Operating Conditions Of Equipment Research Articles

Design of A Grid-connected Control System for Distributed Photovoltaic Power Generation Based on PLC

Distributed photovoltaics interfere with continuous power generation after grid connection. In the face of the failure of a single module, the current grid-connected control system needs to readjust system parameters to flexibly adapt to equipment operating conditions. Therefore, this paper is researching a photovoltaic power generation grid-connected control system based on PLC. In the hardware part, PLC is used to complete power generation control, monitoring MCU, data acquisition, control, and other modules. In the software part, the grid-connected state is optimized and controlled according to the distributed photovoltaic output power and the remaining energy storage capacity. The PLC is the execution center, which will control the on-off state of the equipment switch after the data operation. The experimental results show that the designed system can optimally control the power generation and energy storage units according to the power change, reduce the cost of electricity consumption through the adjustment of charge and discharge, reduce the IO usage rate, and improve the concurrent response ability of users. The design system not only guarantees the quality and efficiency of photovoltaic grid connection but also realizes rapid networking and independent tracking of power points to meet the requirements of flexible communication.

Research and application of equipment intelligent operation and maintenance platform

The intellectualization of equipment operation and maintenance is one of the main development directions under the background of intelligent manufacturing. Based on lean theory and an intelligent algorithm, this paper establishes the equipment hierarchical management model and equipment intelligent operation and maintenance framework, introduces the lean management strategy and equipment operation state prediction algorithm in the operation and maintenance framework in detail, and finally realizes the design and development of equipment intelligent operation and maintenance platform. The platform has been proved in a steel structure manufacturing enterprise that it can effectively realize equipment condition monitoring, fault diagnosis, and performance prediction, and has a significant effect in reducing accidental shutdown loss and improving machine utilization.

A Permanent Magnet Ferromagnetic Wear Debris Sensor Based on Axisymmetric High-Gradient Magnetic Field.

The detection of wear debris in lubricating oil is effective for determining current equipment operating conditions for fault diagnosis. In this paper, a permanent magnet ferromagnetic wear debris sensor is proposed that is composed of a compact structure and a detection coil that generates an induced voltage when wear debris passes through a magnetic field. A three-dimensional model of the sensor is established, the internal axisymmetric high-gradient magnetic field of the sensor is analyzed, and a mathematical model of the sensor signal is proposed. The effects of the air gap structure of the sensor and the relative permeability, velocity, and volume of the wear debris on the sensor performance are analyzed. The correctness of the theoretical results is proven by single particle experiments, and the sensor is calibrated to achieve quantitative analysis of the wear debris.

A multi-scale and multi-objective optimization strategy for catalytic distillation process

This work aims to establishing a multi-scale and multi-objective optimization strategy for the catalytic distillation process. Firstly, the catalyst layer efficiency factor correlation that embraces the influence of the catalyst layer structure was obtained and then coupled with the process simulation to establish a multi-scale model for the catalytic distillation process. On this basis, genetic algorithm was employed to realize the multi-objective optimization. The hydrolysis process of methyl acetate in the reactive dividing wall column was investigated. The equipment parameters and operational conditions of the catalytic distillation column, and the structure parameters of the catalyst layer are optimized simultaneously. The results indicated that the minima of the total annual cost, the gas emission cost and the exergy loss decrease by 19.26%, 23.11% and 67.27%, respectively, by comparing with the counterparts obtained from the single-factor sensitivity analysis. Furthermore, the catalyst cost decreases by 30.88% per year.

Digital thread-based modeling of digital twin framework for the aircraft assembly system

Digital twins are proposed to manage the industrial production sites by mapping physical space entities to virtual spaces. Existing digital twin modeling methods are mainly realized through deploying several modules, including a physical module, a virtual module, a service module, a data module, and a connection module. However, the assembly environment of the aircraft assembly process is complex and challenging to manage. These methods do not provide the data flow and the intrinsic interaction between modules. As a consequence, a digital thread-based modeling digital twin (DTDT) framework with five modules is established in this paper, which takes advantage of both the digital twin and digital thread. Benefited by better data management, the DTDT framework would be conducive to the controllability and traceability of the manufacturing process and product quality. The key characteristics of products, process parameters, and equipment operation conditions, are stored in the database module. And then, the digital thread module will link the entity module and virtual module together by building the directed graph, in which interfaces are provided to the system services module for the process monitoring and processing quality prediction. Finally, the implementation of the DTDT framework is shown through a case of the drilling and riveting process in aircraft assembly. The comparative analysis confirms that this model can improve the efficiency of the aircraft assembly process.

Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis.

The rotating component is an important part of the modern mechanical equipment, and its health status has a great impact on whether the equipment can safely operate. In recent years, convolutional neural network has been widely used to identify the health status of the rotor system. Previous studies are mostly based on the premise that training set and testing set have the same categories. However, because the actual operating conditions of mechanical equipment are complex and changeable, the real diagnostic tasks usually have greater diversity than the pre-acquired datasets. The inconsistency of the categories of training set and testing set makes it easy for convolutional neural network to identify the unknown fault data as normal data, which is very fatal to equipment health management. To overcome the above problem, this article proposes a new method, Huffman-convolutional neural network, to improve the generalization ability of the model in detection task with various operating conditions. First, a new Huffman pooling kernel is designed according to the Huffman coding principle and the Huffman pooling layer structure is introduced in the convolutional neural network to enhance the model's ability to extract common features of data under different conditions. Second, a new objective function is proposed based on softmax loss, intra-class loss, and inter-class loss to improve the Huffman-convolutional neural network's ability to distinguish different classes of data and aggregate the same class of data. Third, the proposed method is tested on three different datasets to verify the generalization ability of the Huffman-convolutional neural network in diagnosis tasks with multi-operating conditions. Compared with other traditional methods, the proposed method has better performance and greater potential in multi-condition fault diagnosis and anomaly detection tasks with inconsistent class spaces.

Operation monitoring method of pipeline equipment in natural gas compressor station based on spiral microphone array

The abnormal operating conditions of pipeline equipment in natural gas compressor station, such as gas pipeline leakage or equipment failure, lead to negative social impacts. To solve the current problem of untimely and inaccurate manual maintenance, this study proposes a 24/7 real-time method for monitoring the operation of pipeline equipment in natural gas compressor station based on a spiral microphone array. First, the Linearly Constrained Minimum Variance (LCMV) beamformer is used to enhance the signal in the target angle direction and suppress the interference in other directions. Then we train the support vector machine (SVM) model based on the station’s Mel-Frequency Cepstral Coefficients (MFCC) feature dataset for diagnosis. The results show that the method is of great feasibility and reliability.

Online detection of bus bar plane stress based on electromagnetic loading

PurposeThe purpose of this study is to develop an electromagnetic loading method for online measurement of the acoustoelastic coefficients and bus bar plane stress.Design/methodology/approachA method based on the combination of electromagnetic loading and the acoustoelastic effect is proposed to realize online measurement of acoustoelastic coefficients and plane stress. Electromagnetic loading is performed on the bus bar specimen, and the acoustoelastic coefficients and the bus bar plane stress are obtained by the ultrasonic method. An electromagnetic loading experimental platform is designed to provide electromagnetic force to the metal plate, including an electromagnetic loading module, an ultrasonic testing module and a stress simulation module.FindingsThe feasibility of the proposed electromagnetic loading method is proved by verification experiments. The acoustoelastic coefficients and plane stress measured using the electromagnetic loading method are more accurate than those measured using the traditional method.Originality/valueThe proposed electromagnetic loading method provides a new study perspective and enables more accurate measurement of the acoustoelastic coefficients and plane stress. The study provides an important basis for evaluating the operation status of electrical equipment.

Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic

With the rapid development of industrial informatization and deep learning technology, modern data-driven fault diagnosis (MIFD) methods based on deep learning have been receiving attention from the industry. However, most of these methods require sufficient training samples to achieve the desired diagnostic effect, and the scarcity of fault samples in the actual industrial environment leads to the limitation of the development of MIFD methods. In addition, data-driven fault diagnosis methods often need to face cross-load or even cross-domain problems across different devices due to changes in equipment operating conditions and production requirements. In this paper, we design a parameter optimization and feature metric-based fault diagnosis method with few samples, called model unknown matching network model, for the problem of sparse fault samples and cross-domain between data sets in real industrial environments. The method combines both a parametric optimization-based meta-learning network, which extracts optimization information to adapt between different domains, and a metric-based metric learning network, which extracts metric information for similarity discriminations. The experimental results show that the method outperforms the current baseline method for the five-shot fault diagnosis problem of bearings under limited data conditions and achieves an accuracy of up to 94.4 in cross-device diagnosis experiments from bearings to gas regulators, indicating the feasibility of the method. The features are visualized by T-SNE to show the validity of the model.

Acceleration sensor of medium-high frequency dual FBG based on L-shaped support sensitization structure

It is a key part for micro seismic crack detection, mechanical equipment operation state and structural health monitoring to measure high frequency vibration. A kind of FBG acceleration sensor based on flexure hinge is proposed targeting at the problem of existing medium-high-frequency FBG acceleration sensor’s low sensitivity. Two FBGs are fixed on L-shaped support in a differential arrangement to improve sensitivity of sensor and eliminate adverse effects caused by temperature changes. Matlab and ANSYS software are used to simulate, analyze and optimize sensor. According to the simulation results, the real sensor is made, and the sensor calibration experiment is carried out. Research results show that resonant frequency of sensor is about 1700 Hz and has good linearity in flat range of 50–1000 Hz. Sensitivity of sensor can reach 18.9 pm/g, while linearity is higher than 99%, and temperature sensitivity is merely 0.074 pm/°C. Research results provide reference for researching and developing the same type of sensor, and further improving optical fiber acceleration sensor sensitivity.

Evaluation of hand-arm vibration(HAV) exposure levels among grounds maintenance workers: An observational human exposure measurement study.

Evaluation of hand-arm vibration(HAV) exposure levels among grounds maintenance workers: An observational human exposure measurement study.

Информационно-алгоритмическое обеспечение эффективных систем управления

Modern production machinery is equipped with multifunctional automated process control systems (APCS) based on software and hardware complexes (SHC) of a network hierarchical structure. The experience of designing and operating multifunctional process control systems proves that the efficiency of the systems being developed significantly depends on the array of problems at different stages of the technology design and system modernization. It is to be developed, both at the fundamental level of local automatic control systems and at a higher level of hierarchical system structure. At the same time, special attention is paid to the software failure, which is fixed, as a rule, by periodically updating all the content. For process control systems, such an approach under the operation conditions of power equipment is unacceptable. In this regard, the effectivity problems of complex systems are to be classified in terms of technological process factors, the theory of automatic control and instrumental factors associated with the proper software implementation. As a theoretical basis to solve the problem of information sufficiency and synthesis of structurally stable systems, the methods of potentials and coordinates of the thermodynamics of irreversible processes are used. The testing method is applied as a tool to analyze and diagnose software and hardware tools. The authors have defined three groups of factors: system, controller and local. The task to analyze the conformity of information and algorithmic support means to expert and verify the absence of defects in the software of a multifunctional system and the fulfillment of the declared requirements. The automated testing system is a unified environment to analyze the execution of test scenarios. It provides continuous validation and verification of the information and algorithmic support of the SHC. In particular, a methodology has been developed to carry out resource tests of the software and hardware complex and checking the operation of the mechanisms for backing up, synchronizing, and replacing its components by simulating failures in the processor modules, hardware and software components. A set of methodological factors has been defined to develop advanced software and hardware systems for process control systems. It significantly affects the adequacy of the information and algorithmic support of a complex system. A technology of automated testing of software and hardware tools of the SHC has been developed. Methods to solve interdisciplinary problems are generalized. The authors recommend solving the problems at the earlier stages of a complex system design. The proposed technology to develop and diagnose automatically software and hardware of the SHC by implementing a single environment to study, compare and detect the defects of the SHC software throughout the entire life cycle of the process control system allows you to identify and eliminate the mutual negative influence of the SHC components both at the development stage and at the stage of the subsequent system organization, including solving the problem of information security at the stage of system installation and operation. The developed approach to solve complex interdisciplinary problems that require solution at the early stages of designing complex systems makes it possible to avoid fundamentally erroneous decisions and increase the competitiveness of the SHC as a systemically important APCS complex at the time of installation.

Self-organizing maps for efficient classification of flow regimes from gamma densitometry time series in three-phase fluidized beds

The potential of artificial neural networks as a tool to classify and identify a change in the flow regime of a three-phase fluidized bed is studied. Particularly, the suitability of self-organizing maps (SOMs), unsupervised neural networks that visualize the data in a lower dimension, is evaluated. Statistical features of experimental time series determined in a three-phase (granulated carbon-air-water) fluidized bed are extracted as inputs to train the SOM. Photon-count time series are obtained along the fluidized bed vertical axis by gamma-densitometry at different operative conditions. Then, they are analyzed to determine the underlying flow regime indexes. When each input data is presented to the SOMs, a neuron is activated, giving a visual representation of the data. The resulting models show three different regions on the map for the homogenous, transition, and heterogeneous flow regimes. Once these regions are delimited, the map can quickly classify the equipment operating conditions. The ability of the SOMs to diagnose a flow transition is verified against visual observation and gas hold-up trends. The conclusions are tested for their sensitivity to alternative axial positions of the radiation source used for the densitometry.

A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method

Information such as probability distribution, performance degradation trajectory, and performance reliability function varies with the service status of rolling bearings, which is difficult to analyze and evaluate using traditional reliability theory. Adding equipment operation status to evaluate the bearing operation performance status has become the focus of current research to ensure the effective maintenance of the system, reduce faults, and improve quality under the condition of traditional probability statistics. So, a mathematical model is established by proposing the hierarchical maximum entropy Bayesian method (HMEBM), which is used to evaluate the operation performance status of rolling bearings. When calculating the posterior probability density function (PPDF), the similarities between time series regarded as a weighting coefficient are calculated using overlapping area method, membership degree method, Hamming approach degree method, Euclidean approach degree method, and cardinal approach degree method. The experiment investigation shows that the variation degree of the optimal vibration performance status can be calculated more accurately for each time series relative to the intrinsic series.

Research on rotor system fault diagnosis method based on vibration signal feature vector transfer learning

Aiming at the common fault diagnosis problems of rotors in industrial applications. A rotor system fault diagnosis method based on vibration signal feature vector transfer learning is proposed. First, Statistical methods and wavelet packet decomposition are used for vibration signal feature extraction. Then, the ReliefF algorithm is used to evaluate the fault features and screen out the sensitive fault features set. Next, the training data and real time test data are mapped to the kernel Hilbert space using the transfer component analysis method. Finally, the weighted k-nearest neighbor method is used as the fault feature classifier for fault pattern recognition. Model training and validation using typical failure datasets of different equipment and different operating conditions. Compared with other related methods, the results indicate that the proposed method has better generalization and diagnostic accuracy. This research will promote the engineering application of intelligent fault diagnosis of rotor system.

Using the "minimum risk" method in the technical diagnosis of metallurgical equipment

The difficult operating conditions of metallurgical equipment due to dynamic loads require special attention when designing components in the field of reliability and fail-free operation. In order to increase the reliability and durability of the spindle drive unit of the rolling stand of the hot rolling mill "1680", it is proposed to switch from "oil mist" type lubrication systems to "oil-air" type systems for bronze liners and bearings of the balancing mechanism. The oil-air lubrication principle has undeniable advantages in terms of component lubrication, flow distribution, and provides a volumetric flow of oil by injecting air into each bearing of the equipment, guaranteeing an accurate volume at each lubrication point, regardless of bearing back pressure, atmospheric pressure, temperature and oil viscosity . In order to optimize decision-making when designing new components and parts of metallurgical equipment, the vector of making reliable design decisions is increasingly shifting towards mathematical modeling of production processes and situations that arise during the performance of technological operations. It has been established that in order to determine the permissible value of the content of wear products in the form of metal shavings, one can use the general theory of recognition, which is an important section of technical cybernetics and deals with the recognition of images of any nature, namely, the "minimal risk" method. Recognition algorithms are partly based on diagnostic models that establish a connection between the state of a technical system and diagnostic signals coming from these systems. The performed calculations make it possible to accurately establish the limiting values of iron-containing impurities in the working fluid of the "oil-air" lubrication system and indicate that if the limit value x0 = 11 is exceeded, that is, if the content of iron-containing impurities in the working fluid is more than 11 g per 100 cm3, the object should be stopped for inspection and the working fluid should be cleaned by filtration. The possibility of determining the permissible value of the content of wear products in the form of metal shavings in "oil-air" lubrication systems using the general theory of recognition, namely the "minimal risks" method, which simplifies the process of setting the date of its cleaning by filtration, without burdening it with especially cumbersome formulas and calculations.

Feasibility of using ultrasonic-based prediction for hydrogen embrittlement susceptibility of high-strength heat-resistant steel 2.25Cr-1Mo-0.25V

Nondestructive evaluation for the mechanical properties loss of equipment materials serviced in the hydrogen environment is particularly vital for monitoring the equipment operating conditions. In this study, the quantitative relationship between the introduced hydrogen content and the mechanical property degradation of 2.25Cr-1Mo-0.25V steel was investigated using electrochemical hydrogen charging technique and tensile testing, and a nondestructive testing method based on pulse-echo ultrasonic measurement technique, combined with the results of mechanical assessment for hydrogen embrittlement (HE) susceptibility, was proposed to indirectly estimate the degree of hydrogen-induced plasticity loss of the steel. Moreover, signal reconstruction technique was also used to improve the measurement accuracy of ultrasonic echo signal. The results of the study showed that hydrogen present in the metal lattice intensifies the energy attenuation of ultrasonic echo, verifying the feasibility and validity of the measurement method in the effective online prediction of HE susceptibility of the tested steel.

A novel dynamic simulation approach for Gas-Heat-Electric coupled system

Dynamic simulation of Gas-Heat-Electric Coupled System (GHECS) can accurately describe the operation state of networks and equipment of the system, which can support the analysis of the stability and safety of GHECS. However, differences among simulation time-step of subsystems, computational complexities and lengthy simulation time make it difficult to precisely simulate the coupled system. To solve the aforementioned problems, this paper establishes a novel parameter interaction framework for simulating the GHECS and proposes a system decoupling methodology and simulation sequence determination approach. A variable time-step solving strategy is also introduced to balance the simulation accuracy and efficiency. To validate the proposed methodology, comprehensive analysis of various cases on a 53-node testbed is presented. Results prove that the dynamic simulation can precisely describe 740 s heat transfer delay and 1015 s dynamic response time, and the variable time-step solving strategy can improve the simulation accuracy by 1.7%.

A logical reasoning-based method for sensing the operating status of equipment

Grid equipment monitoring is to achieve comprehensive perception, rapid analysis and accurate disposal of grid operation status. As the scale of the grid continues to expand, many problems such as low efficiency in single point-based alarming, complicated and difficult process, irregular work norms, etc., have occurred in the current operating mode, which greatly restricts the improvement of management level of monitoring operation. Based on the correlation between substation monitoring information and monitoring equipment operation, we establish the correspondence between primary equipment such as main transformer, busbar, line and capacitor and the corresponding telemetry such as current, voltage, active power, reactive power and frequency. Then we analyze the occurrence mechanism of power grid equipment operation events, sense the equipment operation status, and realize timely and accurate sense and push of equipment operation defects and faults, thereby improving operational efficiency of monitoring devices.

Polyester and Epoxy Resins with Increased Thermal Conductivity and Reduced Surface Resistivity for Applications in Explosion-Proof Enclosures of Electrical Devices.

Composite materials are still finding new applications that require the modification of various properties and are characterized by the summary impact on selected operational features. Due to the operating conditions of electrical equipment enclosures in potentially explosive atmospheres, the surface resistivity ensuring anti-electrostatic properties, i.e., below 109 Ω and resistance to the flame while maintaining appropriate operational enclosure properties is very important. It is also crucial to dissipate heat while reducing weight. Currently metal or cast-iron enclosures are used for various types of electrical devices. As part of the work, a material that can be used for a composite matrix for the enclosure was developed. The study aimed to assess the influence of selected fillers and chemical modifications on the thermal conductivity coefficient, resistivity, and strength properties of matrix materials for the production of electrical device enclosures used in the mining industry. Selected resins were modified with graphite, copper, and carbon black. Tests were carried out on the coefficient of thermal conductivity, surface resistivity, flammability, and flexural strength. At the final stage of the work, a multi-criteria analysis was carried out, which allowed the selection of a composite that meets the assumed characteristics to the highest degree. It is a vinyl ester composite modified with 15 wt.% MG394 and 5 wt.% MG1596 graphite (W2). The thermal conductivity of composite W2 is 5.64 W/mK, the surface resistivity is 5.2 × 103 Ω, the flexural strength is 50.61 MPa, and the flammability class is V0.