Multisensor-Driven Motor Fault Diagnosis Method Based on Visual Features

Tang, Yao; Zhang, Xiaofei; Qin, Guanshi; He, Yunze; Xie, Jinping; Zhou, Junhong; Long, Zhuo

doi:10.1109/tii.2022.3201011

Cited by 13 publications

(4 citation statements)

References 32 publications

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“…With the development of data acquisition technology, the arrangement of multi-channel sensors to obtain equipment operation data has become a common method of condition monitoring [34]. Multidimensional information provides a more comprehensive description of machine health states, for example, different measurement points respond differently to the same machine health states, and different information can complement each other to help obtain more discriminating features [35]. As a powerful tool for multidimensional data analysis, graph convolution has demonstrated its effectiveness in classification tasks in various research areas.…”

Section: Graph Convolution Modulementioning

confidence: 99%

A dual-view network for fault diagnosis in rotating machinery using unbalanced data

Chen¹,

Yu²,

Kong³

et al. 2023

Meas. Sci. Technol.

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Data-driven intelligent methods have demonstrated their effectiveness in the area of fault diagnosis. However, most existing studies are based on the assumption that the distributions of normal and faulty samples are balanced during the diagnostic process. This assumption significantly decreases the application range of a diagnostic model as the samples in most real-world scenarios are highly unbalanced. To cope with the limitations caused by unbalanced data, this paper proposed an original dual-view network (DVN). Firstly, an interactive graph modeling strategy is introduced for relationship information modeling of multi-sensor data. Meanwhile, the graph convolution operation is used as the baseline for feature extraction of the constructed interactive graph to mine for fault representations. Secondly, an original dual-view classifier consisting of a binary classifier and a multi-class classifier is proposed, which divides fault diagnosis into two stages. Specifically, in the first stage, the binary classifier performs the binary inference from the view of fault detection. In the second stage, the multi-class classifier performs the full-state inference from the view of fine-grained fault classification. Then, based on the dual-view classifier, a weight activation module is designed to alleviate training bias toward majority classes by sample-level re-weighting. Finally, the diagnosis results can be obtained according to the output of the multi-class classifier. Fault diagnosis experiments using two different datasets with varying data unbalance ratios were conducted to validate the effectiveness of the proposed method. The superiority of the proposed DVN is verified through comparisons with state-of-the-art methods. The effectiveness of the DVN is further validated through ablation studies with some ablative models. The DVN code is available at: https://github.com/CQU-ZixuChen/DualViewNetwork.

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Section: Graph Convolution Modulementioning

confidence: 99%

A dual-view network for fault diagnosis in rotating machinery using unbalanced data

Chen¹,

Yu²,

Kong³

et al. 2023

Meas. Sci. Technol.

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“…The GAF converts time series data into 2D images [24,25]. Firstly, the time series X = {x 1 , x 2 , .…”

Section: Multi-signal Gramian Angular Fieldsmentioning

confidence: 99%

“…However, the above methods often have problems of low fault diagnosis accuracy and a wide range of applications, and the related research has the limitation of extracting the detailed features of the signals in a single dimension only. However, the motor operating state signals can be converted into two-dimensional or high-dimensional space to comprehensively display the implied multidimensional information through multi-dimensional data fusion and high-dimensional visual knowledge methods [24].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion

Wang,

Ma,

Meng

et al. 2023

Sensors

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Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing faults, and other faults arising from irregular vibrations and frequent start–brake cycles. While fault diagnosis for PMSMs offers an effective means to enhance operational efficiency, the multi-sensor information fusion is often overlooked. In industrial production processes, the collected data inevitably suffers from noise contamination, which can adversely impact diagnostic outcomes. To enhance the robustness of diagnostic methods in noisy environments and mitigate the risk of overfitting, a PMSM fault diagnosis method based on image features of multi-sensor fusion is proposed. Firstly, the vibration acceleration signals of the PMSM at different positions were acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method combines sensor signals from three different installation locations into a single image. Next, the multi-texture features are fused to extract the features of the image. Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. It has excellent performance even in noisy environments. The method is non-invasive and can be extended and applied to the condition monitoring and diagnosis of industrial motors.

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“…The grayscale image method was also adopted [39][40][41]. A color symmetrized dot pattern (CSDP) method [42] was newly designed to infuse three multisensor signals into images, handling multisensor information fusion problems. The CSDP method was proposed based on the conventional SDP.…”

Section: Time Series To Image Transformationmentioning

confidence: 99%

Intelligent Online Inspection of the Paste Quality of Prebaked Carbon Anodes Using an Anomaly Detection Algorithm

Li,

Yong

et al. 2023

Systems

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Prebaked carbon anodes are a critical consumable in the aluminum electrolysis industry. Prebaked carbon anode paste is the intermediate product of the prebaked carbon anode, and its quality significantly impacts the prebaked carbon anode. Therefore, inspecting the quality of the prebaked carbon anode paste is essential. Currently, the quality inspection of the paste still relies on laboratory analysis or manual experience. A laboratory inspection cannot obtain results in real time, while manual inspection poses potential risks. To address these issues, an online intelligent inspection method for prebaked carbon anode paste based on an anomaly detection algorithm was proposed. Firstly, we acquired the temperature of the paste and the power of the kneading motor. Secondly, we transformed these time-series data into images using the Gramian Angular Field (GAF) technique and joined them to create the paste anomaly detection dataset. Thirdly, we trained a matched anomaly detection model based on the PatchCore algorithm. Finally, we compared two advanced models: HaloAE and TSRD. PatchCore performs best on our dataset with an AUC-ROC score of 0.9943, followed by HaloAE (0.9906) and TSRD (0.9811). Our proposed method enables on-time intelligent inspection of prebaked carbon anode paste quality. This eliminates the need for manual inspection, reduces labor requirements, and ensures worker safety.

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Multisensor-Driven Motor Fault Diagnosis Method Based on Visual Features

Cited by 13 publications

References 32 publications

A dual-view network for fault diagnosis in rotating machinery using unbalanced data

A dual-view network for fault diagnosis in rotating machinery using unbalanced data

Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion

Intelligent Online Inspection of the Paste Quality of Prebaked Carbon Anodes Using an Anomaly Detection Algorithm

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