Working Conditions Of Equipment Research Articles

A novel attention-based long short term memory and fully connected neutral network approach for production energy consumption prediction under complex working conditions

Continuously growing demands due to the predictable faults or abnormal events of the flexible production line are becoming a challenge to easily cause the energy waste, which needs to accurately predict the real-time energy consumption of the actual production process. By reviewing the related works about prediction model approach and energy consumption analysis in the actual production application, it is necessary to overcome two main challenges: optimal prediction selection and internal relation mining of energy consumption under different equipment working conditions. To deal with the existing challenges, the paper proposes a novel attention-based Long and Short-Term Memory (LSTM) approach to predict energy consumption with temporal series under various equipment working conditions. This method offers forecasting support to form a complete prediction framework for the automatic Miniature Circuit Breaker (MCB) production line, which can be used to predict the specific working condition based on attention-based LSTM approach. By comparing various prediction approaches for energy consumption under the working condition prediction with various equipment of different production line, the Fully Connected Neutral (FCN) network approach can achieve a high accuracy and effectiveness based on MAE, R2 and MSE evaluations.

Denoising graph neural network based hydraulic component fault diagnosis method

Accurately identifying faults in hydraulic components is essential for improving the reliability and safety of hydraulic systems. Since the hydraulic components of heavy-duty equipment usually work in complex and variable environments and are subject to interference from multiple sources of noise. As a result, fault diagnosis of hydraulic components is challenging, leading to the development of various fault diagnosis models. However, current hydraulic component fault diagnosis models have the following limitations: (1) only able to handle Euclidean space data and ignore the topological relationships within the signals that could provide additional useful information for fault diagnosis, (2) only diagnose faults using single-dimensional acceleration data and fail to incorporate more equipment working conditions. This study focuses on the hydraulic directional valve and proposes a fault diagnosis method based on denoising graph neural network. Firstly, computational fluid dynamics(CFD) is used to analyze the flow field of the spool and sleeve of the valve under different wear states and working conditions. It is found that different degrees of wear have similarities under different working conditions, and there is a regular correlation between the acceleration signal of the valve body and the fault mode under the same working condition. Therefore, acceleration and working condition information are selected as the raw information for fault diagnosis. Then, horizontal visibility graph(HVG) was utilized to transform acceleration signal to graph, and the graph nodes, which can incorporate multidimensional information, were set to be the equipment working conditions and acceleration values. However, the noise in the raw signal could seriously affect the quality of the HVG. To solve this problem, an adaptive threshold denoising algorithm was proposed to reduce the interference noise in the acceleration signal. The denoising threshold parameter was learned directly from the raw data. Finally, graph neural network(GNN) is applied to fault diagnosis of hydraulic components from non-Euclidean multidimensional information. Experimental results demonstrate the superior robustness and accuracy of this method in handling strong noise and multiple working conditions compared to several classical fault diagnosis methods. This research provides an effective approach for hydraulic component fault diagnosis.

A rapid outliers detection and correction method for external ballistic velocity measurement data

Due to the influence of various sudden and abnormal factors during the tracking environment, equipment working conditions, and operation process, there are inevitable outliers in the tracking measurement process of aircraft such as carrier rockets, artificial satellites, and missiles. Therefore, the prerequisite for ensuring the reliability of processing results is to timely and accurately detect and correct outliers. In this paper, we proposed the sliding window-based variable degree B-spline function method (SWVD B-spline), which can handle isolated outliers and spotted outliers. SWVD B-spline uses variable degree B-spline function to model observed data in sliding windows, which can detect and correct outliers point by point along with window sliding. Then, we propose an initial window data selection method to remove outliers in initial windows to ensure the processing effect. In addition, because there are often inflection points in external ballistic velocity measurement data, differential evolution is used to optimize variable degree B-spline in windows that include inflection points to improve processing accuracy. The experimental results verify that SWVD B-spline can handle various outliers rapidly and efficiently.

Perceived Impact of University Education on Practices of Graduate Nurses at Federal Medical Centre, Owo, Nigeria

Introduction: Nurses globally have made giant strides in the development of nursing, which has moved nurses' education from hospital-affiliated schools into Universities and colleges. The study determined the impact of the knowledge of the University level of education on nursing practices at Federal Medical Center, Owo. Method: A descriptive research design was adopted. Using a convenient sampling technique, a total of 133 nurses were accessed. Data were collected using a self-designed structured questionnaire which consists of five sections and a checklist to observe the practice of both graduate and diploma nurses. Data collected were analyzed with SPSS software using both descriptive and inferential statistics. Result: There are many benefits of university education as identified by the respondents as follows: it brings job satisfaction (89.5%), it helps to give the best care (86.3%), it gives room for competitiveness (89.5%), it encourages leadership role (92.6%), it increases the degree of autonomy in the nursing profession (90.5%), it helps to give best care (86.3%), and so on. The factors affecting the practices of graduate nurses include understaffing (86.4%), unavailability of necessary equipment (88.4%), Lack of role differentiation (86.3%), and Hospital policy (92.6%) and poor working condition of equipment and instruments (100.0%). There is a significantly higher degree of expertise in health care delivered by graduate nurses over diploma nurses; and their ability to plan, implement and evaluate patient care outcomes (P=0.009). In addition, a significant relationship exists between knowledge acquired by graduate nurses and their nursing care practices determines to be P= 0.004. In Conclusion, graduate nurses have a greater impact on healthcare delivery than diploma nurses. Therefore, they should be employed in larger numbers for better healthcare delivery in hospitals.

Superplastic behavior of a Ni-Co-base superalloy with high γ′-phase content

The Ni-Co-base superalloy with a 750 °C service temperature contains a substantial number of refractory and γ′-phase forming elements that cause high deformation resistance, low thermoplasticity, and a limited hot-working window. The traditional deformation processing is carried out at low strain rates and high temperatures > 1100 °C, which is extremely strict on equipment working conditions. We coarsened the γ′-phase to facilitate the dynamic recrystallization (DRX) and inhibit grain growth, obtaining small grains of less than 10 µm. Ultimately, the superplasticity of the alloy can reach 1650% elongation at temperatures below 1000 °C and a high strain rate of 0.01/s.

Fault pattern recognition of rolling bearing based on smoothness prior approach and dual-input depth spatial–temporal fusion

Accurate fault diagnosis is critical for the safe and stable operation of mechanical equipment. Current deep learning (DL)-based fault diagnosis can extract various and deep discriminative features efficiently. Different DL methods are applicable to different data types, and the extracted features are also different. In addition, the equipment working conditions are complex, and noise from the working environment is inevitable. If only a single network or single input is used, it is difficult to extract the information that can comprehensively describe the fault features, which affects the diagnosis accuracy, especially when multiple faults (more than ten types) are considered. Hence, this study proposes a method for fault diagnosis based on the smoothness prior approach (SPA) and a dual-input depth spatial–temporal fusion network. First, the original signal is decomposed via SPA and two different types of input are constructed. Second, a dual-input depth spatial–temporal fusion network is proposed to extract deeper information by simultaneously learning temporal and spatial features. These two types of features are fused using a two-dimensional convolutional neural network to complete classification tasks. The average recognition accuracy of the proposed network can be maintained above 99% on two commonly used benchmark vibration datasets. Compared with several state-of-the-art diagnosis methods under different noise level experiments, the proposed method can achieve higher diagnosis accuracy for each dataset under different working conditions.

DDPG-based continuous thickness and tension coupling control for the unsteady cold rolling process

Cold rolling is an important part of the iron and steel industry, and the unsteady rolling process of cold rolling usually brings significant influences on the stability of product quality. In the unsteady rolling process, various disturbances and uncertainties such as variable lubrication state, variable equipment working conditions lead to difficulties in the establishment of state space model of thickness and tension, which has become a thorny problem in thickness and tension control. In this paper, we present a model-free controller based on Deep Deterministic Policy Gradient (DDPG), which can continuously control the thickness tension of the unsteady rolling process without the mathematical model. We first formulate the thickness and tension control problem to Markov Decision Process (MDP). We apply strategies such as dividing state space variables with the mechanism model, defining reward function and state normalization, the random disturbance and complex uncertainties of the unsteady cold rolling process are coped with by utilizing the DDPG controller. In addition, these strategies also ensure the learning performance and stability of DDPG controller under random disturbance. Simulations and experiments show that the proposed the DDPG controller does not require any prior knowledge of uncertain parameters and can operate without knowing unsteady rolling mathematical models, which has better accuracy, stability, and rapidity for thickness and tension in the unsteady rolling process than proportional integral (PI) controller. The artificial intelligence–based controller brings both product quality improvement and intelligence to cold rolling.

Data-driven system efficiency prediction and production parameter optimization for PW-LHM

Heavy oil wells in Tahe oilfield are exploited by injecting light hydrocarbon mixing into annulus to reduce viscosity, and system efficiency prediction is of great significance for the evaluation of the energy consumption and management level of the production system. However, system efficiency of the pumping unit well with light hydrocarbon mixing (PW-LHM) is affected by many factors, and it is difficult to use traditional methods to make comprehensive evaluation and quantitative prediction. In order to accurately predict the system efficiency and evaluate the viscosity reduction effect, this paper used the Pearson correlation coefficient analysis method to analyze the correlation between production data, diluting attributes and system efficiency, and principal component analysis (PCA) was utilized to conduct data dimension reduction and controlling parameters determination. Considering the change trend and correlation of the artificial lifting equipment and mixing equipment working conditions, a time series prediction model for system efficiency was established based on the long short-term memory (LSTM) algorithm. Filed application results show that the prediction model based on LSTM can perform accurate prediction of the system efficiency, early forewarning of the production conditions and timely adjustment of the lifting and mixing parameters of the PW-LHM.

Failure prediction using artificial intelligence for heavy duty equipment

Failure analysis of heavy-duty equipment is becoming one of the largest areas where artificial intelligence is finding application. The process of detecting the conditions of fault varies widely across various approaches of the diagnostic system to different working conditions of equipment. The application of decision-making for fault detection methods allows in-depth analysis by creating a reasoning system that works like human being. Many faults detection decision-making systems have been rule-based where slight changes in prediction data affect the entire result. On the other hand, the prediction of the failure analysis includes real-time data or various modelling techniques on which effective algorithms have been made. A combination of AI systems with other advanced prediction models can be used for optimum maintenance methodologies for heavy-duty equipment.

The Influence of Climate Parameters on Maintenance of Wind Farms-A Galician Case Study.

There are different monitoring procedures in wind farms with two main objectives: (i) to improve energy production by the capability of the national electrical network and (ii) to reduce the stooped hours due to preventive and or corrective maintenance activities. In this sense, different sensors are employed to sample in real-time the working conditions of equipment, the electrical production and the weather conditions. Despite this, just the anemometer measurement can be related to the more important errors of interruption of power regulation and anemometer errors. Both errors are related to gusty winds and contribute to more than 33% of the cost of a wind farm. The present paper reports some mathematical relations between weather and maintenance but there are no extreme values of each variable that let us predict a near failure and its corresponding loss of working hours. To achieve this, statistical analysis identifies the relation between weather variables and errors and different models are obtained. What is more, due to the difficulty and economic implications involving the implementation of complex algorithms and techniques of artificial intelligence, it is still a challenge to optimize this process. Finally, the obtained results show a particular case study that can be extrapolated to other wind farms after different case studies to adjust the model to different weather regions, and serve as a useful tool for weather maintenance.

Research on Alarm Classification Methods Based on Multivariate Condition Data

The primary focus of alarm classification is to classify alarms according to equipment condition and operator actions, which is the equivalent of classifying multivariate alarm-relevant condition data. This paper proposed an alarm classification model which aims to appropriately cluster values of alarm-relevant condition data through cluster analysis. We proposed an alarm classification model, and implemented a clustering tool for multivariate alarm-relevant condition data and a statistical tool for alarm classification. This model formulates alarm classification rules by conducting cluster analysis on condition data. These rules in turn can be jointly employed by the model to classify alarm events according to equipment working conditions and operator actions. The resulting alarm categories with a high frequency of occurrence are then displayed.

Influence of the temperature on the fracture energy of a methacrylate adhesive for mining applications

The effects of the increase in temperature are of great importance when evaluating the strength of an adhesive. Some processes in mining, such as copper electro-wining, produce thermal changes that modify the working conditions of equipment and structures; these elements are exposed to temperatures that can reach up to 80 °C. The study presented here aims to determine the behavior, under fracture of mode I type, of a two-component adhesive regularly used to join pieces in acid mist extraction systems. For this purpose, specimens for a double cantilever beam test were produced and tested in an Instron tensile machine, which includes an environmental chamber to control the test temperature; each lot of specimens was tested at 20, 50 and 80 °C respectively, at a speed of 1 mm/min. From the results obtained, it is possible to appreciate that the adhesive at 50 °C decreased its strength by 14 % with respect to those at the reference temperature of 20 °C. The same tendency was observed in the specimens tested at 80 °C, in which there was a pronounced reduction in strength quantified by 26 %. Moreover, deformation in the adhesive grew with the increase in temperature, acquiring greater plasticity and modifying its cohesive properties.

The Design and Implementation of the Power Distribution Unit in Campus Intelligent Computer Room

This paper described a kind of designation method to the power distribution system in the modern computer room .Compared with the traditional power supply socket, The Power Distribution Unit (PDU) with these functions whichcan monitor the RMS current and voltage, line frequency ,energy usage ,load limitation and the relay controlling to current leak ,over load or over current etc., There also has the wired RS-485and reserved the wireless WIFI interfaces in the PDU through which the working conditionsof equipment in the computer room can be connected and monitored by administrator at any time and place.

Special features of using methods of mathematical modelling for the determination of electron beam welding conditions

A mathematical model for the process of electron beam welding, based on the solution of the nonlinear Stefan problem, by the finite difference method is described. An original adaptive algorithm is proposed. The algorithm can be used to determine the required power of the electron beam and the efficiency of focusing in the dialogue regime in determining the working conditions of equipment on the basis of the defined characteristics of the welded joint and welding speed.

Decision support system for scheduling preventive maintenance activities

It has been observed that in many manufacturing companies, preventive maintenance activities, such as regular lubricating, checking and maintaining, are scheduled and carried out in a pre-deterniined time period, eg. once per month, regardless equipment working conditions. This results in a certain amount of waste of maintenance resources. To solve this problem, we use the concepts in reliability engineering, statistical process control (SPC) techniques, and artificial intelligence to develop a decision support system for scheduling preventive maintenance activities in dynamic time periods according to the working conditions of the equipment. This paper reports the basic methods of the system. Some industrial applications are also described in the paper.

Integrated maintenance management system in a textile company

In this paper, an AI (artificial intelligence) approach to the design and control of an integrated maintenance management system is reported. The research work has been done on two levels. At the managerial level, the overall maintenance management system is designed by the GRAI method. This system is designed as an integrated system which makes decisions on maintenance activity scheduling and control, taking into consideration not only equipment working conditions but also maintenance cost, product quality, and production efficiency. At the decision support level, a number of intelligent decision support systems (IDSSs) are developed based on Bayesian theory or causal probabilistic networks (CPNs). In this paper, a generic CPN for the maintenance of open-end spinning mills is reported.