Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

Dong, Fangmin; Yu, Xiao; Ding, Enjie; Wu, Shoupeng; Fan, Chun‐Yang; Huang, Yanqiu

doi:10.1155/2018/5063527

Cited by 8 publications

(3 citation statements)

References 77 publications

(115 reference statements)

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“…To validate the efficacy and generalizability of the proposed methods, this study carries out cross-domain fault diagnosis experiments on two types of bearing fault datasets (Case Western Reserve University (CWRU) experimental platform [2], [14], [34], [35], [48] and SQI-MFS test platform [49], [50] , [51]) under balanced and unbalanced data samples. Furthermore, to demonstrate the superiority of the proposed DTCNN-SJM, some models are built by ready-made various established techniques for comparison.…”

Section: Experimental Validationmentioning

confidence: 99%

A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching

Liu,

Dong,

et al. 2024

IEEE Photonics J.

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In practical industrial environment, variable working condition can result in shifts in data distributions, and the labeled fault data in various working conditions is difficult to collect because rotating machines often works in normal status, and the insufficient labeled fault data brings data samples imbalance and performance degradation of intelligent fault diagnosis model. To overcome these problems, by integrating the superiority of deep learning method and feature-based transfer learning method, this work proposes an innovative cross-domain fault diagnosis framework based on deep transfer convolutional neural network and supervised joint matching. Firstly, the continue wavelet transform is used to process original bearing vibration signals and extract time-frequency images. Secondly, a deep transfer convolutional neural network is built by the way of fine-tuning, and the trained network is used to extract deep features from different domains. Thirdly, a new domain adaptation approach, supervised joint matching, is developed to conduct joint feature distribution matching and instance reweighting with the consideration of maximum marginal criterion. The intelligent bearing fault diagnosis model is then trained to predict the labels of the target domain's feature data. To verify the performance of the proposed approaches, this study uses two distinct datasets pertaining to bearing defects for conducting cross-domain fault diagnosis in the presence of balanced and imbalanced data. The experimental analysis indicates that the designed methods can achieve desirable diagnostic accuracy and possess robust generalization ability.

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Section: Experimental Validationmentioning

confidence: 99%

A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching

Liu,

Dong,

et al. 2024

IEEE Photonics J.

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“…This paper uses the open motor bearing data set provided by the laboratory of Western Reserve University as a training set, which includes motor normal bearing data, drive end bearing fault data and fan end bearing fault data [11]. In the bearing fault data, it includes rolling fault, inner ring fault and outer ring fault.…”

Section: Processing Of Experimental Data Setsmentioning

confidence: 99%

Deep Convolution Neural Network Motor Fault Identification Based on Generative Adversarial Network under Unbalanced Sample

Li¹,

Jiao²,

Lin³

2020

dtetr

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Based on the problem that the traditional motor fault diagnosis method relies on the signal processing power and the model generalization ability is poor, this paper proposes a fault diagnosis method based on generative adversarial network under unbalanced data sets. It builds a small sample training set to train generative adversarial network, and adds the generated sample to the original small sample training set. A deep convolutional neural network (DCNN) model that is suitable for motor fault diagnosis is proposed, and the fault characteristics are learned from the original data layer by layer, so as to realize the identification of different faults. After a lot of experimental analysis, the method is better than the existing depth model in terms of detection rate and error rate.

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“…Convolutional neural networks (CNNs) represent the essential building blocks in 2D pattern analytics. Sensorbased applications such as mechanical fault diagnosis [2], [3], structural health monitoring [4], human activity recognition (HAR) [5], hazardous gas detection [6] have been powered by CNN models in industry and academia. CNN-based models, as one of the main types of artificial neural networks (ANNs), have been widely used in sensor analytics with automatic learning from sensor data [7], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

CNN Sensor Analytics With Hybrid-Float6 Quantization on Low-Power Embedded FPGAs

et al. 2023

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The use of artificial intelligence (AI) in sensor analytics is entering a new era based on the use of ubiquitous embedded connected devices. This transformation requires the adoption of design techniques that reconcile accurate results with sustainable system architectures. As such, improving the efficiency of AI hardware engines as well as backward compatibility must be considered. In this paper, we present the Hybrid-Float6 (HF6) quantization and its dedicated hardware design. We propose an optimized multiply-accumulate (MAC) hardware by reducing the mantissa multiplication to a multiplexor-adder operation. We exploit the intrinsic error tolerance of neural networks to further reduce the hardware design with approximation. To preserve model accuracy, we present a quantization-aware training (QAT) method, which in some cases improves accuracy. We demonstrate this concept in 2D convolution layers. We present a lightweight tensor processor (TP) implementing a pipelined vector dot-product. For compatibility and portability, the 6-bit floating-point (FP) is wrapped in the standard FP format, which is automatically extracted by the proposed hardware. The hardware/software architecture is compatible with TensorFlow (TF) Lite. We evaluate the applicability of our approach with a CNN-regression model for anomaly localization in a structural health monitoring (SHM) application based on acoustic emission (AE). The embedded hardware/software framework is demonstrated on XC7Z007S as the smallest Zynq-7000 SoC. The proposed implementation achieves a peak power efficiency and run-time acceleration of 5.7 GFLOPS/s/W and 48.3×, respectively.

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Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

Cited by 8 publications

References 77 publications

A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching

A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching

Deep Convolution Neural Network Motor Fault Identification Based on Generative Adversarial Network under Unbalanced Sample

CNN Sensor Analytics With Hybrid-Float6 Quantization on Low-Power Embedded FPGAs

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