The advances of intelligent fault diagnosis in recent years show that deep learning has strong capability of automatic feature extraction and accurate identification for fault signals. Nevertheless, data scarcity and varying working conditions can degrade the performance of the model. More recently, a tool has been proposed to address the above challenges simultaneously. Meta-learning, also known as learning to learn, uses a small sample to quickly adapt to a new task. It has great application potential in few-shot and cross-domain fault diagnosis, and thus has become a promising tool. However, there is a lack of a survey to conclude existing work and look into the future. This paper comprehensively investigates deep meta-learning in fault diagnosis from three views: (i) what to use, (ii) how to use, and (iii) how to develop, i.e. algorithms, applications, and prospects. Algorithms are illustrated by optimization-, metric-, and model-based methods, the applications are concluded in few-shot cross-domain fault diagnosis, and open challenges, as well as prospects, are given to motivate the future work. Additionally, we demonstrate the performance of three approaches on two few-shot cross-domain tasks. Typical meta-learning methods are implemented and available at https://github.com/fyancy/MetaFD .

Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects

Fault diagnosis method for sucker rod well with few shots based on meta-transfer learning

Multi-objective optimization of seeding performance of a pneumatic precision seed metering device using integrated ANN-MOPSO approach

Hybrid modeling for submergence depth of the pumping well using stochastic configuration networks with random sampling

Cyber-attacks against cyber-physical power systems security: State estimation, attacks reconstruction and defense strategy

Online diagnosis for sucker rod pumping well has great significances for rapidly grasping operations of the oil well Feature extraction of the working condition and determination of the online diagnostic algorithm are two indispensable parts In this paper, five feature vectors are extracted using Freeman chain codes Then, an optimized density peak clustering (DPC) method is proposed to realize online diagnosis solved by an improved brain storm optimization (BSO) algorithm, in which the cloud model is adopted to generate new solutions in the searching space During the online diagnosis process, a new cluster updating strategy is used to update the cluster centers online According to the proposed online diagnostic method, various samples are automatically classified into different classifications by the unsupervised learning The simulation results verify that the proposed online diagnosis method is satisfactory, which can give a higher and more stable diagnostic accuracy

Online fault diagnosis for sucker rod pumping well by optimized density peak clustering.

The invention provides an oil field routing inspection fixed-point data acquisition system and method based on multiple unmanned aerial vehicles and relates to the technical field of oil field production. The method comprises the following steps: setting number m of unmanned aerial vehicles as well as task points and a task area in a well area needing to be subjected to routing inspection performed by each unmanned aerial vehicle, and performing routing inspection path planning by each unmanned aerial vehicle, thus obtaining an optimal path; performing fixed-point acquisition on distributed task points by all the unmanned aerial vehicles according to respective optimal paths; acquiring data and judging whether the data is valid data or not, acquiring the data again if the data is invalid,if the data is valid, after each unmanned aerial vehicle performs data acquisition on the task points, marking the task points and sharing the marked information to other unmanned aerial vehicles in collaborative operation, judging whether each unmanned aerial vehicle traverses all the set task points, if yes, marking the task points and sharing the marked information until all the unmanned aerialvehicles complete data acquisition on all the distributed task points, and if not, arriving at the next task point by each unmanned aerial vehicle for acquiring data. The system and method provided by the invention can improve oil field routing inspection efficiency and oil production security and management.

Oil field routing inspection fixed-point data acquisition system and method based on multiple unmanned aerial vehicles

A multiple-model-based on-line soft measurement method for the gas-oil ratio of a pumping well oil liquid comprises the following steps: a pumping well remote wireless data acquisition system is built, offline training is conducted according to historical production data, and a multiple-model-based soft measurement model for the gas-oil ratio of the pumping well oil liquid is built; an average up-stroke load, a down-stroke average load, a wellhead pressure, a pumping unit stroke, a stroke frequency, a oil liquid outlet quantity flowing out of the wellhead, a motor up-stroke average current anda motor down-stroke average current are newly acquired and introduced into an offline trained multi-model to obtain online gas-oil ratio data of the oil liquid, thereby realizing online soft measurement of the -gas-oil ratio of the pumping well oil liquid. The on-line continuous measurement of production data can be realized, the measurement cost is low, the real-time performance is high, the on-site construction risk is reduced, and the problems of data loss, and recording errors caused by manual recording can be avoided; and the calculation complexity is low, the prediction accuracy is high, and the problem of influence of noise contained in the collected data sample on the prediction precision can be effectively solved.

Multiple-model-based on-line soft measurement method for gas-oil ratio of pumping well oil liquid

Solving interval many-objective optimization problems by combination of NSGA-III and a local fruit fly optimization algorithm

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Papers

Online fault diagnosis for sucker rod pumping well by optimized density peak clustering.

Oil field routing inspection fixed-point data acquisition system and method based on multiple unmanned aerial vehicles

Multiple-model-based on-line soft measurement method for gas-oil ratio of pumping well oil liquid

Solving interval many-objective optimization problems by combination of NSGA-III and a local fruit fly optimization algorithm