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 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.

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.

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.

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.

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.

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.

University-School Partnership in China:Teachers’Personal Factors,Working Conditions,and Principal Leadership That Explain Their Development in Teaching

In China,the university-school partnership(USP)is a community of continuous professional development(PD)for teachers,involving teacher educators who visit schools.This study explores teachers’personal factors,school working conditions,and principal leadership in order to explain differences in teachers’learning when they have participated in the training program.Using a one-group pretest-posttest design,375 teachers from 12 primary schools in Shanghai participated.Their learning performances are measured by changes in their teaching quality as evaluated by their students.Results of regression analyses show that teachers generally receive higher scores on teaching quality after the program than before.Three factors are significantly and negatively related to the changes in quality:teachers’educational level,the extent to which teachers feel emotional pressure in their profession,and the support from their school principal.Implications for school leaders and policymakers are discussed.

Generation of a scenario library for testing driver-automation cooperation safety under cut-in working conditions

To improve the efficiency of safety tests of driver-automation cooperation,a method for generating a scenario library is proposed that considers the probability of scenario occurrence and driver-handling challenges in real driving situations.First,the original scenario data under cut-in conditions stored in a time series are extracted from the scenario data set.Then,a mathematical performance index is used to model the scenario and a significance function in terms of the occurrence frequency of the scenario,and the performance challenge between the driver and the vehicle is established.Next,the important scenario set is extracted from the original scenario set by constructing and optimizing a significance auxiliary function.Finally,the extracted important scenario sets are filtered by using the significance function values of the scenarios to generate a scenario library.Simulation results show that the proposed method for scenario library generation can effectively identify scenarios with potential adventure during driver-automation cooperation and thus accelerate safety tests compared with traditional methods.

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.

Methods of Random Fatigue Design for Vehicle Transmission System Components

Some representative working conditions were measured, and the amplitude distribution rule of each representative working condition after analysis of measured data was got. The building of 2 D distributing function between the range and the mean of random load was discussed. Experiment was carried out to get the fatigue strength data of the material of transmission component. Accessing the P S a S m N camber of combined load of bending and torsion on this material after analysis. And the process of calculating the 2 D fatigue life in multi working condition was discussed.

Stress analysis of full-length grouted bolt under shear deformation of anchor interface

The bolt anchoring force is closely related to the shear properties of the anchor interface. The shear stress distribution of full-length grouted bolts is analyzed based on the stress-strain relationship among the bolt, grout, rock mass and bond interface,considering the shear properties of the grout and contact interface bonding behavior. In this case, the interfacial shear stress of the grout and rock mass and the bolt axial force are obtained under pull-out and normal working conditions. The results show that the peak shear stress of the interface with the shear deformation of the bond interface is significantly lower than that without it when the pull-out force is applied. When designing bolt parameters of grade IV and V rock mass, the relative deformation between the rock mass and anchor should be considered, with a “unimodal” to “bimodal” shear stress distribution.In the case of a low elastic modulus of rock masses,both the shear stress concentration and distribution range are obvious, and the neutral point is near the bolt head. As the elastic modulus increases, the shear stress concentration and distribution range are reduced, and the neutral point moves towards the distal end. As a result, the optimum length of fulllength grouted bolts can be determined by in-situ pull-out tests and decreases with the increased elastic modulus of the rock mass.

Novel Ship Propulsion System

As the development tends towards high-speed, large-scale and high-power, power of the ship main engine becomes larger and larger. This make the engine design and cabin arrangement become more and more difficult. Ship maneuverability becomes bad. A new ship propulsion system, integrated hydraulic propulsion （IHP）, is put forward to meet the development of modem ship. Principle of IHP system is discussed. Working condition matching characteristic of IHP ship is studied based on its matching characteristic charts. According to their propulsion principle, dynamic mathematic models of IHP ship and direct propulsion （DP） ship are developed. These two models are verified by test sailing and test stand data. Based on the software Matlab/Simulink, comparison research between IHP ship and DP ship is conducted. The results show that cabin arrangement of IHP ship is very flexible, working condition matching characteristic of IHP ship is good, the ratio of power to weight of IHP ship is larger than DP ship, and maneuverability is excellent. IHP system is suitable for engineering ship, superpower ship and warship, etc.