Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

Research on the dynamic response of connecting rod bearing bush wear of reciprocating machine under variable working conditions

As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the bearing and the abnormal vibration of the body.Based on the characteristics of poor lubrication state and complex force of connecting rod small head bearing, a mixed lubrication model considering oil groove feed was established, and the dynamic simulation of the reciprocating compressor model with lubricated bearings was carried out;considering different speeds and gas load conditions, the law of the impact of the eigenvalues changing with working conditions was explored.The fault simulation experiment was carried out by selecting representative working conditions, which verified the correctness of the simulation method.The study found that two contact collisions between the pin and the bearing bush occurred in one cycle, the collision impact was more severe under the wear fault, and the existence of the gap made the dynamic response more sensitive to the change of working conditions.This research provides ideas for the location and feature extraction of fault symptom signal angular segments in the process of complex measured signal processing.

The laparoscopic rating scale for the evaluation of working conditions for surgical treatment of super-obesity

In this technical note,a novel rating scale(abdominal integral index)was introduced for assessing the conditions of the working laparoscopic space based on linear measurements to select the optimal one or two-stage surgical treatment for super-obesity.Patients with the same height and similar BMI values had different rating scale scores,reflecting different conditions of laparoscopic bariatric surgery.The rating scale helps surgeons and patients make a safe option for surgery,depending on the experience of the surgeon and technical laparoscopic conditions.

A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

Most gear fault diagnosis(GFD)approaches su er from ine ciency when facing with multiple varying working conditions at the same time.In this paper,a non-negative matrix factorization(NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix,aiming to o er a fast multi-tasking solution.The short-time Fourier transform(STFT)is first used to obtain the time-frequency features from the gear vibration signal.Then,the optimal clustering numbers are estimated using the Bayesian information criterion(BIC)theory,which possesses the simultaneous assessment capability,compared with traditional validity indexes.Subsequently,the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks.Finally,the parameters involved in BIC and NMF algorithms are determined using the gradient ascent(GA)strategy in order to achieve reliable diagnostic results.The Spectra Quest’s Drivetrain Dynamics Simulator gear data sets were analyzed to verify the e ectiveness of the proposed approach.

A new diagnostic method for identifying working conditions of submersible reciprocating pumping systems

The submersible pumping unit is a new type of pumping system for lifting formation fluids from onshore oil wells, and the identification of its working condition has an important influence on oil production. In this paper we proposed a diagnostic method for identifying the working condition of the submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump structure, different characteristics in loading and unloading processes of the submersible linear motor were obtained at different working conditions. The characteristic quantities were extracted from operation data of the submersible linear motor. A diagnostic model based on the support vector machine （SVM） method was proposed for identifying the working condition of the submersible pumping unit, where the inputs of the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this method were analyzed and validated by the data acquired from an experimental simulation platform. The model proposed had an excellent performance in failure diagnosis of the submersible pumping system. The SVM classifier had higher diagnostic accuracy than the learning vector quantization （LVQ） classifier.

A dynamic size-based time series feature and application in identification of zinc flotation working conditions

Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have time series information.Based on the conventional froth size distribution characteristics,this paper proposes a size trend core feature(STCF)considering the froth size distribution,i.e.,a feature centered on the time series of the froth size distribution.The core features of the trend are extracted,the inter-frame change factor and the inter-frame stability factor are given and two calculation methods of the feature factors are proposed.Meanwhile,the STCF feature algorithm was established based on the core features by adding the inter-frame change factor and the inter-frame stability factor.Finally,a flotation condition recognition model based on BP neural network was established.The experiments show that the recognition model has achieved excellent results,proving that the method proposed effectively overcomes the limitation of the lack of dynamic information in the existing traditional size distribution features and the introduction of the two factors can improve the classification accuracy to varying degrees.

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

A Method of Shield Attitude Working Condition Classification

Aiming at solving shield attitude rectification failure problem,a method of shield working condition classification based on support vector data description( SVDD) was introduced. Shield attitude mechanics model containing priori knowledge was helpful to feature selection. SVDD handled the one class classification problem and a decision function for attitude rectification was proposed. Experimental results indicate that the method is able to accomplish the shield attitude working condition classification.

Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model

This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.

The Relationship between Working Conditions and Adverse Health Symptoms of Employee in Solar Greenhouse

To determine the correlation between the working environment and the health status of employees in solar greenhouse, 1171 employees were surveyed. The results show the ＇Greenhouse diseases＇ are affected by many factors. Among general uncomforts, the morbidity of the bone and joint damage is the highest and closely related to labor time and age. Planting summer squash and wax gourd more easilv cause skin pruritus.

The European Working Conditions Survey( EWCS) :a growing instrument: a reflection on the methodology,the reasons for and the use of the survey

The European Working Conditions(the EWCS)Series Eurofound,the European Foundation for the improvement of working and living conditions is a European tripartite agency which contributes to the improvement of working and living conditions.A key instrument to do so is the EWCS.The EWCS aims to comparably measure working conditions across European countries and beyond.This allows for the analysis of relationships between different aspects of working conditions,the identification of groups at risk and issues of concern,as well as areas of progress and the monitoring of trends over time.These analyses contribute to European policy development,in particular on issues with regard to the quality of work and employment.Following earlier editions of the EWCS in1991,1995,2000,2005 and 2010,a sixth wave of the survey will be fielded in 2015.Eurofound develops the questionnaire and outlines the design of the survey as well as a strict quality assurance framework.The actual preparation and implementation of fieldwork is contracted out.The development of the questionnaire is user led.Tripartite users of the survey are key in setting priorities for change.The questionnaire of the 6th EWCS maintain many trend questions which allow mapping changes over time.Yet a number of new questions will be included in the 6th editions which cover recent changes in work,sustainability of work,boundaryless work,work life balance,organisational justice,engagement,sleeping problems,chronic diseases,blurring frontiers in employment status.Changes over time On average there are little changes:this is because work situations and working conditions are becoming more diverse and inequalities in working conditions are increasing.On average,workers work less and less workers work long hours.They also work less"atypical"working hours.Work is taking place in more collective environment(clients,devolution of coordination tasks down to employee level etc.).Yet employment relations are more individualised.Technology use is on the increase but level of reported cognitive demands has remained the same.Exposure to physical risks remains high.Exposure to psychosocial risks is probably on the increase as illustrated by work intensification.Employment status in evolution:frontiers between categories less strict:the employment contract plays a key role in framing working conditions.Over time,there has been slow progress in gender segregation.Women still bear much of the burden of care activities.Sustainable work on the increase:good working conditions are key for sustainability as well as the provision of meaningful work.Less people report a good/very good work life balance.Important differences are lost at the aggregate level.Unfavourable working conditions tend to cluster disproportionally in some groups.Policy conclusions The improvement of working conditions is not automatic and need to be supported.Lifecourse perspective is important to understand trade-offs between job quality features.Social and economic policies are linked together:good work and good working conditions are key in facilitating good performance and wellbeing.Actions to support the improvement of working conditions are not necessarily costly.Many actors contribute to the improvement of working conditions and their efforts need to be supported.

The Realization and Working Conditions of Memristor Based on Multisim

An equivalent circuit is realized using Multisim software by transforming a kind of circuit element according to Mapping principle and circuit theory. The effects of every parameter on the equivalent circuit are analyzed and the working conditions of the equivalent circuit are concluded by simulation.

DTCWT-based zinc fast roughing working condition identification

The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process.The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform(DTCWT) is proposed for process monitoring of zinc fast roughing.Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification(i RFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.

Effect of working condition on thermal stress of NiFe_2O_4-based cermet inert anode in aluminum electrolysis

Based on the FEA software ANSYS,a model was developed to simulate the thermal stress distribution of inert anode.In order to reduce its thermal stress,the effect of some parameters on thermal stress distribution was investigated,including the temperature of electrolyte,the current,the anode cathode distance,the anode immersion depth,the surrounding temperature and the convection coefficient between anode and circumstance.The results show that there exists a large axial tensile stress near the tangent interface between the anode and bath,which is the major cause of anode breaking.Increasing the temperature of electrolyte or the anode immersion depth will deteriorate the stress distribution of inert anode.When the bath temperature increases from 750 to 970 ℃,the maximal value and absolute minimal value of the 1st principal stress increase by 29.7% and 29.6%,respectively.When the anode immersion depth is changed from 1 to 10 cm,the maximal value and absolute minimal value of the 1st principal stress increase by 52.1% and 65.0%,respectively.The effects of other parameters on stress distribution are not significant.

Research progress and prospects on machinery monitoring under varying working condition

A general review is given about the research progress of the rotating machinery condition monitoring under varying working condition. The major typical methods for analyzing are reviewed,including their progress,deficiencies and capabilities. Some prospects are given finally.

An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.

Assessment of Multiple Working Condition System Reliability with Agent-Based Simulation Method

The mission reliability assessment plays a great role in logistics planning and supporting resource optimization of complex system.But the current problem,which is difficult to solve,is how to model and analyze the characters of system reliability under the complex mission profile.In order to solve the problem,an agentbased simulation method was used to assess reliability for complex systems with various random working conditions.A multi-working condition simulation agent(MA)was designed and used to simulate the random transferring process of working conditions of system,and it cooperated with system simulation agents(SAs)and unit simulation agents(UAs)to realize system mission reliability(MR)simulation.Through simulation experiments,effect of multiple working conditions mission on the reliability of system was analyzed by comparing with the basic reliability condition.Feasibility and efficiency of the method were proved through simulation experiments of the case system.The research result provides a viable and useful method and a solution for MR analysis and assessment of complex systems in multi-working conditions,which can help to evaluate the reliability of operating system orienting to the practical mission and environment,and it is meaningful for the reliability analysis and the design of complex systems.

考虑谐波严重程度的长时间尺度谐波责任划分方法

针对现有谐波责任划分方法未考虑不同谐波严重程度下责任所造成的实际影响差异,提出一种考虑谐波严重程度的长时间尺度谐波责任划分方法。考虑谐波数值分布与变化趋势两方面因素划分工况,并计算各工况综合权重量化谐波严重程度;基于典型相关性分析原理筛选长时间尺度数据,并根据谐波责任定义式估算谐波责任;结合上述综合权重获取长时间尺度综合谐波责任划分指标;采用仿真算例与实测数据进行验证,与传统方法相比,所提方法可反映各谐波源在长时间尺度下不同次数谐波造成的累计影响,更适用于谐波精准治理与公平奖惩工作。

基于改进条件生成对抗网络的可控场景生成方法

可再生能源发电具有较强的随机性和波动性,为实现高效可靠的场景建模,提出一种基于改进条件生成对抗网络的可控场景生成方法。提出基于条件生成对抗网络的场景生成框架,结合Transformer的全局注意力机制以及常规卷积和深度可分离卷积的局部泛化机制,设计适用于提取可再生能源发电不同维度特征的网络结构;利用条件生成对抗网络模型建立低维气象特征隐空间和高维可再生能源发电数据之间的映射关系,提出一种可控场景生成方法,并建立随机场景生成、场景约减、极端场景生成和连续日场景生成4种生成策略。基于实际光伏、风电数据和气象数据的仿真结果表明,所提模型与方法能够有效学习可再生能源发电的随机性、时序性、波动性及空间相关性,实现对不同策略下场景的可控生成。