Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic

Abstract

With the rapid development of industrial informatization and deep learning technology, modern data-driven fault diagnosis (MIFD) methods based on deep learning have been continuously emphasized by the industry. However, most of these methods require sufficient training samples to achieve the desired diagnostic effect, but the scarcity of fault samples in the actual industrial environment leads to the limited development of MIFD methods. In addition, due to the changes of equipment operating conditions and production requirements, data-driven fault diagnosis methods often need to face the cross domain problem of cross load or even cross different equipment. In this paper, a parameter optimization and feature metric-based fault diagnosis method with few samples, called model agnostic matching network model, is designed for the problem of sparse fault samples and cross-domain between data sets in real industrial environments. The method combines both a parameter-based optimization meta-learning network, which extracts optimization information adapted to different domains, and a metric-based meta-learning network, which extracts metric information for similarity discriminations. The experimental result show that the method outperforms the current baseline method for the 5-shot fault diagnosis problem of rolling bearings under limited data conditions and achieves an accuracy of up to 94.4% in cross-equipment diagnosis experiments from rolling bearings to gas regulators, indicating the feasibility of the method. The features are visualized by T-SNE to show the validity of the model.

Investigating the effect of intelligent assistance systems on motivational work characteristics in assembly

“…Fourth, we explicitly omitted sources of interference in the representations of the assembly workplace with IAS to investigate its pure effect on MWC. However, interferences, such as time delays or modeling errors (Tao et al, 2022 ; Xu et al, 2021 ), can be expected to occur in operational practice in assembly process planning (Qian et al, 2023 ) when working with IAS, which could alter the experiences of workers with such systems. Depending on whether assembly workers need to fix certain malfunctions on their own, knowledge characteristics could increase by using IAS, for example, because programming skills are needed to fix interferences but are not necessary for the majority of daily assembly processes.…”

“…In this way, the EVM study counteracts assembly process- and organization-specific effects, such as an organization’s technology-averse tendency. Third, the EVM study design allows the pure investigation of the effect of the IAS on MWC without the impact of additional interferences, such as time delays or incorrect modeling which could occur in occupational practice (Tao et al, 2022 ; Xu et al, 2021 ).…”

A high-performance framework for personal protective equipment detection on the offshore drilling platform

“…In order to determine whether workers are wearing helmets and workwear correctly, we propose a method for constructing Fig. 16 The implementation diagram of the proposed method on an offshore drilling platform. The bottom layer is the surveillance camera, which is responsible for collecting the site images; the middle layer is the server, where the model is deployed on each server and is responsible for completing the PPE detection; the top layer is the client, where the identification results of each server are aggregated to the client, which is responsible for visualizing the identification results and generating alert messages position features based on object bounding boxes.…”

“…With the continuous progress of information technology and industrial technology, modern industrial production is developing in the direction of high speed, precision and intelligence, and data-driven [16,17] abnormal event diagnosis methods are gaining more and more attention and development, including the application of visual data for PPE detection. The vision-based methods can be divided into two categories: one is the traditional method of image processing combined with machine learning [5][6][7][18][19][20]; the other is using deep learning technology, e.g., object detection [9][10][11][12][21][22][23][24][25][26][27][28].…”

Robust fault diagnosis of a high-voltage circuit breaker via an ensemble echo state network with evidence fusion

“…For fault diagnosis, a robust and high-accuracy classifier is required to distinguish the type of fault. For example, Tao et al [23] proposed a feature metric-based fault diagnosis approach under limited data conditions. In the study, a parametric optimization-based meta-learning network and a metric learning network were combined to extract optimization information to adapt between different domains and metric information for similarity discrimination, respectively.…”