In the event of mechanical equipment failure, the fault may not belong to any known category, and existing deep learning methods often misclassify such faults into a known class, leading to erroneous fault diagnosis. In order to address the challenge of identifying new types of faults in mechanical equipment fault diagnosis, this paper proposes a novelty detection and fault diagnosis method for bearing faults based on a hybrid deep autoencoder network. Firstly, a hybrid deep autoencoder network with one input and two outputs was constructed. The original data were then fed into the network to obtain its low-dimensional representation and reconstructed data. By setting a threshold based on the reconstruction error, novel class faults can be detected, while known faults can be classified based on low-dimensional features. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 98.59% (100%) for novel class identification (known fault classification) on the CWRU bearing dataset, 96.79% (98.53%) on the Paderborn dataset, and 84.34% (97.03%) on the MFPT dataset. Therefore, the hybrid deep autoencoder network not only accurately detects unknown types of faults but also effectively classifies known fault types, demonstrating excellent fault identification and classification capabilities.

/pdf/novelty-detection-and-fault-diagnosis-method-for-bearing-1ylefg6o.pdf

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network


 The mechanical fault of gas-insulated switchgear (GIS) seriously threatens the security of the power grid. Recently, acoustic-based fault diagnosis methods, which have the advantage of non-contact measurement, have been applied to the GIS mechanical fault diagnosis, but vulnerable to the interference of the background noise. To improve the capacity of the acoustic-based GIS fault diagnosis under noise background, by simulating the sound feature extraction ability and anti-noise ability of human auditory system, a novel GIS mechanical fault diagnosis method based on saliency feature of auditory brainstem response (SFABR) is proposed. First, an auditory saliency model, which considers both the auditory periphery and the auditory nerve center was constructed by combining the deep auditory model and the saliency model. After processing GIS emitted acoustic signal, the auditory brainstem response (ABR) was obtained, and the saliency features of the ABR were extracted to obtain the SFABR. Then, the characteristic frequency distribution of the auditory saliency model was adjusted to make it more suitable for the spectral characteristics of the GIS sound signal. Finally, the SFABR was mapped to a two-dimensional CNN to train a model for GIS mechanical fault diagnosis. This method simulates the process of auditory response extraction and the selection effect of auditory attention on sound elements. 110kV three-phase GIS fault simulation experiment shows that for GIS mechanical faults, the diagnosis method based on SFABR can obtain 96.1% fault identification accuracy. In different noise environments, compared with the traditional acoustic-based fault diagnosis methods, this method has stronger anti-noise performance, and can more effectively realize the identification of GIS mechanical failure types. In future research, the method can be further extended to fault diagnosis of more types of power equipment.

Mechanical fault diagnosis of Gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

Animals control is an important task for the safe operation of substations. Aiming at the problems existing in the control of small animals, with the superior prediction ability of machine learning, a prediction model of small animals hazard grade is proposed, which combines gradient boosting decision (GBDT) and logistic regression (LR) algorithm. The model combined substation operation and maintenance data with local meteorological data, performs features screening by calculating the variance value, and achieves classes balance by using sampling technology. And finally the model achieves the prediction of small animals hazard grade in substation. By using different data sets and not using GBDT algorithm to train the model, the prediction results are compared and analyzed. The proposed model is better in all prediction performance indicators, which verifies the validity of the method. 

Application of Small Animals Control in Substation Based on GBDT and LR Fusion Algorithm

Animals control is an important task for the safe operation of substations. Aiming at the problems existing in the control of small animals, with the superior prediction ability of machine learning, a prediction model of small animals hazard grade is proposed, which combines gradient boosting decision (GBDT) and logistic regression (LR) algorithm. The model combined substation operation and maintenance data with local meteorological data, performs features screening by calculating the variance value, and achieves classes balance by using sampling technology. And finally the model achieves the prediction of small animals hazard grade in substation. By using different data sets and not using GBDT algorithm to train the model, the prediction results are compared and analyzed. The proposed model is better in all prediction performance indicators, which verifies the validity of the method.

Aiming at the problems such as few defect types and no unified detection parameters, a transmission line defect detection method based on deep learning is proposed in this paper. Firstly, the power transmission line defect identification data set is established. Secondly, in order to further reduce the impact of background and lighting factors on the identification accuracy, a relevant data set is defined, and an end-to-end deep learning algorithm with high identification accuracy based on region-based convolutional neural networks (R-CNN) is proposed for establishing a detection model. Finally, experimental results show that the proposed detection method has a better detection performance.

A UAV-Based Transmission Line Defect Detection Method

The safe running of substations is an important part of power system security, as well as an important work of the power companies. The intrusion of kites, plastic trash, small animals and other objects will affect the safe running of substations. With the development of video monitoring technology, intelligent recognition technology of images and videos, video monitoring has gradually become the main monitoring means of unattended substations. This paper firstly analyses the research status and main technologies of video object recognition. To achieve the detection of foreign objects in substations, moving object detection and video object detection technologies are studied. And two outlines are proposed: using background subtraction and image classification, using YOLOv3 to detect objects in videos. In the end of the paper, YOLOv3 was tested by training a detector of small animals. The results show that the value of mAP equals to 0.7169 and the detection speed fulfills the request of real-time detection. This method could serve as reference for detecting and tracking intrusion objects in substations.

Research on Video Detection of Object Intrusion in Substation

To reduce the false alarm rate of voiceprint recognition of electrical faults under noise interference, a two-stage algorithm was proposed in this paper. Energy storage motors of circuit breakers were taken as the research object. The sound of normal motors and motors with different kinds of abnormalities was captured. After preprocessing like framing and windowing, the Mel frequency cepstral coefficient (MFCC) were extracted as feature vectors. Then, what was different from normal was that we first fed the feature vectors into a novelty detection algorithm, one-class SVM. Only samples that were familiar to one-class support vector machine (SVM) would be delivered to the second algorithm, C-SVM. The second algorithm classified sound signals into different categories. The experiment results show that the two-stage combination can effectively reduce the false alarm rate in noisy environment and the recall rate of faults only slightly decreased when there is little noise. Specifically speaking, it reduced the false alarm rate by 63.47% when the signal-to-noise ratio (SNR) is –20dB and the recall of faults only dropped by 0.33% when SNR is 20dB.

Research on Voiceprint Recognition of Electrical Faults With Lower False Alarm Rate

In order to better improve the accuracy of transmission line defect detection and ensure the safety of transmission line operation, an optimization algorithm of transmission line defect detection and identification based on embedded AI is proposed. Firstly, based on embedded AI, the target characteristics of transmission line defects are identified, and based on the identification results, the defect area location processing is carried out, the transmission line defect detection and identification algorithm is improved, and the evaluation index of transmission line safety level is constructed, Simplify the transmission line defect detection process. Finally, the application proves that the transmission line defect detection and identification optimization algorithm based on embedded AI has high practicability and fully meets the research requirements.

Optimization Algorithm of Transmission Line Defect Detection and Identification Based on Embedded AI

Mechanical fault is a common fault of gas insulated switchgear (GIS). If not found in time, it will cause major safety hazards such as opening and closing fault. In this paper, a diagnosis method based on improved Mel cepstrum coefficient for GIS mechanical fault on-line monitoring is proposed. Firstly, the Mel frequency cepstral coefficients (MFCC) are extracted from the preprocessed sound signals; in order to adapt to the characteristics of smooth sound energy change in GIS operation, the MFCC is optimized to obtain improved features; Support Vector Machine (SVM) is introduced to build a GIS mechanical fault diagnosis model based on acoustics and uses bagging algorithm to integrate the SVM model. In this study, mechanical fault is simulated on real GIS to obtain real fault sound signals for training and testing. The experimental results show that compared with the traditional MFCC, the improved MFCC has a higher recognition accuracy in GIS fault sound recognition system. The F1-score is generally over 80%, and the F1-score of specific mechanisms can even reach 99%.

https://iopscience.iop.org/article/10.1088/1742-6596/1659/1/012055/pdf

GIS Mechanical Fault Diagnosis Method Based on Middle Time Mel Cepstrum Coefficient

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Papers

Research on Video Detection of Object Intrusion in Substation

Research on Voiceprint Recognition of Electrical Faults With Lower False Alarm Rate

Optimization Algorithm of Transmission Line Defect Detection and Identification Based on Embedded AI

GIS Mechanical Fault Diagnosis Method Based on Middle Time Mel Cepstrum Coefficient