Operation Of Equipment Research Articles

FDS-MOMEDA: optimization-blind deconvolution in finite high-dimensional spaces for extracting pulse signal in rolling bearing fault diagnosis

Rolling bearing fault diagnosis is crucial for ensuring the safe and reliable operation of mechanical equipment. Detecting faults directly from measurement signals is challenging due to severe noise and interference. Blind deconvolution (BD), as a preferred method, effectively recovers periodic pulses from the measured vibration signals of faulty bearings. This study introduces a simulated annealing-based BD approach to enhance the pulse signal components reflecting faults in vibration signals measured on rolling bearings. This method iteratively searches for the optimal coordinates in a high-dimensional orthogonal optimization space, where the optimal coordinates reflect the combination of the inverse filter coefficients. Compared to the generalized spherical optimization space used in the ‘Optimization-Blind Deconvolution’ method in previous works, the proposed finite high-dimensional optimization space helps overcome the problem of inverse filter coefficient convergence, allowing for the design of inverse filters without limit of its shape. To better accommodate the cyclostationarity characteristics of bearing signal measured in reality, the proposed method employs a target vector that allows for uncertainty in pulse occurrence instants, thus overcomes challenges introduced by pseudo-periodic phenomena resulting from bearing slippage. Numerical simulations and experimental results on real bearing vibration signals confirm that the proposed method can design more flexible filters to enhance pulse-like patterns in signals, effectively utilize limited filter resources. Its capacity to tolerate inaccurate fault period estimates, high background noise, and pulse randomness enables it to effectively address vibration measurement signals in real-world scenarios.

A Deep Learning Method for Bearing Cross-Domain Fault Diagnostics Based on the Standard Envelope Spectrum.

Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the reconstructed envelope spectrum is proposed to improve the ability of rolling bearing cross-domain fault diagnostics in this paper. First, based on the envelope spectrum morphology of rolling bearing failures, a standard envelope spectrum is constructed that reveals the unique characteristics of different bearing health states and eliminates the differences between domains due to different bearing speeds and bearing models. Then, a fault diagnosis model was constructed using a convolutional neural network to learn features and complete fault classification. Finally, using two publicly available bearing data sets and one bearing data set obtained by self-experimentation, the proposed method is applied to the data of the fault diagnostics of rolling bearings under different rotational speeds and different bearing types. The experimental results show that, compared with some popular feature extraction methods, the proposed method can achieve high diagnostic accuracy with data at different rotational speeds and different bearing types, and it is an effective method for solving the problem with cross-domain fault diagnostics for rolling bearings.

Visualization display and exit fault diagnosis of secondary virtual real circuit in intelligent substation

Most communication in smart substations uses fiber optic connections to connect secondary equipment now. Due to the lack of intuitive connection between devices, it leads to difficulties in monitoring and relay protection. In order to solve the problems of visualizing, monitoring, and diagnosing secondary virtual circuits in the operation and maintenance of intelligent substations, a comprehensive monitoring technology system for secondary virtual circuits in intelligent substations is constructed. Firstly, a breadth first search algorithm is proposed to sort out the physical connection relationships between secondary devices, which achieves visual display of secondary virtual circuits. Then, based on deep search fault inference algorithms, it can diagnose and locate the fault area. Through the massive data source of intelligent substations, multiple information fusion can be achieved. By applying the D-S evidence theory, precise fault localization can be achieved. Finally, the proof table method is used to determine the type of fault and to meet the practical application requirements for the operation and maintenance of secondary equipment in intelligent substations.

Penyuluhan Rancang Bangun Pembangkit Listrik Menggunakan Alternator Kepada Siswa SMKN 3 Tangerang Selatan

Counseling students at SMK Negeri 3 South Tangerang City who have motorbike competency technical skills is very important to understand and understand the function of the alternator from motorbike equipment for generating electrical energy. The source of electrical energy in automotive motorbikes generally comes from a power plant using an alternator (alternating generator) to supply electrical loads, including the electrical load of charging the battery. Implementation of outreach activities is carried out for students in class . The counseling method for class for 90 minutes understand and comprehend in a tutorial the operation of equipment designed to build a power plant using an alternator. The final result is understanding and comprehending the implementation of the counseling given the opportunity to write class comments for 30 minutes on the google classroom page. It was found that 17 students wrote class comments on the results of counseling on the design of power plants using alternators. abstract must be a minimum of 150 words, and a maximum of 300 words, using Indonesian and English in italics with Times New Roman 10. The abstract must be clear, providing a brief overview of the problems and solutions to community service problems. The abstract contains the background or importance of the topic of community service, objectives of community service, service partners; service methods, and a summary of results and conclusions. The abstract ends with comments about the importance of the service results or a short conclusion.

Effect of high-temperature molten salt corrosion on the mechanical properties of stainless steel

The corrosivity of molten salt can be detrimental to the safe operation and longevity of concentrated solar thermal power generation equipment. Additionally, the presence of impurity Cl− can increase corrosion on metal materials, leading to mechanical property degradation. To assess the extent of this effect, the corrosion behaviour of three types of stainless steel (304, 316L, and 347H) in molten salt at high temperatures was studied using the loss-in-weight method and tensile tests. According to the findings, the Rdepths of 304, 316L, and 347H stainless steels are 0.0018 mm/a, 0.0016 mm/a, and 0.0019 mm/a, respectively, resulting in a decrease in elongation of 12.2%, 7.0%, and 3.7% after 720 h of corrosion at 600°C. Furthermore, at a higher temperature of 680°C, the Rdepths of all stainless steels increased, and the elongation decreased by 25.4%, 18.9%, and 13.5%, respectively. Similar corrosion behaviour was observed in 347H stainless steel with increased chloride ion concentration. However, the yield strength and tensile strength of stainless steel did not exhibit significant changes under different experimental conditions. The corrosion mechanism of metal alloys in molten salt is mainly due to the selective corrosion of Cr and its dissolution into molten salt in the form of ions.

Assessment of the technical state of mining machinery and devices with the use of diagnostic methods

Abstract In this article, one of the possible, effective methods of assessing the technical state of mining machinery and devices has been presented. The article's main goal is to show the possible methods and ways of measuring temperature, oscillations, and vibrations generated during the operation of mining equipment and machines’ gears, which are possible in underground conditions. Devices measuring temperatures without coming into contact with the given object are built based on different types of infrared radiation detectors or matrixes of such detectors. Thermal imaging devices picture the temperature distribution on the entire surface instead of pyrometers, which measure temperatures at a given point. However, vibrations and oscillations generated by the work of gears of devices and machinery have been made using a vibrometer pen and a machine condition tester, after which the obtained measurements were used to diagnose the degradation of individual elements of those machines. Such an assessment of the degradation of individual elements of machines in production conditions has been applied with positive results in one of the mines of the Polish Mining Group (Polska Grupa Górnicza – PGG).

Optimization and uncertainty analysis of Co-combustion ratios in a semi-isolated green energy combined cooling, heating, and power system (SIGE-CCHP)

This study delved into the integration of biomass gas and natural gas within a Combined Cooling, Heating, and Power (CCHP) system. A Semi-Isolated Green Energy CCHP (SIGE-CCHP) model was devised to scrutinize the performance of co-firing equipment across diverse optimization objectives, while manipulating the proportions of natural gas and biomass gas as inputs. Findings revealed that escalating the share of biomass gas led to a reduction in carbon emissions but triggered an escalation in operational and maintenance costs. However, at an optimal mixing ratio of 1:1, carbon emissions exhibited marginal increments, coupled with a substantial decrease in operational and maintenance expenses. Notably, when prioritizing operational and maintenance costs, the system exhibited optimal performance, resulting in a notable 26.76 % cost reduction. Conversely, when prioritizing carbon emissions, the system metamorphosed into a carbon sequestration entity, with a maximal capacity to absorb 2021.86 kg of carbon dioxide. This study furnishes theoretical underpinnings for optimizing the operation of co-firing equipment, augmented by a sensitivity analysis aimed at intuitively elucidating the repercussions of varying mixing ratios on the system.

Dynamic failure assessment analysis of 15MnTi steel supporting structure with a circumferential surface crack under impact loads

Supporting structures, whose strength have great influence on the safe operation of the whole equipment, are widely used in various ship equipment. In this paper, dynamic failure assessment analysis is carried out to discuss the structural integrity of the cracked supporting structure under impact loads. Considering the dynamic response characteristics of structures, the dynamic fracture toughness determined by the loading rate of dynamic J-integrals is used to calculate the failure assessment point (FAP). Due to the widespread uncertainties in practical engineering, such as crack size and mechanical properties of materials, the failure assessment results will also have uncertainty. To analyze the structural integrity affected by uncertain variables, the polygonal convex set (PCS) model is introduced to the failure assessment diagram (FAD) to propose a non-probabilistic failure assessment method. The FAP is extended to a failure assessment region, and structural integrity can be analyzed by the non-probabilistic reliability degree. Compared with the traditional interval model, the non-probabilistic reliability degree calculated by PCS model is closer to the probabilistic reliability degree, which avoids premature judgement of structural failure. For the 15MnTi supporting structure with a circumferential semi-elliptical surface crack under impact loads, the critical depth obtained by non-probabilistic methods decrease by 10% compared with deterministic results, and the failure mechanism is summarized as plastic collapse resulting from the excessive net section stress. In the absence of statistical data on crack size or mechanical properties, the non-probabilistic failure assessment method can be used as an effective alternative to the probabilistic method.

Fault diagnosis of rolling bearings under varying speeds based on gray level co-occurrence matrix and DCCNN

Rolling bearings are widely used in various industries, including rail transit, aerospace, and wind power generation, playing a critical role. However, bearing failures can lead to serious consequences, impacting equipment operation and even causing safety accidents. Hence, the diagnosis of bearing faults is of utmost importance. However, the variable speed conditions experienced during bearing operation pose significant challenges to fault diagnosis. To overcome the limitations of traditional methods in diagnosing bearing faults under variable speed conditions, this paper proposes a fault diagnosis method based on the gray-level co-occurrence matrix (GLCM) and Dual Channel Convolutional Neural Network (DCCNN). The method introduces a two-dimensional grayscale matrix construction (2D-GMC) technique to extract grayscale texture features for fault diagnosis. Additionally, an unconventional kernel design method, based on grayscale image contrast, is proposed to reduce the complexity associated with traditional square kernels. A new DCCNN architecture is developed accordingly. Furthermore, the transfer learning concept is utilized to train the proposed DCCNN model using fault signals at specific rotational speeds. The method intercepts the variable speed into multi-speed short-time series, then constructs gray image under different speed to realize the rapid fault diagnosis of bearings under variable speed conditions.

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism.

Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction-attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects.

The use of image analysis to study the effect of moisture content on the physical properties of grains.

Designing machines and equipment for post-harvest operations of agricultural products requires information about their physical properties. The aim of the work was to evaluate the possibility of introducing a new approach to predict the moisture content in bean and corn seeds based on measuring their dimensions using image analysis using artificial neural networks (ANN). Experimental tests were carried out at three levels of wet basis moisture content of seeds: 9, 13 and 17%. The analysis of the results showed a direct relationship between the wet basis moisture content and the main dimensions of the seeds. Based on the statistical analysis of the seed material, it was shown that the characteristics examined have a normal or close to normal distribution, and the seed material used in the investigation is representative. Furthermore, the use of artificial neural networks to predict the wet basis moisture content of seeds based on changes in their dimensions has an efficiency of 82%. The results obtained from the method used in this work are very promising for predicting the moisture content.

Analysis of Safety Management Issues and Countermeasures for Construction Operations of Petroleum Refining and Chemical Equipment

The economic operation in modern society is closely related to petroleum, and petroleum refining products play a great role in various industries such as military, transportation, agriculture, light industry, and textile industry. Refining and chemical enterprises belong to fixed asset intensive enterprises, among which the equipment used for oil refining is the most important asset in fixed assets. Improving the operational safety of equipment plays an extremely important role and significance in improving and enhancing the economic benefits of enterprises. This article starts with the analysis of safety hazards at construction sites and explores potential risk factors such as human factors and equipment failures. A series of safety management measures are proposed to address safety issues, improving the effectiveness of safety management by establishing a sound safety management system, applying advanced safety equipment and technology, and developing effective emergency response and post-disaster recovery plans.

Research and application of power equipment monitoring and fault diagnosis

Power equipment monitoring and fault diagnosis are important parts of power system operation and maintenance. By monitoring the status and performance parameters of power equipment in real time, and timely discovering and diagnosing potential fault problems, the reliability, safety and economy of the power system can be improved. At present, this research has made significant progress, but there are still some research gaps that need to be further explored and resolved. Through the research and application of power equipment monitoring and fault diagnosis, this paper aims to improve the reliability, safety and economy of power grid, reduce the risk of failure, optimize equipment operation and maintenance strategies, and improve power supply quality and user satisfaction. Condition monitoring and fault diagnosis are very important for the normal operation of the power system equipment, which can not only strengthen the fault diagnosis of the power system equipment, but also avoid the problem of large-scale paralysis caused by the failure of the power system equipment.

Experimental simulation of the generation of microwave radiation during the interaction of a lightning discharge with an aircraft

On a specialized testing installation — a high-voltage Marx generator, which allows the formation of a spark discharge up to 20 meters long, the process of the impact of lightning radio emission in the microwave range on an aircraft (AC) is simulated. A metal AC model made on a scale of 1:50 was placed in a 9 m long rod-plane spark gap. A positive voltage pulse was applied to the high-voltage electrode from the generator (front 100 μsec, duration 7500 μsec). The generated spark discharge passed through the AC model. It was found that when the spark channel passes through the body of the model, electromagnetic pulses of the microwave range with a duration of less than 500 psec and a rise time of less than 100 psec arise. The measurement of the resulting microwave pulses was carried out using special radio equipment in the subnanosecond range. High-speed photography of the discharge development, carried out synchronously with electromagnetic measurements, showed that microwave pulses (up to 10 GHz) arise at the stage of development of the spark discharge leader in the part of the “high-voltage electrode – insulated model” gap. Such pulses can be considered as a factor dangerous for various AC microwave radio systems, when the AC is struck by lightning, as well as for other objects, including power facilities, which contain equipment with microelectronics. Such microwave pulses can also occur during nearby lightning discharges (for example, in a lightning rod of a power substation), when a powerful streamer corona occurs on devices and wires of ultra-high voltage lines), and when a high-voltage spark discharge occurs when switching equipment (disconnectors, arresters) is triggered. In this case, disruptions are possible in the operation of control equipment (for example, relay protection devices) that contains microelectronics, as well as various communication means available at electric power facilities operating in the microwave range (above 1 GHz).

Facile Estimation of Surface Oxygen Vacancies in Co3O4 Catalysts with a Colorimetric Method.

Oxygen vacancy (Ov) is known to act as an active center of the metal oxide. Quantification of surface Ov is vital for understanding the quantitative structure-activity relationship. Facile quantification characterization of surface Ov is highly desirable but still challenging. In this study, we presented a simple colorimetric method for rapidly quantifying surface Ov. As an example of metal oxide nanoparticles, Co3O4 was used to catalyze the 3,3',5,5'-tetramethylbenzidine (TMB)-H2O2 colorimetric reaction. It was found that the absorbance of the TMB-H2O2 system was dependent on the surface Ov amount in Co3O4. The investigation of the mechanism showed that the Ov-dependent absorbance would be attributed to the activity of surface Ov to easily adsorb and dissociate H2O2 into a hydroxyl radical (•OH). The absorbance signal of the TMB-H2O2 system acted as a probe to estimate the surface Ov. This colorimetric measurement could be completed in less than 20 min. The Ov concentrations obtained by the proposed colorimetric method matched well with those obtained by X-ray photoelectron spectroscopy. This method does not require any complex operation and expensive equipment and can be performed in any ordinary chemical laboratory. So, this colorimetric method is expected to become an alternative approach for quantifying the surface Ov in metal oxide nanoparticles. This method will provide essential insights into the rational design and application of Ov.

Performance degradation assessment of rolling bearings based on the comprehensive characteristic index and improved SVDD

Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing.

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

As a key component of mechanical equipment, real-time monitoring and diagnosis of rolling bearings play a critical role in ensuring the stable operation of equipment and the safety of operators. In order to present the current status and trends of fault diagnosis research on rolling bearings more intuitively, the scientific knowledge mapping was used to visualize and analyze the relevant literature in the article. The results show that the number of publications in this area of research has grown significantly in recent years, with China, India, the United States, and England having contributed significantly. The journals such as MECHANICAL SYSTEMS AND SIGNAL PROCESSING, MEASUREMENT, and JOURNAL OF SOUND AND VIBRATION have played an important role in disseminating cutting-edge technologies in this field. In addition, the exploration of modern methods based on data-driven and artificial intelligence, as well as their application to real-world problems, are gradually becoming the focus of research. Through summarising and analysing, the application of modern data processing techniques, the development of more efficient and practical intelligent fault diagnosis techniques, and the close integration of laboratory research and practical applications will become future research trends.

Dry wear and corrosive wear behavior of laser-cladded Co19.6Cr19.6Fe19.6Ni19.6(B13.72Si5.88)19.6Y2 and Fe43.6Ni17.4Cr9Co6B17.5Si1.5Nb5 coatings

Corrosive wear is a key factor affecting the safe operation of offshore engineering equipment. In this study, Co19.6Cr19.6Fe19.6Ni19.6(B13.72Si5.88)19.6Y2 and Fe43.6Ni17.4Cr9Co6B17.5Si1.5Nb5 coatings were prepared on the surface of 304 stainless steel using laser cladding technology. Dry wear and corrosive wear behavior of both coatings and 304 stainless steel were comparatively studied under different loads. The results show that both coatings are mainly composed of FCC solid solution, boride, and Laves phases. The average microhardness of Co19.6Cr19.6Fe19.6Ni19.6(B13.72Si5.88)19.6Y2 and Fe43.6Ni17.4Cr9Co6B17.5Si1.5Nb5 coatings are 578.6 HV0.2 and 632.5 HV0.2, respectively. The dry wear resistance of Fe43.6Ni17.4Cr9Co6B17.5Si1.5Nb5 coating is approximately 3 times that of 304 stainless steel under all friction load conditions. It can be found that the Fe43.6Ni17.4Cr9Co6B17.5Si1.5Nb5 coating with high hardness is less sensitive to friction load changes during dry wear process. In 3.5 wt% NaCl solution, the corrosive wear resistance of CoCrFeNiBSiY coating under friction loads of 30 N and 50 N is 2.68 times and 2.83 times that of 304 stainless steel, respectively. The excellent corrosive wear resistance performance of the CoCrFeNiBSiY coating is mainly attributed to its superior corrosion resistance and repassivation ability. During the corrosive wear process, a dense passivation film is formed on the surface of both coatings, which significantly reduces their friction coefficient and volume loss rate.

Simulation of freezing a sea water droplet moving in a cold air

A rapid freezing of a sea water droplet moving in a cold air is modeled. A droplet freezing model is a key component needed for computational forecasting of ice accretion on surfaces of ships and other equipment operating in low-temperature regions. The freezing process consists of the three stages: 1) water cooling to the incipient solidification temperature; 2) liquid solidification; 3) further freezing of the primarily solidified droplet. An icy region, being formed during the 2nd stage, initially represents a slurry, composed of ice crystals suspended in water. Further cooling is associated with an increase in the crystal concentration that at a certain threshold causes slurry transformation into a spongy (porous) ice. The solidification stage model is composed of the three coupled differential equations formulated in the moving coordinate system. All the physical and thermophysical water properties, required for modeling droplet freezing, are calculated by using the empirical correlations. The set of the equations is solved numerically. The solidification process is illustrated by computational examples for different droplet sizes and water salinities. The computed icy region thickness vs. time, as well as temperature distributions and porosities along droplet radius at different time moments are shown.

Fault Diagnosis Method for Railway Signal Equipment Based on Data Enhancement and an Improved Attention Mechanism

Railway signals’ fault text data contain a substantial amount of expert maintenance experience. Extracting valuable information from these fault text data can enhance the efficiency of fault diagnosis for signal equipment, thereby contributing to the advancement of intelligent railway operations and maintenance technology. Considering that the characteristics of different signal equipment in actual operation can easily lead to a lack of fault data, a fault diagnosis method for railway signal equipment based on data augmentation and an improved attention mechanism (DEIAM) is proposed in this paper. Firstly, the original fault dataset is preprocessed based on data augmentation technology and retained noun and verb operations. Then, the neural network is constructed by integrating a bidirectional long short-term memory (BiLSTM) model with an attention mechanism and a convolutional neural network (CNN) model enhanced with a channel attention mechanism. The DEIAM method can more effectively capture the important text features and sequence features in fault text data, thereby facilitating the diagnosis and classification of such data. Consequently, it enhances onsite fault maintenance experience by providing more precise insights. An empirical study was conducted on a 10-year fault dataset of signal equipment produced by a railway bureau. The experimental results demonstrate that in comparison with the benchmark model, the DEIAM model exhibits enhanced performance in terms of accuracy, precision, recall, and F1.