nearest Neighbours, Neural Network and Decision Tree classifiers exhibited high classification accuracy, with K-Nearest Neighbours showing the lowest computation time. The study concludes that K-Nearest Neighbours is the best classification technique for gas turbine engine fault detection and isolation application.

ALEXANDRIA ENGINEERING JOURNAL (2022)

Add to Collection

Article Computer Science, Information Systems

Bearing Vibration Signal Fault Diagnosis Based on LSTM-Cascade CatBoost

Miaomiao Yang et al.

Summary: In this paper, a new fault bearing diagnosis model is proposed, which can directly classify vibration signals without signal processing. The experimental results show that this model has high accuracy in two datasets.

JOURNAL OF INTERNET TECHNOLOGY (2022)

Add to Collection

Article Engineering, Industrial

Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles

Te Han et al.

Summary: This article presents a novel OOD detection-assisted trustworthy machinery fault diagnosis approach, which integrates multiple deep neural networks and utilizes uncertainty analysis to enhance the reliability and safety of intelligent models, showing significant advantages in identifying OOD samples.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2022)

Add to Collection

Article Engineering, Electrical & Electronic

A Deep-Learning-Based Multi-Modal Sensor Fusion Approach for Detection of Equipment Faults

Omer Kullu et al.

Summary: Condition monitoring is an approach used to detect and prevent unexpected equipment failures by monitoring machine conditions. This paper proposes a multi-modal sensor fusion-based deep learning model to detect equipment faults by combining information from different sensors and signal domains. The results show that this model can be advantageous in equipment fault detection.

MACHINES (2022)

Add to Collection

Article Engineering, Manufacturing

Detection of broken rotor bar fault in an induction motor using convolution neural network

Swapnil Gundewar et al.

Summary: This paper proposes an intelligent fault detection technique for induction motors using TDGCI and CNN. The technique automatically extracts features and achieves high accuracy. By optimizing CNN parameters, the computational time is significantly reduced.

JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING (2022)

Add to Collection

Article Engineering, Industrial

Extreme learning machine - radial basis function (ELM-RBF) networks for diagnosing faults in a steam turbine

Arian Dhini et al.

Summary: A fast and reliable fault diagnosis system for steam turbine is crucial in a thermal power plant to prevent unplanned breakdowns. The proposed ELM-RBF system, known for its fast computation, was tested with real fault historical data and showed high accuracy.

JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING (2022)

Add to Collection

Review Computer Science, Artificial Intelligence

Role of artificial intelligence in rotor fault diagnosis: a comprehensive review

Aneesh G. Nath et al.

Summary: Artificial intelligence (AI)-based rotor fault diagnosis (RFD) presents unique challenges within the Industry 4.0 revolution. This study is the first to review and define the role of AI in RFD, emphasizing fault-specific characteristic features and fault-wise analysis. It aims to bridge the gap between laboratory-based solutions and real-world industrial solutions for rotor fault diagnosis.

ARTIFICIAL INTELLIGENCE REVIEW (2021)

Add to Collection

Article Automation & Control Systems

Graph-based semi-supervised random forest for rotating machinery gearbox fault diagnosis

Shaozhi Chen et al.

Summary: An improved random forest (RF) algorithm based on graph-based semi-supervised learning (GSSL) and decision tree is proposed in this paper to enhance classification accuracy under insufficient labeled samples. The effectiveness of the algorithm is verified through hardware experiments, showing better accuracy than conventional methods in gearbox fault diagnosis, leading to further progress in machine learning with insufficient and unlabeled samples.

CONTROL ENGINEERING PRACTICE (2021)

Add to Collection

Article Engineering, Multidisciplinary

Casting defect detection in X-ray images using convolutional neural networks and attention-guided data augmentation

Lili Jiang et al.

Summary: This paper proposes a weakly-supervised Convolutional Neural Network model based on X-ray images to recognize casting defects. The model achieves high test accuracy and recall through attention maps and data augmentation methods, demonstrating accuracy and robustness. The efficiency of the proposed approach is verified by comparing with existing methods and ablation experiments.

MEASUREMENT (2021)

Add to Collection

Article Engineering, Mechanical

A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning

Kun Yu et al.

Summary: A three-stage semi-supervised learning method is proposed in this study for intelligent bearing fault diagnosis under limited labeled data, using data augmentation and metric learning to enhance classifier performance. Experimental results demonstrate that the proposed method outperforms existing diagnostic methods in bearing fault diagnosis under limited labeled samples.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

Add to Collection

Article Engineering, Multidisciplinary

Gearbox fault diagnosis based on Multi-Scale deep residual learning and stacked LSTM model

K. N. Ravikumar et al.

Summary: A fault diagnosis model based on MDRL-SLSTM achieves high classification accuracy in gearbox health prediction task by utilizing CNN and residual learning for feature extraction. The model demonstrates better diagnostic performance with vibration data of the gearbox.

MEASUREMENT (2021)

Add to Collection

Article Engineering, Multidisciplinary

Blockage and cavitation detection in centrifugal pumps from dynamic pressure signal using deep learning algorithm

Rajiv Tiwari et al.

Summary: Centrifugal pumps are crucial in industrial applications, but operational faults may occur during operation. Monitoring the health status of the pumps is essential. This study focuses on using fluid pressure to identify blockages and cavitation in centrifugal pumps, and assess their severity.

MEASUREMENT (2021)

Add to Collection

Article Engineering, Multidisciplinary

Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification

Zirui Wang et al.

Summary: A novel fault diagnosis scheme for planetary gearbox using multi-criteria fault feature selection and heterogeneous ensemble learning classification is proposed in this paper, significantly improving the accuracy and robustness of fault diagnosis. Techniques such as vibration signal analysis, MOEA/D for feature selection, and Dezert-Smarandache rules for classifier-level fusion are utilized to achieve diverse lower dimension quasi optimal fault features and enhance overall accuracy of the fault diagnosis.

MEASUREMENT (2021)

Add to Collection

Article Engineering, Mechanical

A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery

Xinya Wu et al.

Summary: Accurate fault diagnosis is crucial for the safe operation of rotating machinery. The proposed method, hybrid classification autoencoder, utilizes both labeled and unlabeled data for training and achieved high diagnostic accuracies in experiments with minimal labeled data. This approach shows potential for more efficient fault diagnosis in the future.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

Add to Collection

Article Computer Science, Information Systems

Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network

Shaoqing Liu et al.

Summary: Fast and efficient fault diagnosis is crucial for ensuring uninterrupted operation of equipment. The proposed method, based on Convolutional Neural Network (CNN) and feature fusion, utilizes the interrelationship between data sources to improve accuracy and speed in fault diagnosis.

COMPUTER COMMUNICATIONS (2021)

Add to Collection

Article Engineering, Multidisciplinary

Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images

Anurag Choudhary et al.

Summary: This paper presents a noninvasive thermal image-based method for bearing fault diagnosis in rotating machines, comparing the performance of shallow and deep learning approaches using ANN and CNN. The CNN based on LeNet-5 structure outperforms ANN in fault identification, and the use of infrared thermography enables automatic detection of bearing faults for early warnings and reduced system shutdowns.

MEASUREMENT (2021)

Add to Collection

Article Engineering, Multidisciplinary

Gas path fault diagnosis for gas turbine group based on deep transfer learning

Xusheng Yang et al.

Summary: This paper proposes a new method to identify the health states of newly-run gas turbines by transferring shared knowledge from data-rich gas turbines. Experiments show that this method can effectively improve the diagnostic accuracy of newly-run gas turbines. By employing convolutional neural networks and transfer learning, diagnostic efficiency is enhanced.

MEASUREMENT (2021)

Add to Collection

Article Automation & Control Systems

Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Majid Hussain et al.

Summary: This paper presents an induction motor fault detection and identification system using signal processing techniques. A comparative investigation confirmed the superiority of the long short-term memory model in identifying individual faults, achieving 97.87% accuracy.

INTELLIGENT AUTOMATION AND SOFT COMPUTING (2021)

Add to Collection

Article Computer Science, Information Systems

A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning

Md Junayed Hasan et al.

Summary: In this study, a novel automated health state diagnosis framework for centrifugal pump is proposed, combining a signal to time-frequency imaging technique and an Adaptive Deep Convolution Neural Network model (ADCNN). The framework employs Continuous Wavelet Transform to extract fault information from vibration signals, which are then converted into grayscale images for input to the ADCNN model. The performance of this diagnostic framework was validated with experimental results showing an average improvement of 4.7 - 15.6% over existing methods.

IEEE ACCESS (2021)

Add to Collection

Article Computer Science, Information Systems

An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm

Lanjun Wan et al.

Summary: An efficient rolling bearing fault diagnosis method based on Spark and improved random forest (IRF) algorithm is proposed to enhance diagnosis speed and accuracy, allowing for quick and effective fault diagnosis in large-scale rolling bearing datasets.

IEEE ACCESS (2021)

Add to Collection

Article Engineering, Electrical & Electronic

An Early Classification Approach for Improving Structural Rotor Fault Diagnosis

Aneesh G. Nath et al.

Summary: A framework for diagnosing structural rotor faults effectively was proposed to address issues related to industrial data acquisition and data scarcity. An early classification approach was used in the fault classification phase, demonstrating exceptional performance in SRF diagnosis.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

Add to Collection

Article Engineering, Electrical & Electronic

Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor

Prashant Kumar et al.

Summary: The study proposes a fault detection approach using a combination of Convolutional Neural Network (CNN) and batch normalisation (BN) to simultaneously detect bearing faults and broken rotor bars in squirrel cage induction motors (SCIMs), utilizing the different patterns of SCIM vibration signals for fault diagnosis.

IET ELECTRIC POWER APPLICATIONS (2021)

Add to Collection

Article Computer Science, Information Systems

Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

Gopi Krishna Durbhaka et al.

Summary: This paper introduces a new hybrid modeling approach for classifying gearbox faults from vibration signals data, demonstrating higher diagnostic accuracy compared to conventional methods.

CMC-COMPUTERS MATERIALS & CONTINUA (2021)

Add to Collection

Review Engineering, Multidisciplinary

Data fusion approaches for structural health monitoring and system identification: Past, present, and future

Rih-Teng Wu et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2020)

Add to Collection

Article Engineering, Aerospace

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Yongbo Li et al.

CHINESE JOURNAL OF AERONAUTICS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor

Diego Cabrera et al.

NEUROCOMPUTING (2020)

Add to Collection

Article Engineering, Multidisciplinary

A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery

Xiaoli Zhao et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2020)

Add to Collection

Article Chemistry, Analytical

Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss

Aijun Yin et al.

SENSORS (2020)

Add to Collection

Article Engineering, Multidisciplinary

Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network

Ying Zhang et al.

MEASUREMENT (2020)

Add to Collection

Review Engineering, Mechanical

Applications of machine learning to machine fault diagnosis: A review and roadmap

Yaguo Lei et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)

Add to Collection

Article Engineering, Electrical & Electronic

A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion

Duy Tang Hoang et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

Add to Collection

Article Engineering, Mechanical

Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault

Diogo Stuani Alves et al.

MECHANISM AND MACHINE THEORY (2020)

Add to Collection

Article Computer Science, Hardware & Architecture

Generative Adversarial Networks

Ian Goodfellow et al.

COMMUNICATIONS OF THE ACM (2020)

Add to Collection

Article Engineering, Multidisciplinary

Fast bearing fault diagnosis of rolling element using Levy Moth-Flame optimization algorithm and Naive Bayes

Shuang Sun et al.

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY (2020)

Add to Collection

Article Automation & Control Systems

Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization

Sheng Guo et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

Add to Collection

Article Engineering, Mechanical

Bearing fault identification based on convolutional neural network by different input modes

Tian Han et al.

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING (2020)

Add to Collection

Review Engineering, Mechanical

Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review

Purushottam Gangsar et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)

Add to Collection

Article Engineering, Mechanical

Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery

Xiang Li et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)

Add to Collection

Article Engineering, Mechanical

Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis

Moslem Azamfar et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)

Add to Collection

Article Chemistry, Multidisciplinary

Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram

Minh Tuan Pham et al.

APPLIED SCIENCES-BASEL (2020)

Add to Collection

Review Computer Science, Interdisciplinary Applications

Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges

Jovani Dalzochio et al.

COMPUTERS IN INDUSTRY (2020)

Add to Collection

Review Computer Science, Information Systems

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Dhiraj Neupane et al.

IEEE ACCESS (2020)

Add to Collection

Article Computer Science, Information Systems

Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis Under Strong Noises and Variable Loads

Meiying Qiao et al.

IEEE ACCESS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis

Zhiyu Zhu et al.

NEUROCOMPUTING (2019)

Add to Collection

Article Automation & Control Systems

A fault diagnosis scheme for rotating machinery using hierarchical symbolic analysis and convolutional neural network

Yuantao Yang et al.

ISA TRANSACTIONS (2019)

Add to Collection

Article Engineering, Mechanical

A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines

Xingxing Jiang et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

Add to Collection

Review Engineering, Mechanical

A review of stochastic resonance in rotating machine fault detection

Siliang Lu et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

Add to Collection

Article Chemistry, Analytical

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

Gaowei Xu et al.

SENSORS (2019)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network

M. M. Manjurul Islam et al.

COMPUTERS IN INDUSTRY (2019)

Add to Collection

Article Engineering, Multidisciplinary

Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images

Xiaoxun Zhu et al.

MEASUREMENT (2019)

Add to Collection

Article Chemistry, Analytical

A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion

Wenfeng Gong et al.

SENSORS (2019)

Add to Collection

Article Engineering, Electrical & Electronic

Real-time broken rotor bar fault detection and classification by shallow 1D convolutional neural networks

Turker Ince

ELECTRICAL ENGINEERING (2019)

Add to Collection

Review Green & Sustainable Science & Technology

Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future

Yang Zhao et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)

Add to Collection

Article Chemistry, Multidisciplinary

Fault Diagnosis of Induction Motor Using Convolutional Neural Network

Jong-Hyun Lee et al.

APPLIED SCIENCES-BASEL (2019)

Add to Collection

Article Engineering, Biomedical

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

Bo Wu et al.

JOURNAL OF VIBROENGINEERING (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

Novel paralleled extreme learning machine networks for fault diagnosis of wind turbine drivetrain

Xian-Bo Wang et al.

MEMETIC COMPUTING (2019)

Add to Collection

Review Computer Science, Information Systems

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

Syahril Ramadhan Saufi et al.

IEEE ACCESS (2019)

Add to Collection

Article Computer Science, Information Systems

Fault Diagnosis of Wind Turbine Gearbox Based on Deep Bi-Directional Long Short-Term Memory Under Time-Varying Non-Stationary Operating Conditions

Lixiao Cao et al.

IEEE ACCESS (2019)

Add to Collection

Review Transportation

Deep learning-based image recognition for autonomous driving

Hironobu Fujiyoshi et al.

IATSS RESEARCH (2019)

Add to Collection

Article Computer Science, Information Systems

Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

Yibing Li et al.

IEEE ACCESS (2019)

Add to Collection

Article Acoustics

A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network

Boualem Merainani et al.

JOURNAL OF VIBRATION AND CONTROL (2018)

Add to Collection

Article Engineering, Mechanical

An integrated method based on CEEMD-SampEn and the correlation analysis algorithm for the fault diagnosis of a gearbox under different working conditions

Jiayu Chen et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)

Add to Collection

Article Chemistry, Multidisciplinary

Broken Rotor Bar Fault Detection and Classification Using Wavelet Packet Signature Analysis Based on Fourier Transform and Multi-Layer Perceptron Neural Network

Sahar Zolfaghari et al.

APPLIED SCIENCES-BASEL (2018)

Add to Collection

Article Engineering, Mechanical

Crack Fault Classification for Planetary Gearbox Based on Feature Selection Technique and K-means Clustering Method

Li-Ming Wang et al.

CHINESE JOURNAL OF MECHANICAL ENGINEERING (2018)

Add to Collection

Article Automation & Control Systems

Nested SVDD in DAG SVM for induction motor condition monitoring

Slaheddine Zgarni et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2018)

Add to Collection

Review Computer Science, Artificial Intelligence

The use of machine learning algorithms in recommender systems: A systematic review

Ivens Portugal et al.

EXPERT SYSTEMS WITH APPLICATIONS (2018)

Add to Collection

Article Automation & Control Systems

Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

Min Xia et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2018)

Add to Collection

Article Computer Science, Artificial Intelligence

Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN

Rene-Vinicio Sanchez et al.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2018)

Add to Collection

Article Automation & Control Systems

Improved similarity-based modeling for the classification of rotating-machine failures

Matheus A. Marins et al.

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS (2018)

Add to Collection

Article Engineering, Biomedical

Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

Rahmoune Chemseddine et al.

JOURNAL OF VIBROENGINEERING (2018)

Add to Collection

Article Engineering, Multidisciplinary

A smart and intuitive machine condition monitoring in the Industry 4.0 scenario

G. Dinardo et al.

MEASUREMENT (2018)

Add to Collection

Article Chemistry, Analytical

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network

Sheng Guo et al.

SENSORS (2018)

Add to Collection

Article Chemistry, Analytical

Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data

Nannan Zhang et al.

SENSORS (2018)

Add to Collection

Article Chemistry, Multidisciplinary

An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation

Zenghui An et al.

APPLIED SCIENCES-BASEL (2018)

Add to Collection

Article Automation & Control Systems

Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors

Ignacio Martin-Diaz et al.

ISA TRANSACTIONS (2018)

Add to Collection

Article Chemistry, Analytical

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Muhammad Syafrudin et al.

SENSORS (2018)

Add to Collection

Article Thermodynamics

Fault diagnosis using improved pattern spectrum and fruit fly optimization algorithm-support vector machine

Bing Wang et al.

ADVANCES IN MECHANICAL ENGINEERING (2018)

Add to Collection

Article Acoustics

Fault diagnosis of wind turbine gearbox based on wavelet neural network

Chen Huitao et al.

JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL (2018)

Add to Collection

Article Engineering, Industrial

Engine gearbox fault diagnosis using machine learning approach

Kiran Vernekar et al.

JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING (2018)

Add to Collection

Article Automation & Control Systems

Fault diagnostics of spur gear using decision tree and fuzzy classifier

A. Krishnakumari et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)

Add to Collection

Article Engineering, Manufacturing

Predictive maintenance, its implementation and latest trends

Sule Selcuk

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE (2017)

Add to Collection

Article Computer Science, Artificial Intelligence

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2017)

Add to Collection

Article Engineering, Multidisciplinary

A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox

Luyang Jing et al.

MEASUREMENT (2017)

Add to Collection

Article Computer Science, Artificial Intelligence

Looseness diagnosis method for connecting bolt of fan foundation based on sensitive mixed-domain features of excitation-response and manifold learning

Renxiang Chen et al.

NEUROCOMPUTING (2017)

Add to Collection

Article Chemistry, Analytical

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

Muhammad Sohaib et al.

SENSORS (2017)

Add to Collection

Article Computer Science, Information Systems

Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions

Ran Zhang et al.

IEEE ACCESS (2017)

Add to Collection

Article Engineering, Manufacturing

Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario

Zhe Li et al.

ADVANCES IN MANUFACTURING (2017)

Add to Collection

Article Engineering, Electrical & Electronic

Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network

Zhuyun Chen et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2017)

Add to Collection

Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

Add to Collection

Article Acoustics

A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Geva clustering

Kun Yu et al.

APPLIED ACOUSTICS (2017)

Add to Collection

Article Engineering, Civil

Preventive and corrective maintenance - cost comparison and cost-benefit analysis

Christer Stenstrom et al.

STRUCTURE AND INFRASTRUCTURE ENGINEERING (2016)

Add to Collection

Article Computer Science, Artificial Intelligence

Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery

Dongyang Dou et al.

APPLIED SOFT COMPUTING (2016)

Add to Collection

Article Engineering, Electrical & Electronic

Condition Monitoring and Fault Diagnosis of Induction Motor Using Support Vector Machine

Rakesh A. Patel et al.

ELECTRIC POWER COMPONENTS AND SYSTEMS (2016)

Add to Collection

Article Computer Science, Artificial Intelligence

A new support vector data description method for machinery fault diagnosis with unbalanced datasets

Lixiang Duan et al.

EXPERT SYSTEMS WITH APPLICATIONS (2016)

Add to Collection

Article Acoustics

Convolutional Neural Network Based Fault Detection for Rotating Machinery

Olivier Janssens et al.

JOURNAL OF SOUND AND VIBRATION (2016)

Add to Collection

Article Engineering, Multidisciplinary

A sparse auto-encoder-based deep neural network approach for induction motor faults classification

Wenjun Sun et al.

MEASUREMENT (2016)

Add to Collection

Article Engineering, Mechanical

Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

Chuan Li et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2016)

Add to Collection

Article Automation & Control Systems

A Survey of Fault Diagnosis and Fault-Tolerant Techniques-Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

Zhiwei Gao et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2015)

Add to Collection

Article Computer Science, Artificial Intelligence

S-transform and ANFIS for detecting and classifying the vibration signals of induction motor

C. N. Gnanaprakasam et al.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2015)

Add to Collection

Article Engineering, Multidisciplinary

A Diagnosis Method for Rotation Machinery Faults Based on Dimensionless Indexes Combined with K-Nearest Neighbor Algorithm

Jianbin Xiong et al.

MATHEMATICAL PROBLEMS IN ENGINEERING (2015)

Add to Collection

Article Engineering, Mechanical

Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery

Na Lu et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2015)

Add to Collection

Article Multidisciplinary Sciences

Human-level control through deep reinforcement learning

Volodymyr Mnih et al.

NATURE (2015)

Add to Collection

Article Computer Science, Artificial Intelligence

Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

Chuan Li et al.

NEUROCOMPUTING (2015)

Add to Collection

Article Engineering, Mechanical

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego Cabrera et al.

FRONTIERS OF MECHANICAL ENGINEERING (2015)

Add to Collection

Article Engineering, Mechanical

A sequential fuzzy diagnosis method for rotating machinery using ant colony optimization and possibility theory

Hao Sun et al.

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2014)

Add to Collection

Article Engineering, Mechanical

Multi-scale enveloping order spectrogram for rotating machine health diagnosis

Jinjiang Wang et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2014)

Add to Collection

Article Automation & Control Systems

Vibration analysis for bearing fault detection and classification using an intelligent filter

Jafar Zarei et al.

MECHATRONICS (2014)

Add to Collection

Article Acoustics

Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features

Ling-li Jiang et al.

SHOCK AND VIBRATION (2014)

Add to Collection

Article Automation & Control Systems

Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique

Abdenour Soualhi et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2013)

Add to Collection

Article Automation & Control Systems

Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks

Zhenyou Zhang et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2013)

Add to Collection

Article Engineering, Multidisciplinary

Exploiting sound signals for fault diagnosis of bearings using decision tree

M. Amarnath et al.

MEASUREMENT (2013)

Add to Collection

Article Computer Science, Artificial Intelligence

Application of the fuzzy min-max neural network to fault detection and diagnosis of induction motors

Manjeevan Seera et al.

NEURAL COMPUTING & APPLICATIONS (2013)

Add to Collection

Article Computer Science, Artificial Intelligence

Ant colony clustering analysis based intelligent fault diagnosis method and its application to rotating machinery

Debin Zhao et al.

PATTERN ANALYSIS AND APPLICATIONS (2013)

Add to Collection

Article Computer Science, Interdisciplinary Applications

An overview of time-based and condition-based maintenance in industrial application

Rosmaini Ahmad et al.

COMPUTERS & INDUSTRIAL ENGINEERING (2012)

Add to Collection

Article Engineering, Electrical & Electronic

Deep Neural Networks for Acoustic Modeling in Speech Recognition

Geoffrey Hinton et al.

IEEE SIGNAL PROCESSING MAGAZINE (2012)

Add to Collection

Article Energy & Fuels

Reliability-Centered Maintenance for Wind Turbines Based on Statistical Analysis and Practical Experience

Katharina Fischer et al.

IEEE TRANSACTIONS ON ENERGY CONVERSION (2012)

Add to Collection

© Peeref 2019-2024. All rights reserved.