Non-stationary Conditions Research Articles

Mechanisms of formation of wear-resistant dissipative structures in non-stationary lubrication conditions

The work aimed to determine the influence of the processes of supramolecular self-organization in the lubricating layer on the patterns of wear of friction pairs. The mechanisms of structural adaptability of tribocoupler elements were analyzed, and the regularities of the manifestation of a large-scale and energy jump, which characterizes the transition of the tribosystem to a metastable state, were determined. An evaluation of the tribotechnical characteristics of commercial transmission oils was carried out on a software-hardware complex that, using a roller analogy, simulates the operation of gears in conditions of rolling with slipping. It was established that the activation of contact surfaces in the mode of frequent starts and stops leads to active interaction of the lubricant's components and the metal's surface layers with the gradual formation of boundary adsorption layers. For transmission oil 'Bora B' T-Shyp, an increase in anti-friction properties has been established due to the effective lubricating ability of the oil when forming the hydro- and non-hydrodynamic components of the lubricating layer thickness. The effect of the chemical activity of the zinc dialkyl dithiophosphate antiwear additive and the hydrocarbon components of the base of transmission oils on the effectiveness of the formation of boundary films is considered. It was determined that the formation of stable boundary films of the lubricant is the leading process in manifesting their damping properties concerning the localization of elastic-plastic deformation along the depth of the metal. When boundary films are formed on 90-95% of the contact area, the change in the microstructure of the near-surface layers is fixed at a depth of up to 20 microns; when boundary films are formed on 20...50% of the surface area, the spread of elastic-plastic deformation reaches a depth of up to 50 microns. The kinetics of the formation of boundary films by the lubricant and the indicators of the specific work of friction in contact are correlated with the intensity of wear of the contact surfaces

A low-complexity multi-channel active noise control system using local secondary path estimation and clustered control strategy for vehicle interior engine noise

Conventional multi-channel active noise control (ANC) system based on the adaptive notch filtered-x least mean square algorithm is typically used for active control of vehicle interior engine noise with prominent harmonic characteristics. However, due to the large estimated secondary path length and the centralized control strategy, the system has high computational complexity and fragile stability. To alleviate this problem, an efficient multi-channel ANC system using the local secondary path (LSP) estimation and clustered control strategy is proposed in this paper. Based on the modified LSP estimation method, the system may utilize a set of low-order but accurate LSP models to simplify the convolution operations of reference filtering. Besides, the proposed clustered control strategy combines the advantages of centralized and decentralized control strategies, and thus the system not only enjoys good noise attenuation performance and stability, but also its computational burden is further greatly reduced. Numerical simulations are performed to evaluate the noise attenuation performance and frequency mismatch effect of the proposed multi-channel ANC system. In addition, a series of real vehicle ANC experiments are conducted. The results show that, under stationary work conditions, the interior overall, 2nd order, 4th order, 6th order engine noises may be attenuated by 12.91 dB(A), 32.98 dB(A), 15.34 dB(A) and 8.74 dB(A), respectively, and under nonstationary work conditions, the maximum overall noise reductions at the four error microphones may reach 6.40 dB(A), 6.14 dB(A), 12.48 dB(A) and 11.07 dB(A), respectively. This fully confirms the effectiveness of the proposed multi-channel ANC system in real-world applications.

Specific features of the development of an adaptive control system for special ship devices on the example of a trawl winch of a fishing vessel

The trawl winch, being part of the auxiliary power plant of a fishing vessel, during its operation significantly affects the safety, reliability, and energy efficiency of the vessel as a whole. This is due to the peculiarity of the trawl winch. In addition to the existing external hydrometeorological factors, the dynamic performance of the machine is greatly influenced by the variability of the loading from the towed and raised fishing gear with the catch. Such non-stationary impacts have a negative impact on the performance and reliability of the equipment. Also, this type of lifting devices on a fishing vessel does not have a reserve. Therefore, when it fails, the fishing process also stops, which entails large economic losses. For smooth control of the winch operation in non-stationary conditions, an adaptive control system with an increased level of reliability is proposed in the work, which makes it possible to smoothly respond to load fluctuations. A control system diagram has been developed, as well as a diagram of the trawl winch drive itself, which is sensitive to changes in loading, the essence of which is to connect a backup hydraulic motor when the load from the towed or lifted object is exceeded.

A Hydraulic Axial Piston Pump Fault Diagnosis Based on Instantaneous Angular Speed under Non-Stationary Conditions

Due to the intense noise interference in hydraulic systems, it is extremely difficult to detect component faults through vibration signals. Diagnostic performance is also constrained by highly time-varying and non-stationary operating conditions. This study proposes to use instantaneous angular speed (IAS) signals that are both operational and state parameters as sources of information. Firstly, the instantaneous angular speed fluctuation (IASF) of a piston pump is analyzed theoretically, and it is concluded that its fluctuating components contain the health status information of the components. The IASF can then be obtained by subtracting the speed trend term from IAS signals obtained via a magneto-electric speed sensor. A synchro-extraction of the normal S transform (SNST) is proposed to process it via line-pass filtering. Finally, the filtered and reconstructed IASF signal is utilized to draw a two-dimensional polar coordinate map online. A non-stationary-condition test is carried out on the test platform to monitor the morphological characteristics of the valve plate under normal, slight, and severe wear conditions. The polar plot shows significant increases in speed fluctuations and oscillation times within a range from 180° to 270°. The relevant research results reflect that the IAS signal can provide a new method for monitoring the operating status of and conducting fault diagnoses for hydraulic equipment.

A Multi-Framework of Google Earth Engine and GEV for Spatial Analysis of Extremes in Non-Stationary Condition in Southeast Queensland, Australia

The frequency and severity of extremes, including extreme precipitation events, extreme evapotranspiration and extreme water storage deficit events, are changing. Thus, the necessity for developing a framework that estimates non-stationary conditions is urgent. The aim of this paper is to develop a framework using the geeSEBAL platform, Generalised Extreme Value (GEV) models and spatiotemporal analysis techniques that incorporate the physical system in terms of cause and effect. Firstly, the geeSEBAL platform has enabled the estimation of actual evapotranspiration (ETa) with an unprecedented level of spatial-temporal resolution. Following this, the Non-stationary Extreme Value Analysis (NEVA) approach employs the Bayesian method using a Differential Evolution Markov Chain technique to calculate the frequency and magnitude of extreme values across the parameter space. Station and global climate datasets have been used to analyse the spatial and temporal variation of rainfall, reference evapotranspiration (ETo), ETa and water storage (WS) variables in the Lockyer Valley located in Southeast Queensland (SEQ), Australia. Frequency analysis of rainfall, ETa, and water storage deficit for 14 stations were performed using a GEV distribution under stationary and non-stationary assumptions. Comparing the ETa, ETo and ERA5 rainfall with station data showed reasonable agreement as follows: Pearson correlation of 0.59–0.75 for ETa, RMSE of 45.23–58.56 mm for ETa, Pearson correlation of 0.96–0.97 for ETo, RMSE of 73.13–87.73 mm for ETo and Pearson correlation of 0.87–0.92 for rainfall and RMSE of 37.53–57.10 mm for rainfall. The lower and upper uncertainty bounds between stationary and non-stationary conditions for rainfall station data of Gatton varied from 550.98 mm (stationary) to 624.97 mm (non-stationary), and for ERA5 rainfall datasets, 441.30 mm (stationary) to 450.77 mm (non-stationary). The results demonstrate that global climate datasets underestimate the difference between stationary and non-stationary conditions by 9.47 mm compared to results of 73.99 mm derived from station data. Similarly, the results demonstrate less variation between stationary and non-stationary conditions in water storage, followed by a sharp variation in rainfall and moderate variation in evapotranspiration. The findings of this study indicate that neglecting the non-stationary condition in some hydrometeorological variables can lead to underestimating their amounts. This framework can be applied to any geographical area for estimating extreme conditions, providing valuable insights for infrastructure planning and design, risk assessment and disaster management.

Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions

In recent years, the rapid development of convolutional neural networks (CNNs) has significantly advanced the progress of intelligent fault diagnosis. Most currently-available CNN-based diagnostic models are built on the premise that the monitored machine operates under stable conditions. However, in real-world scenarios, rotary machinery usually operates at varying speeds, making the fault-related pulse features susceptible to noise oversaturation. To extract discriminative features from mechanical signals under non-stationary conditions, a global contextual feature aggregation network (GCFAN) is developed in this paper. To begin with, a global contextual module (GCM) is embedded in the CNN architecture to explore multimodal features. Then, a multiscale attention module (MSAM) is introduced to guide the model to focus on global and local discriminative information. Further, a multiscale feature enhancement module (MFEM) is established to enlarge the receptive field and eliminate useless features. Finally, the GCFAN architecture is constructed based on these improvements. To achieve favourable diagnostic results under fluctuating variable speed conditions, we apply the label smoothing algorithm and the AMSGrad algorithm to assist the training of the model. Two case studies using the benchmark variable speed bearing dataset and the HF-MS variable speed gearbox dataset were carried out to test the practicality of the developed approach. Experimental results demonstrated that the developed GCFAN performs better than seven state-of-the-art approaches.

Evaluation of the limiting pressure and geometry of capsules in the gas turbine engine blade crack detection system under conditions of unsteadiness and temperature unevenness

The problem of optimization of the system for diagnosing damage in the blades of gas turbine engines in nonstationary conditions is considered. A model for calculating the pressure inside a capsule with an ionizing substance is proposed, taking into account the centrifugal gas forces and the non-uniformity of the temperature field. Expressions are obtained for determining the minimum possible diameter of capsules placed in the blade body in order to increase the effectiveness of the crack detection system. It was established that the pressure in the volume of the capsule varies exponentially in the radial direction with the highest value in the peripheral section of the blade. Keywords: turbine blade, centrifugal force, temperature non-uniformity, non-stationarity ivan_andrianov_90@mail.ru, el.chep@bk.ru

Application of Incoherent Multi-Frequency Signals for Information Transmission in a Nonstationary Hydroacoustic Environment

A class of digital incoherent methods of information transmission based on multi-frequency signals for mobile underwater complexes is presented, which does not require regular and accurate estimation of channel parameters and is a priori resistant to various types of interference in nonstationary hydroacoustic communication channels, and is oriented to solving communication and navigation problems for underwater robotic complexes. The limitation of spectral efficiency of such multi-frequency systems of information transmission by the value of 0.5 bits/s/Hz in various frequency ranges, nonstationary hydrological conditions, with achievement of the maximum range at acceptable levels of error probability during information decoding is shown. The operability of the proposed class of multi-frequency multiplexing methods was confirmed by numerical and in situ sea experiments on the shelf at distances from 2.5 to 7 km with the mutual drifting of ships and with sea waves of up to 3 points.

An information-induced fault diagnosis framework generalizing from stationary to unknown nonstationary working conditions

An information-induced fault diagnosis framework generalizing from stationary to unknown nonstationary working conditions

Forecasting the Dynamic Response of Rotating Machinery under Sudden Load Changes

This paper analyzes vibration data that shows sudden amplitude changes due to non-stationary load conditions. The data were recorded in a wind turbine that operated under gusty winds and showed high peaks during short periods. Data were analyzed with the auto-regressive integrated moving average (ARIMA) algorithm, and the results were compared to the exponential forecasting method. Other methods have been applied for forecasting vibration data, but the simplicity of this method makes it suitable for rotating machinery with high variable loading conditions. The analysis of the method’s parameters is included in this paper, and the results showed that the optimum configuration depends on the data variations and the existence of significant trends. Forecasting vibration data is challenging; it depends on the source data quality, the preprocessing algorithms, and the deterioration of the mechanical elements. Predictions become less accurate when the machine operates under sudden changes, and evaluating damaging effects caused by the sudden event is difficult to estimate.

Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions.

A stochastic model for characterizing the conversion gain of Active Pixel Complementary metal-oxide-semiconductor (CMOS) image sensors (APS), assuming stationary conditions was recently presented in this journal. In this study, we extend the stochastic approach to non-stationary conditions. Non-stationary conditions occur in gated imaging applications. This new stochastic model, which is based on fundamental physical considerations, enlightens us with new insights into gated CMOS imaging, regardless of the sensor. The Signal-to-Noise Ratio (SNR) is simulated, allowing optimized performance. The conversion gain should be determined under stationary conditions.

Assessment and improvement of the accuracy of tacholess instantaneous speed estimation

Accurately estimating instantaneous angular speed (IAS) is crucial for effective fault diagnosis under nonstationary operating conditions. Although numerous methods have been developed to improve the accuracy of IAS estimation, finding a suitable technique (method and vibration component) for a specific application remains challenging. This is because accurately assessing the accuracy of the estimated IAS without a tachometer is not straightforward. Thus, since the accuracy of the estimated IAS is unknown, its accuracy cannot be further improved.To address the challenge of accurately estimating and further improving the accuracy of IAS without a tachometer, this study proposes a method to enhance the accuracy of IAS obtained from different techniques (methods and vibration components). The study first investigates the influence of IAS error on order analysis. Next, it discusses three indicators that can assess the accuracy of the estimated IAS without a tachometer. These indicators are designed to compare the energy of the deterministic and random parts of the signal, assess the sparsity of the order spectrum, and quantify the smearing of the vibration component of interest on the order spectrum. The signal and all IASs are divided into windowed sections to determine the most accurate IAS in each section using the three indicators separately or a resultant indicator obtained by merging them using the entropy weighting method. The most accurate IAS are then merged together to obtain the resultant IAS.The study finds that the indicators can effectively evaluate the accuracy of the IAS without a tachometer. The proposed method also produces a more accurate IAS than the IAS candidates obtained by different techniques.

Programmable self-heating synthesis of ultrafine RuMn oxide nanoclusters for efficient oxygen evolution

The fabrication processes of self-supported OER electrode with enhanced flexibility and universality hold great significance for meeting the practical requirements on high throughput and compatibility in industrial scenarios. The electro-thermal conversion under non-stationary equilibrium conditions is expected to achieve surface composition and morphological modulation of metallic nanostructured catalysts. Herein, a programmable self-heating strategy is reported to fabricate carbon-based ultrafine amorphous RuMn-oxide nanoclusters supported on nickel foam substrate, in which rapid surface conduction and non-equilibrium thermal treatment facilitated efficient chemical immobilization and oxidation. The obtained RuMn/XC-30W sample displays a superior electrocatalytic performance toward oxygen evolution, reaching current densities of 10 and 100 mA cm−2 at an extraordinarily low overpotential of 194 and 270 mV in 1 M KOH, along with a small Tafel slope of 67.17 mV dec−1. The long-term stability could also be certified by the minimal activity degradation under 24 h of operation. The excellent electrocatalytic performance are attributed to the strong electronic interactions and increased exposure of electrochemically active sites generated by rapid self-heating thermal process. This strategy presents a novel avenue for the design of self-supported electrodes in water electrocatalysis and various sustainable energy applications.

Dynamic recommendation algorithms for a COVID-19 restrictions scenario in the restaurant industry

研究目的利用关于不同国家餐厅及其顾客反馈的数据, 我们探索了以下问题：(i) 在餐饮行业, 用户行为和偏好在COVID-19限制期间如何改变, (ii) 这些变化如何影响推荐算法的性能, 以及 (iii) 可以提出哪些方法来改进封锁情景下的餐厅推荐质量。研究方法为了评估用户行为和偏好的变化, 本研究进行了定量和定性数据分析, 对比了COVID-19限制期前后的情况。为了评估非稳态环境中餐厅推荐系统的性能, 我们测试了最先进的协同过滤算法。我们提出并研究了一种基于过滤的方法, 以提高封锁情景下推荐算法的质量。研究发现研究发现, 在COVID-19限制期间, 平均评分和评论数量发生了变化。实验研究证实：(i) 在COVID-19限制期间, 所有最先进的餐厅行业推荐系统的性能显著下降; (ii) 使用滑动窗口和后过滤方法可以改进非稳态环境下餐厅推荐的准确性和稳定性。实践意义我们提出了两种新方法：基于CatBoost分类模型的关闭餐厅后过滤和滑动窗口方法。这些方法可以应用于经典的协同过滤推荐算法, 并在非稳态条件下提高指标值。这些方法对于推荐系统的开发者和大规模餐厅和酒店聚合平台都有帮助。因此, 这对于应用的最终用户和企业主都有好处, 因为当用户得到良好的推荐时, 他们会诚实地对餐厅进行评价, 而不会因为外部因素降低评分。研究创新本文首次提供了COVID-19限制对不同国家餐厅推荐系统有效性影响的广泛多方面的实验研究, 并提出和验证了两种解决餐厅推荐性能下降问题的新方法。

Fatigue strength estimation of a CF/PEKK composite through self-heating temperature analysis using cyclic bending tests at 20 kHz

Very high cycle fatigue (VHCF) testing at conventional frequencies is highly time-consuming and unrealistic for large sample sizes. Though testing at ultrasonic frequencies can accelerate the testing, any undesirable thermal effect arising from testing at 20 kHz can cause significant heat generation in the specimens, increasing the likelihood of thermally induced failure. When mitigated, the temperature behavior during ultrasonic fatigue testing may conversely provide knowledge of the fatigue behavior of the composite. We study this hypothesis in this work by estimating the fatigue strength of a CF/PEKK composite through its temperature evolution during 3-point bending tests at 20 kHz with different cyclic load amplitudes. The fatigue strength of the composite was estimated using the temperature characteristics from an increasing cyclic amplitude test. This estimation was validated with four constant cyclic amplitude tests. Further, the estimation model was extended to different pulse–pause sequences by two constant cyclic amplitude tests. The results indicate a reliable and rapid method for fatigue life estimation as a material property. Finally, the criticality of the self-heating phenomenon during the non-stationary self-heating condition was evaluated.

A Bearing Fault Diagnosis Method Based on Improved Mutual Dimensionless and Deep Learning

Under nonlinear and non-stationary dynamic conditions, the fault diagnosis methods based on multi-dimensional dimensionless indicators (MDI) often cannot provide effective and accurate health monitoring in the fault diagnosis of petrochemical units. In view of the above problems, this paper preprocesses the dynamic signal and reconstructs a new dimensionless indicator. The indicator combines Complementary Ensemble Empirical Mode Decomposition (CEEMD) with MDI, named as Complementary Ensemble multi-dimensionless indicators (CEMDI). By using the sequential mapping method, the CEMDI processed data can be converted into gramian angular fields (GAF). In processing sparse data, the advantages of convolutional neural networks (CNNs) were used to identify different fault types. The method is validated using three datasets, motor bearing data provided by the Case Western Reserve University, multistage centrifugal fan data and machinery failure prevention technology challenge data. Compared with the traditional dimensionless index method and the latest published dimensionless methods in the literature, the fault diagnosis methods based on CEMDI and CNN exhibits good performance in identifying fault types under different conditions, which verifies its effectiveness and superiority.

Stationary and non-stationary temperature-duration-frequency curves for Australia

Australian summer heat events have become more frequent and severe in recent times. Temperature-duration-frequency (TDF) curves connect the severity of heat episodes of various durations to their frequencies and thus can be an effective tool for analysing the heat extremes. This study examines Australian heat events using data from 82 meteorological stations. TDF curves have been developed under stationary and non-stationary conditions. Generalised Extreme Value (GEV) distribution is considered to estimate extreme temperatures for return periods of 2, 5, 10, 25, 50 and 100 years. Three major climate drivers for Australia have been considered as potential covariates along with Time to develop the non-stationary TDF curves. According to the Akaike information criterion, the non-stationary framework for TDF modelling provides a better fit to the data than its stationary equivalent. The findings can be beneficial in offering new information to aid climate adaptation and mitigation at the regional level in Australia.

An Overview of Kriging and Cokriging Predictors for Functional Random Fields

This article presents an overview of methodologies for spatial prediction of functional data, focusing on both stationary and non-stationary conditions. A significant aspect of the functional random fields analysis is evaluating stationarity to characterize the stability of statistical properties across the spatial domain. The article explores methodologies from the literature, providing insights into the challenges and advancements in functional geostatistics. This work is relevant from theoretical and practical perspectives, offering an integrated view of methodologies tailored to the specific stationarity conditions of the functional processes under study. The practical implications of our work span across fields like environmental monitoring, geosciences, and biomedical research. This overview encourages advancements in functional geostatistics, paving the way for the development of innovative techniques for analyzing and predicting spatially correlated functional data. It lays the groundwork for future research, enhancing our understanding of spatial statistics and its applications.

An enhanced K-SVD denoising algorithm based on adaptive soft-threshold shrinkage for fault detection of wind turbine rolling bearing

Due to nonstationary operating conditions of wind turbines and surrounding harsh working environments, the impulse features induced by bearing faults are always overwhelmed by heavy noise, which brings challenges to accurately detect rolling bearing faults. Sparse representation exhibits excellent performance in nonstationary signal analysis, but it is closely bound up with the degree of similarity between the atoms in a dictionary and signals. Therefore, this paper investigates an enhanced K-SVD denoising method based on adaptive soft-threshold shrinkage to achieve high-precision extraction of impulse signals, and applies it to fault detection of generator bearing of wind turbines. An adaptive sparse coding shrinkage soft-threshold denoising is first proposed to remove noise and harmonic interference in the residual term of dictionary updating, so that the updated atoms show obvious impact characteristics. Furthermore, a soft-threshold shrinkage function with adaptive threshold is designed to further suppress clutter in atoms of the learned dictionary, so as to obtain an optimized dictionary for recovering impulse signals. Two actual engineering cases are selected for analysis, and the envelope spectrum correlation kurtosis corresponding to the results obtained by the proposed method is significantly higher than that of other comparison methods, thus verifying its superiority in detecting rolling bearing faults.

Dynamic characteristics and energy efficiency evaluation of a novel solar seasonal thermal storage - heating system

This paper proposes solar seasonal thermal energy storage system compounded with long-term and short-term energy storage tanks for a single-family dwelling, which using assisted water source heat pump to further improve the systems stability. With the objective of improving the solar energy utilization efficiency and reducing the primary energy consumption, the dynamic simulation model of the “heat collection-heat storage-heat supply” system under the non-stationary boundary conditions throughout the year is established. The energy transfer and conversion law of each subsystem and the dynamic operation characteristics are analyzed through simulation method. The system cooperative control strategy is proposed and parameter optimization is carried out. Results indicate that the annual heat collection efficiency of the water source heat pump composite system has been improved by 2.2 % compared with the electric auxiliary heating system. Meanwhile, the heat storage efficiency and the solar energy guarantee rate increased by 5.8 % and 8.3 %, respectively. Moreover, the optimal water source heat pump composite system not only improves energy efficiency and achieves the cost savings compared with the electric auxiliary heating system, but also reduces carbon dioxide emission by 10.3 %, which have great advantages for energy saving and emission reduction.