Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information

Long, Zhuo; Zhang, Xiaofei; Zhang, Li; Qin, Guanshi; Huang, Shoudao; Song, Derui; Shao, Haidong; Wu, Gongping

doi:10.1016/j.measurement.2020.108718

Cited by 77 publications

(28 citation statements)

References 30 publications

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“…Automatic detection systems are expected to identify different pavement cracks quickly under various conditions, even under adverse weather conditions [ 15 ]. However, because a single sensor cannot handle the challenging conditions, and a basic detection model also cannot handle the redundant data from several sensors, it raises growing challenges for defect detection modeling [ 16 ]. To this end, data acquisition requires the selection of appropriate sensors to quantify the pavement damage.…”

Section: Introductionmentioning

confidence: 99%

Automatic Pavement Defect Detection and Classification Using RGB-Thermal Images Based on Hierarchical Residual Attention Network

Chen

Chandra

Seo

2022

Sensors

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A convolutional neural network based on an improved residual structure is proposed to implement a lightweight classification model for the recognition of complex pavement conditions, which uses RGB-thermal as input and embeds an attention module to adjust the spatial, as well as channel, information of the images. The best prediction accuracy of the proposed model is 98.88%, while the RGB-thermal is used as input and an attention mechanism is used. The attention mechanism increases the attention to detail of the image and regulates the use of image channels, which enhances the final performance of the model. It is also compared with state-of-the-art (SOTA) deep learning models, indicating our model has fewer parameters, shorter training time, and higher recognition accuracy compared to existing image classification models. A visualization method incorporating gradient-weighted class activation mapping (Grad-CAM) is proposed to analyze the classification results, comparing the data the model learns from the images under different input data.

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Section: Introductionmentioning

confidence: 99%

Automatic Pavement Defect Detection and Classification Using RGB-Thermal Images Based on Hierarchical Residual Attention Network

Chen

Chandra

Seo

2022

Sensors

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“…A comparison of SVM and CatBoost classifiers was performed by Gareev et al 51 to diagnose mechanical faults and they conclude that SVM gives lower accuracy (85.3%) while CatBoost gives higher classification accuracy up to 99.3%. Long et al 52 has used an improved AdaBoost classifier fed with multi-sensors data for motor fault diagnosis and has achieved a classification accuracy of 92.38%. The multi-sensory data collection setup has a high cost and AdaBoost performance was not satisfactory.…”

Section: Performance Comparisonmentioning

confidence: 99%

Condition monitoring of water pump bearings using ensemble classifier

Irfan

Althobiani

Alwadie

et al. 2022

Advances in Mechanical Engineering

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The bearings faults are reported to be the major reason for centrifugal pump (CPs) failures. Limited literature is available to diagnose the minor scratches in the bearing surface through non-intrusive condition monitoring techniques. Recent research on the analysis of bearing scratches through non-intrusive motor current analysis (MCA) has shown encouraging results where the comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% through MCA combination with machine learning and CNN algorithm was reported which is very low. The key factors of low accuracies were identified as low amplitudes of the harmonics in the MCA spectrum, the magnitude of environmental noise, and utilization of conventional feature extraction techniques. This problem has been tackled in this paper by developing a novel feature extractor (NFE) that extracts powerful features from the integrated current and voltage sensors data. The NFE has been derived using the threshold-based decision mechanism which has the capability to identify the location of the feature harmonic, feature extraction, measure the amplitude of the fault component, and compare it with the derived threshold. The experimental data has been collected for the bearing balls (BB), bearing cage (BC), inner race (IR) and the outer race (OR) faults, and the performance of the NFE has been tested on an ensemble classifier (CatBoost) and the better classification accuracy (99.2% for an individual feature and 100% with the combination of two or more features) of NFE has been achieved as compared to previously reported methods.

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“…Attention mechanism is a resource allocation scheme, which can strengthen the ability of the model to focus on more critical information [35][36][37][38][39][40][41][42]. The principle of attention mechanism used in the paper is shown in Fig.…”

Section: Attention Mechanism In the Clstm Modelmentioning

confidence: 99%

A multi-task prediction method for acid concentration based on attention-CLSTM

Zhu

Liu

Cao

et al. 2022

Preprint

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The accurate measurement of acid concentration, including hydrogen chloride (HCl) and ferrous chloride concentrations (FeCl2), is a critical part of ensuring the quality of strip steel pickling. In this study, a multi-task attention convolutional long short-term memory (MACL) neural network model was proposed to predict hydrogen ion and ferrous ion concentrations simultaneously. Firstly, in order to extract significant information from the input sequence, an attention mechanism was added to the model to calculate the correlation between the input features and the acid concentration at each time step. Next, multi-task learning explores the connections between the two tasks and exploits hidden constraints to improve prediction accuracy. Finally, deep features were extracted through convolutional long short-term memory (CLSTM) neural network, and the acid concentration was predicted. The proposed MACL model was compared with other popular prediction models. The experimental results show that proposed MACL model generally outperforms other models, indicating that proposed model has excellent predictive performance and effectiveness.

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Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information

Cited by 77 publications

References 30 publications

Automatic Pavement Defect Detection and Classification Using RGB-Thermal Images Based on Hierarchical Residual Attention Network

Automatic Pavement Defect Detection and Classification Using RGB-Thermal Images Based on Hierarchical Residual Attention Network

Condition monitoring of water pump bearings using ensemble classifier

A multi-task prediction method for acid concentration based on attention-CLSTM

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