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 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.

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.

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.

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.

Describing failure in geomaterials using second-order work approach

Geomaterials are known to be non-associated materials. Granular soils therefore exhibit a variety of failure modes, with diffuse or localized kinematical patterns. In fact, the notion of failure itself can be confusing with regard to granular soils, because it is not associated with an obvious phenomenology. In this study, we built a proper framework, using the second-order work theory, to describe some failure modes in geomaterials based on energy conservation. The occurrence of failure is defined by an abrupt increase in kinetic energy. The increase in kinetic energy from an equilibrium state, under incremental loading, is shown to be equal to the difference between the external second-order work,involving the external loading parameters, and the internal second-order work, involving the constitutive properties of the material. When a stress limit state is reached, a certain stress component passes through a maximum value and then may decrease. Under such a condition, if a certain additional external loading is applied, the system fails, sharply increasing the strain rate. The internal stress is no longer able to balance the external stress, leading to a dynamic response of the specimen. As an illustration, the theoretical framework was applied to the well-known undrained triaxial test for loose soils. The influence of the loading control mode was clearly highlighted. It is shown that the plastic limit theory appears to be a particular case of this more general second-order work theory. When the plastic limit condition is met, the internal second-order work is nil. A class of incremental external loadings causes the kinetic energy to increase dramatically, leading to the sudden collapse of the specimen, as observed in laboratory.

Exploring the factors affecting electric bicycle riders'working conditions and crash involvement in Ningbo,China

The rapid development of the delivery industry brings convenience to modern society.However,the high rates of crashes and the survival of electric bicycle(e-bike)riders in the delivery industry raise concerns.The primary objective of this study is to explore the factors affecting delivery e-bike riders’stressful work pressure and crash involvement in China.Data were collected by a questionnaire survey administered in the city of Ningbo,China.A bivariate ordered probit(BOP)model was developed to simultaneously examine the factors associated with both the working conditions of delivery e-bike riders and their involvement in crashes.The marginal effects for the contributory factors were calculated to quantify their impacts on the outcomes.The results showed that the BOP model can account for commonly unobserved characteristics of the working conditions and crash involvement of delivery e-bike riders.The BOP model results showed that the stressful working conditions of delivery e-bike riders were affected by the number of orders and delivery time and rider age and risky riding behaviors.Delivery rider involvement in crashes was affected by the number of orders,strength of the punishment for traffic violations,and familiarity with traffic regulations.It was also found that stressful working conditions and crash involvement were strongly and positively correlated.The findings of this study can enhance our understanding of the factors that affect the working conditions and delivery rider crash involvement.Based on the results,some suggestions regarding public policy,risky riding behaviors,safety promotion,and stronger corporate governance rules were discussed to increase the targeted safety-related interventions for delivery ebike riders in Ningbo,China.

Analysis on the Failure Causes of the Collapsed Tubing in an Oil Well

Due to the influence of multiple factors such as internal and external formation and mechanical pressure, medium corrosion and construction operation environment, a tubing collapse failure occurred in an oil well. In order to determine the failure cause of the tubing, physical and chemical tests and mechanical properties analysis were carried out on the failed tubing sample and the intact tubing. The results show that the chemical composition, ultrasonic and magnetic particle inspection, metallographic test, Charpy impact energy and external pressure mechanical property test of the failed tubing all meet the requirements of API Spec 5CT-2021 standard, but the yield strength of the failed tubing does not meet the requirements of API Spec 5CT-2021 standard. Through the analysis of the working conditions, it can be seen that the anti-extrusion strength of the tubing collapse does not meet the API 5C3 anti-extrusion strength standard. The failure type of the well tubing is tubing collapse caused by large internal and external pressure difference.

二氧化碳汽车空调器变工况性能分析

建立了超临界二氧化碳汽车空调器循环计算模型 ,结合美国Illinois大学制冷空调中心 (ACRC)二氧化碳汽车空调样机实验结果 ,对两种压缩机转速和两种气体冷却器空气进口温度的不同组合条件下的工况 ,进行了循环计算 ,并对计算结果作了分析。计算结果表明 ,压缩机转速越高 ,或者气体冷却器空气进口温度越高 ,二氧化碳汽车空调的工况越恶劣 。

KC－20空调器装配线的工作研究

本文应用工业工程（ＩＥ）的原理、系统论的思想、工作研究的方法对ＫＣ－２０空调器装配线进行了分析、设计和改进。在不增加人力和设备及少量资金投入下，提高了现有装配线的生产效率，使企业获得了显著的经济效益。

天然工质应用于逆向Brayton循环中的理论分析

通过对逆向布雷顿循环 (Braytoncycle)中使用若干种天然工质的理论计算表明 :利用部分阻燃性气体 (如N2 ,CO2 )等与碳氢化合物 (烷烃 )的混合物作为制冷工质 ,是氟里昂工质的一种理想替代物 ,不仅具有安全、不燃、无公害的特点 ,使用于两相多元非共沸物膨胀制冷循环中具有制冷系数高、单位质量制冷(热 )

二氧化碳汽车空调

介绍二氧化碳汽车空调的组成以及工作原理,分析制冷系统主要件的特点及技术关键。

n个部件和开关组成的时序转换系统的可靠性

针对一些部件交替工作的复杂系统的可靠性指标求解问题,给出了由n个部件和一个开关组成的时序转换系统的定义,并对此类转换系统的可靠性进行研究。当转换系统中的部件和开关的寿命都服从指数分布时,得到了转换系统的可靠度解析式,并将结果应用于空调的可靠度求解,在实践中检验结果的合理性。

城市道路的燃油经济性分析

汽车燃油消耗量与发动机类型、制造工艺状况、道路条件、气候情况、海拔高度、驾驶技术等多种因素有关,对于城市道路中,停车怠速、汽车空调及制动能量损耗对于车辆的燃油经济性有重要的影响,对于城市这三个主要因素做出相应的分析。

组织调整材料的操作时间及固化时间的测量方法

目的利用移位式流变仪(Displacementrheometer),开发适当的测量组织调整材料的操作时间和固化时间的方法。方法使用5种组织调整材料。每个材料在23℃和37℃条件下各测量3次。在23℃下发生最初弹性回复的时间定义为操作时间,在37℃下弹性复原率表现为稳定数据的时间定义为固化时间。结果各个组织调整材料的操作时间和固化时间有显著性差异,显示出不同的固化行为。5种组织调整材料中SR的操作时间最短,为(2.7±0.2)min,VG的操作时间最长,为(15.7±0.5)min。VG的固化时间最长〔(25.0±0.8)min〕,SR的固化时间最短〔(7.7±1.9)min〕。结论移位式流变仪方法可以用于测量组织调整材料的操作时间和固化时间,并对分析组织调整材料的固化过程有重大意义。

一种新型分体式空调器及其热环工质的研究

对一种新型分体式空调器的结构、原理、特性和应用前景进行了分析 ,并对热环工质进行了研究与优选。