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.

Study of energy-efficient heat resistance and cooling technology for high temperature working face with multiple heat sources in deep mine

In the present research,we proposed a scheme to address the issues of severe heat damage,high energy consumption,low cooling system efficiency,and wastage of cold capacity in mines.To elucidate the seasonal variations of environmental temperature through field measurements,we selected a high-temperature working face in a deep mine as our engineering background.To enhance the heat damage control cability of the working face and minimize unnecessary cooling capac-ity loss,we introduced the multi-dimensional heat hazard prevention and control method called"Heat source barrier and cooling equipment".First,we utilize shotcrete and liquid nitrogen injection to eliminate the heat source and implemented pressure equalization ventilation to disrupt the heat transfer path,thereby creating a heat barrier.Second,we establish divi-sional prediction models for airflow temperature based on the variation patterns obtained through numerical simulation.Third,we devise the location and dynamic control strategy for the cooling equipment based on the prediction models.The results of field application show that the heat resistance and cooling linkage method comply with the safety requirement throughout the entire mining cycle while effectively reducing energy consumption.The ambient temperature is maintained below 30℃,resulting in the energy saving of 10%during the high-temperature period and over 50%during the low-temperature period.These findings serve as a valuable reference for managing heat damage in high-temperature working faces.

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.

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.

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.

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.

Frequency Analysis of Multiple Layered Cylindrical Shells under Lateral Pressure with Asymmetric Boundary Conditions

Natural frequency characteristics of a thin-walled multiple layered cylindrical shell under lateral pressure are studied. The multiple layered cylindrical shell configuration is formed by three layers of isotropic material where the inner and outer layers are stainless steel and the middle layer is aluminum. The multiple layered shell equations with lateral pressure are established based on Love＇s shell theory. The governing equations of motion with lateral pressure are employed by using energy functional and applying the Ritz method. The boundary conditions represented by end conditions of the multiple layered cylindrical shell are simply supported-clamped（SS-C）, free-clamped（F-C） and simply supported-free（SS-F）. The influence of different lateral pressures, different thickness to radius ratios, different length to radius ratios and effect of the asymmetric boundary conditions on natural frequency characteristics are studied. It is shown that the lateral pressure has effect on the natural frequency of multiple layered cylindrical shell and causes the natural frequency to increase. The natural frequency of the developed multilayered cylindrical shell is validated by comparing with those in the literature. The proposed research provides an effective approach for vibration analysis shell structures subjected to lateral pressure with an energy method.

OPTIMALITY CONDITIONS AND DUALITY RESULTS FOR NONSMOOTH VECTOR OPTIMIZATION PROBLEMS WITH THE MULTIPLE INTERVAL-VALUED OBJECTIVE FUNCTION

In this paper, both Fritz John and Karush-Kuhn-Tucker necessary optimality conditions are established for a （weakly） LU-efficient solution in the considered nonsmooth multiobjective programming problem with the multiple interval-objective function. Further, the sufficient optimality conditions for a （weakly） LU-efficient solution and several duality results in Mond-Weir sense are proved under assumptions that the functions constituting the considered nondifferentiable multiobjective programming problem with the multiple interval- objective function are convex.

The effectiveness of mI SMART:A nurse practitioner led technology intervention for multiple chronic conditions in primary care

Aims:Used as integrated tools,technology may improve access and outcomes of care.A new intervention that integrates multiple technologies called mI SMART has been developed,implemented,and evaluated by Nurse Practitioners.The aim of this paper is to present the initial effectiveness of a webbased,structure of sensors and mobile devices designed to overcome the known health determinant of access to care for rural,chronically ill patients by using technology.Methods:The study was conducted at a community primary-care clinic that provides free healthcare to impoverished adults.Adults with at least one chronic condition,a minimum of 3rd grade reading level,and without dementia/psychosis were recruited.Participants were given a Nexus7 tablet and Bluetooth self-monitoring devices.The intervention lasted for 12 weeks.Blood glucose,blood pressure,and weight were collected using the provided Bluetooth devices and means were evaluated with paired-samples ttests before and after the intervention.Results:Thirty participants were majority female,white,married,high-school educated or less,earning less than$20,000 per annum,and had multiple chronic conditions.Pre-intervention glucose,systolic blood pressure,diastolic blood pressure,weight and Body Mass Index were all reduced after the 12-week intervention.Conclusions:The mI SMART intervention is efficacious for use in improvised adults living in rural areas with multiple chronic conditions.As previously reported,the intervention was also shown to be feasible and acceptable to patients.The next step is a larger randomized controlled trial.

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.

Multiple Solutions for a Class of Semilinear Elliptic Equations with Nonlinear Boundary Conditions

In this paper, using Local Linking Theorem, we obtain the existence of multiple solutions for a class of semilinear elliptic equations with nonlinear boundary conditions, in which the nonlinearites are compared with higher Neumann eigenvalue and the first Steklov eigenvalue.

The M^X/M/1 Queue with Multiple Working Vacation

We study a batch arrival MX/M/1 queue with multiple working vacation. The server serves customers at a lower rate rather than completely stopping service during the service period. Using a quasi upper triangular transition probability matrix of two-dimensional Markov chain and matrix analytic method, the probability generating function (PGF) of the stationary system length distribution is obtained, from which we obtain the stochastic decomposition structure of system length which indicates the relationship with that of the MX/M/1 queue without vacation. Some performance indices are derived by using the PGF of the stationary system length distribution. It is important that we obtain the Laplace Stieltjes transform (LST) of the stationary waiting time distribution. Further, we obtain the mean system length and the mean waiting time. Finally, numerical results for some special cases are presented to show the effects of system parameters.

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.

EXISTENCE OF MULTIPLE SOLUTIONS FOR NONLINEAR ELLIPTIC EQUATIONS WITH MIXED BOUNDARY CONDITIONS

The paper is concerned with the multiplicity of solutions for some nonlinear elliptic equations involving critical Sobolev exponents and mixed boundary conditions.

Distillation Sequence Optimization Considering Extractive Distillation under Multiple Conditions: A Methanol to Propylene Case Study

This study provides insights into the distillation sequence optimization of refinery system in a methanol to propylene plant with extractive distillation under multiple conditions. The simulated annealing algorithm(SA) with relative cost function was used to solve a meaningful optimization problem. It was observed that different conditions had differed on the flowsheet. Case study shows the effectiveness of the proposed method.

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.

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.

Dose of Telehealth to Improve Community-Based Care for Adults Living with Multiple Chronic Conditions: A Systematic Review

The purpose of this systematic review is to identify evidence of the appropriate dose of telehealth intervention services provided to community dwelling adults experiencing chronic illness or disability related to effectiveness, quality, safety, and cost. Academic Search Complete, CINAHL, MEDLINE, Cochrane, and JBI were searched using combinations of “telehealth or telemedicine or telemonitoring or telepractice or telenursing or telecare AND chronic illness or chronic disease”. Of the identified 449 articles, 47 articles met the inclusion criteria. Most study designs were quasi-experimental one group pre-test post-test (N = 16) with few Randomized Controlled Trials (N = 12). Twenty-three published articles studied the effect of telehealth for one chronic condition (49.9%) while 24 (51.1%) examined the effectiveness of telehealth for multiple chronic conditions. Measurement of telehealth outcomes varied and included efficacy, healthcare utilization, quality, adherence, cost, and safety. No standard measure of dose could be extrapolated. Length of intervention was measured and reported differently in each study. The dose of telehealth services that improve care effectiveness, quality, safety, and cost is still unknown for community dwelling adults experiencing chronic illness. The findings from this systematic review do indicate that longer duration of telehealth services (51 weeks), regardless of modality, produced positive outcomes as opposed to those with shorter durations (37 - 38 weeks) that produced neutral or mixed results. Collecting and reporting data related to clinical workflow such as dose of intervention specific to disease and type of modality is recommended. Rigorous study design including standard measurement at the RCT and Comparative Effectiveness level is still needed.