A Multichannel Data Fusion Method Based on Multiple Deep Belief Networks for Intelligent Fault Diagnosis of Main Reducer

Computational Intelligence and Neuroscience

2022

Self Cite

Aiming at the existing problems in machinery monitoring data such as high cost of labeling and lack of typical failure samples, this paper launches a research on the semi-supervised-style intelligent fault diagnosis. Taking a great mount of unlabeled data and only a small quantity of labeled data as inputs, a novel fault diagnosis framework based on adversarial generating module and semi-supervised convolutional neural network (SSCNN) is proposed. Firstly, a semi-supervised learning module based on manifold-regularization-based fuzzy clustering discrimination (MRFCD) is proposed to make full use of the valuable fault-related information contained in unlabeled data. Secondly, MRFCD was introduced into CNN to construct pseudo-labels and estimate the objective function of unlabeled data. Then, the semi-supervised deep-learning-module-based MRFCD-SSCNN is established. Thirdly, to enhance the effect of MRFCD-SSCNN, generative adversarial network (GAN) was utilized to increase the size of training data under failure conditions. The framework based on GAN-MRFCD-SSCNN is proposed to achieve semi-supervised style intelligent fault diagnosis. To verify the performance of the diagnostic framework, vibrational signals of main reducer collected from actual test rig are employed. The comparative results confirm that the proposed framework outperforms some classical semi-supervised diagnostic models and achieves the accuracy of 96.2% using only 400 labeled samples.

Section: Introductionmentioning

confidence: 99%

An Intelligent Fault Diagnosis Based on Adversarial Generating Module and Semi-supervised Convolutional Neural Network

Computational Intelligence and Neuroscience

2022

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“…As the key and principal component of the automobile transmission system, rear axle is of great significance to driving security and comfort of automobile. According to the previous investigation and experience of experts, the faults of the automobile rear axle mainly occur in the main reducer which is the core component of rear axle [1][2][3]. erefore, condition monitoring and fault identification of main reducer can effectively identify various failure types and implement maintenances in time to avoid catastrophic accidents [4].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Deep Feature Learning Model Based on Parallel Convolutional Autoencoder for Intelligent Fault Diagnosis of Main Reducer

Computational Intelligence and Neuroscience

2021

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Traditional diagnostic framework consists of three parts: data acquisition, feature generation, and fault classification. However, manual feature extraction utilized signal processing technologies heavily depending on subjectivity and prior knowledge which affect the effectiveness and efficiency. To tackle these problems, an unsupervised deep feature learning model based on parallel convolutional autoencoder (PCAE) is proposed and applied in the stage of feature generation of diagnostic framework. Firstly, raw vibration signals are normalized and segmented into sample set by sliding window. Secondly, deep features are, respectively, extracted from reshaped form of raw sample set and spectrogram in time-frequency domain by two parallel unsupervised feature learning branches based on convolutional autoencoder (CAE). During the training process, dropout regularization and batch normalization are utilized to prevent over fitting. Finally, extracted representative features are feed into the classification model based on deep structure of neural network (DNN) with softmax. The effectiveness of the proposed approach is evaluated in fault diagnosis of automobile main reducer. The results produced in contrastive analysis demonstrate that the diagnostic framework based on parallel unsupervised feature learning and deep structure of classification can effectively enhance the robustness and enhance the identification accuracy of operation conditions by nearly 8%.

“…Diagnosing fault using a single-channel vibration signal may cause the decrease of diagnostic accuracy due to the direction of sensor and installation position [ 13 ]. Array signal processing is extensively applied in sonar, wireless communications, medical diagnosis and engineering applications [ 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals

Shaohu

2020

Sensors

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Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.