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

Circuit breakers are important control and protection equipment in the power system. The losses caused by their faults, such as damage to other power equipment and power outages in the power system, far exceed the value of the circuit breakers themselves. Statistics show that 10% of circuit breaker failures are caused by incorrect maintenance. The complete disassembly of circuit breakers during major repairs is time-consuming and costly, reaching one-third to one-half of the entire circuit breaker. Moreover, disassembly and reassembly can cause many defects, and the resulting accident examples are countless. Therefore, conducting research on voiceprint monitoring and diagnosis technology for mechanical faults of 10kV circuit breakers has important practical value.

Research on Voiceprint Monitoring And Diagnosis Technology for Mechanical Faults of 10kV Circuit Breakers

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

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