Active learning for new-fault class sample recovery in electrical submersible pump fault diagnosis

Silva, Luciano Henrique Peixoto da; Mello, Lucas Henrique Sousa; Rodrigues, Alexandre; Varejão, Flávio Miguel; Ribeiro, Marcos Pellegrini; Oliveira-Santos, Thiago

doi:10.1016/j.eswa.2022.118508

Cited by 8 publications

(1 citation statement)

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“…Lan et al [12] proposed a fault isolation method based on a finite state machine and a fault prediction method based on an optimized extreme learning machine (OELM), which reduces the number of potential failures and predicts the remaining service life of a device by analyzing and identifying fault data and their redundancy relationships. Silva et al [13] used uncertainty-based active learning to assist with feature extraction and further applied this method to fault diagnosis technology based on statistical analysis.…”

Section: Fault Diagnosismentioning

confidence: 99%

Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

Yang,

Long,

Lin

et al. 2023

Computers, Materials &Amp; Continua

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With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the terms of the loss function. It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model, especially for fault categories with high similarity. Then, the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweight model for edge-side deployment. We propose a two-stage edge-side fault diagnosis method (TSM) that separates fault detection and fault diagnosis into different stages: in the first stage, a fault detection model based on a denoising auto-encoder (DAE) is adopted to achieve fast fault responses; in the second stage, a diverse convolution model with variance weighting (DCMVW) is used to diagnose faults in detail, extracting features from micro and macro perspectives. Through comparison experiments conducted on two fault datasets, it is proven that the proposed method has high accuracy, low delays, and small computation, which is suitable for intelligent edge-side fault diagnosis. In addition, experiments show that our approach has a smooth training process and good balance.

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Section: Fault Diagnosismentioning

confidence: 99%