A multi-branch redundant adversarial net for intelligent fault diagnosis of multiple components under drastically variable speeds

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A sparse multivariate time series model-based fault detection method for gearboxes under variable speed condition

Yuejian Chen et al.

Summary: This paper proposes a method for fault detection based on a sparse LPV-VAR model, which can accurately model multichannel non-stationary vibration signals, leading to higher accuracy in fault detection.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

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Article Automation & Control Systems

Deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM

Prashant Kumar et al.

Summary: Early fault detection in squirrel cage induction motor is crucial for minimizing downtime and maximizing production. This paper presents an adaptive gradient optimizer based deep convolutional neural network (ADG-dCNN) technique for bearing and rotor faults detection in squirrel cage induction motor. The proposed model attained an average accuracy of 99.70% and can be extended to other machinery with multiple sensors.

ISA TRANSACTIONS (2021)

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Article Automation & Control Systems

A Small Sample Focused Intelligent Fault Diagnosis Scheme of Machines via Multimodules Learning With Gradient Penalized Generative Adversarial Networks

Tianci Zhang et al.

Summary: This study proposes an intelligent fault diagnosis method based on generative adversarial networks, where the generator generates mechanical signals to expand the training dataset and the classifier has the ability to extract fault features and classify with high accuracy under small sample conditions.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

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Article Engineering, Electrical & Electronic

Intelligent Rolling Bearing Fault Diagnosis via Vision ConvNet

Yinjun Wang et al.

Summary: This paper designed a one-dimensional vision ConvNet (VCN) which improves the network's ability to detect fault characteristic frequency bands by using a multi-kernel structure. By analyzing artificially generated data and experimental data, the paper discusses the setting method of large convolution kernels and strides. Compared to traditional CNN, wide first-layer kernels (WDCNN) and multiscale kernel-based ResCNN (MK-ResCNN), this network enhances recognition accuracy through a more stable training process for rolling bearing fault classification.

IEEE SENSORS JOURNAL (2021)

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Peng Chen et al.

Summary: Fault detection and diagnosis are crucial for planetary gearboxes in rotating machinery, but face challenges in non-stationary operating conditions. Signal processing methods are typically used, but current deep learning algorithms are limited to stationary conditions. To address this, an Automatic Speed Adaption Neural Network (ASANN) model with incorporating of instantaneous rotating speed is proposed in this paper, offering extraordinary capacity for planetary gearbox fault detection under varying operational scenarios.

MEASUREMENT (2021)

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Article Automation & Control Systems

Cascade Convolutional Neural Network With Progressive Optimization for Motor Fault Diagnosis Under Nonstationary Conditions

Fei Wang et al.

Summary: A cascade CNN (C-CNN) with progressive optimization is proposed for motor fault diagnosis in nonstationary conditions, addressing the limitations of traditional CNNs. Through physical characteristics of nonstationary vibration signals, the C-CNN achieves better performance in both constant and variable speed scenarios compared to existing methods.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Modeling and design of an automatic generation control for hydropower plants using Neuro-Fuzzy controller

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ENERGY REPORTS (2021)

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Article Computer Science, Artificial Intelligence

Image Classification Approach Using Machine Learning and an Industrial Hadoop Based Data Pipeline

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BIG DATA RESEARCH (2021)

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Underwater target recognition using convolutional recurrent neural networks with 3-D Mel-spectrogram and data augmentation

Feng Liu et al.

Summary: Passive recognition of underwater acoustic targets is a hot research issue, and combining deep learning and time-frequency spectrogram for feature extraction, as well as utilizing data augmentation and deep neural network, can effectively enhance the recognition accuracy. Experimental results on the ShipsEar dataset show that the proposed method achieves high recognition performance.

APPLIED ACOUSTICS (2021)

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Article Engineering, Multidisciplinary

A novel tacholess order analysis method for bearings operating under time-varying speed conditions

Madhurjya Dev Choudhury et al.

Summary: A new tacholess order tracking technique based on the FDTW algorithm is proposed in this paper for detecting bearing defects. By aligning and reconstructing the shaft envelope signal, a richer fault indication is provided for defect detection.

MEASUREMENT (2021)

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Article Chemistry, Multidisciplinary

Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis

Kun Xu et al.

Summary: This study introduces a new intelligent fault diagnosis method DAN-DAM, which achieves alignment at both class-level and domain-level through double adversarial mechanism, and verifies its effectiveness through multiple transfer experiments.

APPLIED SCIENCES-BASEL (2021)

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Article Engineering, Electrical & Electronic

Cooperative Estimation to Reconstruct the Parametric Flow Field Using Multiple AUVs

Linlin Shi et al.

Summary: This article investigates the cooperative estimation problem of recovering the parametric flow field using sensor measurements from an AUV team. It establishes a parametric flow model and formulates a set of nonlinear equations to present the deterministic and continuous relationship between errors and the flow model parameters. The proposed iterative algorithm estimates model parameters by solving an inverse problem for these nonlinear equations and simulations illustrate its effectiveness.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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A transfer convolutional neural network for fault diagnosis based on ResNet-50

Long Wen et al.

NEURAL COMPUTING & APPLICATIONS (2020)

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Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty

Xin Gao et al.

NEUROCOMPUTING (2020)

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Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

Xueyi Li et al.

CHINESE JOURNAL OF AERONAUTICS (2020)

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A Wasserstein gradient-penalty generative adversarial network with deep auto-encoder for bearing intelligent fault diagnosis

Xiong Xiong et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2020)

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A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network

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ISA TRANSACTIONS (2020)

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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)

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Article Automation & Control Systems

Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions

Ruonan Liu et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Review Biochemical Research Methods

Data augmentation for deep-learning-based electroencephalography

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JOURNAL OF NEUROSCIENCE METHODS (2020)

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Data processing and augmentation of acoustic array signals for fault detection with machine learning

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JOURNAL OF SOUND AND VIBRATION (2020)

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Data augmentation for automated pest classification in Mango farms

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An Intelligent Fault Diagnosis Method of Rolling Bearing Under Variable Working Loads Using 1-D Stacked Dilated Convolutional Neural Network

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SAR Target Detection Based on SSD With Data Augmentation and Transfer Learning

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An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems

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Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform

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COMPUTERS IN INDUSTRY (2019)

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Generative adversarial networks for data augmentation in machine fault diagnosis

Siyu Shao et al.

COMPUTERS IN INDUSTRY (2019)

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Article Engineering, Multidisciplinary

Fault diagnosis of rolling bearing under fluctuating speed and variable load based on TCO Spectrum and Stacking Auto-encoder

Zhou Xiang et al.

MEASUREMENT (2019)

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Article Engineering, Electrical & Electronic

Convolutional Neural Network-Based Moving Ground Target Classification Using Raw Seismic Waveforms as Input

Yan Wang et al.

IEEE SENSORS JOURNAL (2019)

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Article Engineering, Mechanical

Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions

Sai Ma et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

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A survey on Deep Learning based bearing fault diagnosis

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NEUROCOMPUTING (2019)

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LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification

Jun Pan et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

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Proceedings Paper Computer Science, Artificial Intelligence

A New Strategy for Rotating Machinery Fault Diagnosis under Varying Speed Conditions Based on Deep Neural Networks and Order Tracking

Meng Rao et al.

2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) (2018)

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Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform

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A multi-branch redundant adversarial net for intelligent fault diagnosis of multiple components under drastically variable speeds

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