Fault diagnosis of the bushing infrared images based on mask R‐CNN and improved PCNN joint algorithm

Jiang, Jun; Bie, Yifan; Li, Jiansheng; Yang, Xiaoping; Guo, Ma; Lu, Yuncai; Zhang, Chaohai

doi:10.1049/hve.2019.0249

Cited by 42 publications

(32 citation statements)

References 25 publications

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“…Unlike faster R‐CNN, which has two outputs that is, class label ( L cls ) and bounding‐box offset ( L box ) for each candidate object, the mask‐RCNN has an additional branch that gives the object mask ( L mask ) as the output, which requires much finer spatial layout of an object. The advantage of mask‐RCNN is that training is simple and easy, and it is a very efficient algorithm [14]. So, mask‐RCCN is used in this work.…”

Section: Image Pre‐processingmentioning

confidence: 99%

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Convolutional neural network and Bi‐directional long short memory hybrid deep network aided infrared image classification framework for non‐contact monitoring of overhead insulators

Das

Deb

Chatterjee

et al. 2022

IET Signal Processing

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In this paper, a novel vision-based automated framework for estimation of pollution severity of outdoor insulators is proposed. The correct estimation of pollution severity is important to prevent the premature flashover of insulators. Existing methods to determine the degree of contamination of insulator surface requires direct contact with the insulator, which is practically problematic. Considering the aforesaid fact, in this article, a novel infrared thermal (IRT) image-based automated framework is proposed for noncontact monitoring the surface condition of outdoor insulators. A large number of IRT images corresponding to different contamination levels were captured from several porcelain disc insulators using a thermal camera. The captured IRT images were initially segmented using mask region-based convolutional neural network to remove the effect of background. Then, a hybrid deep learning network consisting of convolutional neural network and Bi-directional long short memory (CNN-BiLSTM) is designed for automated classification of IRT images. It has been observed that the proposed network has achieved an accuracy of 98.86%, specificity of 99.72%, precision of 98.86% and F-1 score of 98.86%, respectively. A comparative study with other deep learning models indicated that the proposed CNN-BiLSTM network delivered better performance. Hence, the proposed framework can be used in real-life for non-contact condition monitoring of insulators.

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Section: Image Pre‐processingmentioning

confidence: 99%

“…The mask-RCNN generates a high-quality segmentation mask at every instance to detect objects in an image. The mask-RCNN is developed as an extended version of faster RCNN [14]. The image workflow of mask-RCNN is shown in Figure 6.…”

Section: Mask-rcnnmentioning

confidence: 99%

Convolutional neural network and Bi‐directional long short memory hybrid deep network aided infrared image classification framework for non‐contact monitoring of overhead insulators

Das

Deb

Chatterjee

et al. 2022

IET Signal Processing

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“…Jiang et al used the Mask R-CNN framework to build a target detection system, which can accurately extract the bushing frame. e segmentation performance of the faulty area is improved by combining it with a pulse-coupled neural network based on linear iterative clustering [10]. Yan e network first uses the encoderdecoder architecture method to get the fused image and then uses Mask R-CNN for instance segmentation [11].…”

Section: Introductionmentioning

confidence: 99%

“…Jiang et al used the Mask R-CNN framework to build a target detection system, which can accurately extract the bushing frame. The segmentation performance of the faulty area is improved by combining it with a pulse-coupled neural network based on linear iterative clustering [ 10 ]. Yan et al established a multispectral instance segmentation network model based on Mask R-CNN and compared the fusion abilities of different fusion methods in detail [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

MSIS: Multispectral Instance Segmentation Method for Power Equipment

Shu

2022

Computational Intelligence and Neuroscience

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Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current method still cannot complete the detection and segmentation of the power equipment well. To better segment the power equipment in the infrared image, in this paper, a multispectral instance segmentation (MSIS) based on SOLOv2 is designed, which is an end-to-end and single-stage network. First, we provide a novel structure of multispectral feature extraction, which can simultaneously obtain rich features in visible images and infrared images. Secondly, a module of feature fusion (MARFN) has been constructed to fully obtain fusion features. Finally, the combination of multispectral feature extraction, the module of feature fusion (MARFN), and instance segmentation (SOLOv2) realize multispectral instance segmentation of power equipment. The experimental results show that the proposed MSIS model has an excellent performance in the instance segmentation of power equipment. The MSIS based on ResNet-50 has 40.06% AP.

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“…Fault diagnosis technology is a comprehensive technology, which involves modern control theory, fuzzy set theory, reliability theory, and signal processing [12]. As for the fault diagnosis of the suspension system, it is mainly realized by the state observer.…”

Section: Introductionmentioning

confidence: 99%

Active Fault‐Tolerant Control Based on the Fault of Electromagnetic Hybrid Active Suspension

Kou

Gao

et al. 2021

Shock and Vibration

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In order to improve the ride comfort and handling stability of the vehicle and realize the recovery of vibration energy, an electromagnetic linear hybrid suspension actuator composed of linear motor and solenoid valve shock absorber is proposed. At the same time, a fault diagnosis and fault-tolerant control strategy is designed to solve the system instability caused by the fault of electromagnetic hybrid active suspension. The 1/4 vehicle two-degree-of freedom suspension model, the linear motor mathematical model, and the solenoid valve shock absorber test model are established. In this paper, the fuzzy sliding mode controller is used as the controller and the unknown input observer is used to estimate the state of the suspension. According to the residual obtained from the unknown input observer and compared with the residual threshold, the suspension fault is determined. In the case of fault, the fuzzy sliding mode controller is used to compensate the force and realize the suspension fault-tolerant control. The performance of the suspension is simulated on random road and bumped road, respectively. The simulation results show that the fault-tolerant control effect of the three performance indexes of the suspension is good, and the ride comfort and safety of the suspension are improved. Finally, the bench test is carried out, and the test results show that in the fault-tolerant control state, the root mean square value of the sprung mass acceleration is reduced by 31.69% compared with the fault state and the dynamic performance of the suspension is improved.

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Fault diagnosis of the bushing infrared images based on mask R‐CNN and improved PCNN joint algorithm

Cited by 42 publications

References 25 publications

Convolutional neural network and Bi‐directional long short memory hybrid deep network aided infrared image classification framework for non‐contact monitoring of overhead insulators

Convolutional neural network and Bi‐directional long short memory hybrid deep network aided infrared image classification framework for non‐contact monitoring of overhead insulators

MSIS: Multispectral Instance Segmentation Method for Power Equipment

Active Fault‐Tolerant Control Based on the Fault of Electromagnetic Hybrid Active Suspension

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