4.6 Article

Hybrid Feature Selection Framework for Bearing Fault Diagnosis Based on Wrapper-WPT

Related references

Note: Only part of the references are listed.
Article Engineering, Mechanical

Discriminative feature learning using a multiscale convolutional capsule network from attitude data for fault diagnosis of industrial robots

Jianyu Long et al.

Summary: This article proposes a fault diagnosis method for industrial robots based on an attitude sensor and a multiscale convolutional capsule network (MCCN). By monitoring the attitude of transmission components, fault features are learned from attitude data, and effective fault diagnosis is achieved by fusing multiscale features and spatial-relational features.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2023)

Article Automation & Control Systems

Wavelet Packet Decomposition-Based Multiscale CNN for Fault Diagnosis of Wind Turbine Gearbox

Dajian Huang et al.

Summary: This article presents an intelligent fault diagnosis method for wind turbine gearbox using wavelet packet decomposition (WPD) and deep learning. The proposed method combines the multiscale characteristic of WPD with the strong classification capacity of convolutional neural networks (CNNs) to effectively classify faults in the gearbox. Experimental results show that the presented method outperforms traditional CNN and multiscale CNN (MSCNN) for fault diagnosis.

IEEE TRANSACTIONS ON CYBERNETICS (2023)

Article Engineering, Mechanical

Multimodal loosening detection for threaded fasteners based on multiscale cross fuzzy entropy

Jiayu Huang et al.

Summary: This study is the first attempt to conduct multimodal loosening detection exploiting ultrasonic and audio response signals simultaneously. A novel loosening detection method, utilizing the complementarity of multimodal signals, is proposed and proved to have excellent detection performances in the applications of two different types of threaded fasteners. The proposed method outperforms other loosening detection methods and MCFE shows great advantages in extracting representative loosening features.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2023)

Article Multidisciplinary Sciences

A Fault Diagnosis Method of Rolling Bearing Based on Wavelet Packet Analysis and Deep Forest

Xiangong Li et al.

Summary: In this paper, a new fault diagnosis method for rolling bearings based on wavelet packet analysis and deep forest algorithm is proposed. The method extracts vibration signal features using wavelet packet analysis and trains a deep forest algorithm model to determine the fault location and type. The validation results show that this method has great potential in real coal mine main fans.

SYMMETRY-BASEL (2022)

Article Materials Science, Multidisciplinary

A Fault Feature Extraction Method Based on LMD and Wavelet Packet Denoising

Jingzong Yang et al.

Summary: A fault feature extraction method for a diaphragm pump check valve based on LMD and wavelet packet transform is proposed in this study. By decomposing, reconstructing, denoising, and extracting features from the signal, the proposed method can effectively extract the fault characteristics of a check valve.

COATINGS (2022)

Article Automation & Control Systems

Residual Gated Dynamic Sparse Network for Gearbox Fault Diagnosis Using Multisensor Data

Honghai Huang et al.

Summary: This article proposes a new method for gearbox fault diagnosis using multisensor fusion. The residual gated dynamic sparse network is used to improve feature learning and fusion ability. Experimental results and engineering application show that this method is more effective than others under noise interference.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

Article Energy & Fuels

Using MLP-GABP and SVM with wavelet packet transform-based feature extraction for fault diagnosis of a centrifugal pump

Maamar Al Tobi et al.

Summary: This study explores artificial intelligent training schemes based on multilayer perceptron with back propagation and genetic algorithm. The hybrid scheme is compared with traditional support vector machine approach for fault and normal scenario analysis of a centrifugal pump. The results show the effectiveness of the proposed hybrid artificial intelligent scheme with optimized hidden layers and neurons.

ENERGY SCIENCE & ENGINEERING (2022)

Article Computer Science, Information Systems

Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach

Ameer M. Hussein et al.

Summary: This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform. The proposed algorithm combines three phase current samples to generate a single current signal and divides the resulting samples into windows for processing. Two methods, non-overlapping and moving/overlapping, are used to create window samples. The algorithm performs two level WPT on the window samples, using information from the wavelet high frequency subbands for fault detection. Experimental results confirm the effectiveness and generalizability of the proposed method.

ELECTRONICS (2022)

Article Chemistry, Analytical

Damage Monitoring of Engineered Cementitious Composite Beams Reinforced with Hybrid Bars Using Piezoceramic-Based Smart Aggregates

Hui Qian et al.

Summary: In this study, the smart aggregate-based active sensing approach was used to monitor the damage of ECC beams under cyclic loading. Time domain analysis and wavelet packet analysis were performed to evaluate the crack development. A self-repairing index was proposed to evaluate the self-repairing capacity of the beams.

SENSORS (2022)

Article Chemistry, Multidisciplinary

Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network

Chuan-Sheng Wu et al.

Summary: In this paper, a convolutional neural network (CNN) is used to extract damage features of simple supported steel beams in the field of structural health monitoring (SHM). The method is validated through experiments and numerical simulations, demonstrating its effectiveness and accuracy with good robustness.

APPLIED SCIENCES-BASEL (2022)

Article Materials Science, Multidisciplinary

Fault Diagnosis of Check Valve Based on KPLS Optimal Feature Selection and Kernel Extreme Learning Machine

Xuyi Yuan et al.

Summary: A fault signal diagnosis model based on the kernel extreme learning machine (KELM) was constructed to diagnose the check valve of high-pressure diaphragm pumps. The model utilized multi-feature extraction and dimensionality reduction techniques, achieving accurate diagnosis of check valve faults.

COATINGS (2022)

Article Optics

A hybrid feature selection combining wavelet transform for quantitative analysis of heat value of coal using laser-induced breakdown spectroscopy

Peng Lu et al.

Summary: A hybrid feature selection method combining wavelet transform was proposed to analyze the heat value of coal using LIBS. Experimental results showed that the method effectively reduced calculation time and improved model performance.

APPLIED PHYSICS B-LASERS AND OPTICS (2021)

Article Automation & Control Systems

Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding

Weicheng Guo et al.

Summary: This article presents a novel prediction system for surface roughness by collecting signals during grinding process, extracting features, and utilizing long short-term memory network for accurate prediction. The proposed system shows excellent prediction performance and reduced costs, proving its practicality and feasibility.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

Article Chemistry, Analytical

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals

Xiaomin Zhang et al.

Summary: In this study, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed, which utilizes data from triaxial accelerometer and extracts data features using wavelet packet decomposition method. The FDI model based on LSTM network successfully detects and identifies quadcopter blade faults, outperforming the BP neural network-based FDI model in terms of performance and accuracy.

SENSORS (2021)

Article Engineering, Chemical

Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network

Yiyang Liu et al.

Summary: A new two-stage hierarchical convolutional neural network is proposed for fault diagnosis of rotating machinery bearings. The method models the failure mode and severity as a hierarchical structure and utilizes spectrum matrix and adaptive learning rate dynamic adjustment strategy to achieve fault diagnosis. The experimental results show that the method has achieved satisfactory results in fault pattern recognition and evaluation.

PROCESSES (2021)

Article Engineering, Electrical & Electronic

Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine

Mingliang Cui et al.

Summary: The study proposes a feature distance stack autoencoder (FD-SAE) for rolling bearing fault diagnosis, which has stronger feature extraction ability and faster network convergence speed compared to existing methods.

IEEE SENSORS JOURNAL (2021)

Article Chemistry, Analytical

A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors

Rafia Nishat Toma et al.

Summary: This study proposed a model for bearing fault classification using motor current signals, incorporating a deep auto-encoder and convolutional neural network, achieving high accuracy in classification while discussing the challenges of feature extraction.

SENSORS (2021)

Article Engineering, Electrical & Electronic

Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks

Yannis L. Karnavas et al.

Summary: The study proposes a deep learning model that concatenates features from two neural streams to capture global and local spatial information, effectively improving the accuracy of rolling element bearings fault detection.

IET ELECTRIC POWER APPLICATIONS (2021)

Article Chemistry, Analytical

Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion

Huibin Zhu et al.

Summary: This paper proposes a bearing fault diagnosis method based on feature fusion, which extracts the time-frequency features of bearing signals through Wavelet Packet Transform and constructs Multi-Weight Singular Value Decomposition to effectively diagnose bearings. The proposed method shows better fault diagnosis and feature extraction capabilities compared to traditional methods.

SENSORS (2021)

Article Chemistry, Analytical

An Explainable AI-Based Fault Diagnosis Model for Bearings

Md Junayed Hasan et al.

Summary: An explainable AI-based fault diagnosis model for bearings is proposed in this paper, involving five stages: data preprocessing, feature extraction, feature selection, feature filtration, and diagnostic decision. The effectiveness of the model is demonstrated on two different datasets, and comparisons with other fault diagnosis algorithms in rotating machinery are included.

SENSORS (2021)

Article Chemistry, Analytical

Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings

Juan Jose Saucedo-Dorantes et al.

Summary: The scientific and technological advances in the field of rotating electrical machinery are leading to increased efficiency in processes and systems involving them. The proposed methodology based on deep feature learning is effective in diagnosing and identifying bearing faults for different bearing technologies, such as metallic, hybrid, and ceramic bearings, in electromechanical systems. The methodology consists of three main stages: design of a deep learning-based model for feature extraction, feature fusion for increased discrimination capabilities, and final assessment using a softmax layer for classification results.

SENSORS (2021)

Article Chemistry, Analytical

Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection

Andrei S. Maliuk et al.

Summary: This paper proposes a Gaussian mixture model-based method for bearing fault band selection (GMM-WBBS) in signal processing, which achieves reliable feature extraction and interference elimination. Classification is done using the Weighted KNN algorithm. Experimental results demonstrate positive effects in filtering discriminative data and improving classification performance.

SENSORS (2021)

Article Engineering, Electrical & Electronic

Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks

Gaowei Xu et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

Article Chemistry, Multidisciplinary

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

Guoqiang Li et al.

APPLIED SCIENCES-BASEL (2020)

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)

Article Chemistry, Multidisciplinary

Feature Extraction for Bearing Fault Detection Using Wavelet Packet Energy and Fast Kurtogram Analysis

Xiaojun Zhang et al.

APPLIED SCIENCES-BASEL (2020)

Article Computer Science, Information Systems

Deep Residual Network for Identifying Bearing Fault Location and Fault Severity Concurrently

Longting Chen et al.

IEEE ACCESS (2020)

Article Automation & Control Systems

Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis

Minghang Zhao et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

Article Chemistry, Multidisciplinary

A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

Yinsheng Chen et al.

APPLIED SCIENCES-BASEL (2019)

Article Engineering, Electrical & Electronic

Estimation of Remaining Useful Life of Rolling Element Bearings Using Wavelet Packet Decomposition and Artificial Neural Network

Abbas Rohani Bastami et al.

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING (2019)

Article Engineering, Manufacturing

Intelligent Fault Diagnosis of Bearings Based on Energy Levels in Frequency Bands Using Wavelet and Support Vector Machines (SVM)

Seyed Majid Yadavar Nikravesh et al.

JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING (2019)

Article Automation & Control Systems

Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes

Minghang Zhao et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Article Engineering, Electrical & Electronic

Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning

Jiedi Sun et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2018)

Review Engineering, Mechanical

Rolling element bearing diagnostics-A tutorial

Robert B. Randall et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2011)

Article Computer Science, Interdisciplinary Applications

Feature Selection with the Boruta Package

Miron B. Kursa et al.

JOURNAL OF STATISTICAL SOFTWARE (2010)