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In the field of aerospace, real-time and accurate monitoring of the milling state is of great significance for improving processing quality and processing efficiency. Relying on milling force signals to monitor tool wear state has problems such as high cost and limited size of machined parts. Aiming at the above problem, a milling cutter wear condition monitoring method based on spindle current is proposed. Firstly, based on the linear correlation between the milling force and the current increment theory, the milling experiment was carried out. The characteristic variation trend and spectral characteristics of the milling force signal and spindle current signal under the change of spindle speed, feed rate, and radial cutting width are analyzed. The cosine similarity demonstrates that the similarity of the root mean square values of the two is 0.9865, 0.9943, and 0.9421, respectively, and the changing trend of the spectral amplitude is consistent. In the iSESOL platform, a self-learning feature boundary model is built from learning data, signal interception, root mean square feature extraction, drawing boundary, and threshold judgment to monitor the wear state of the milling cutter online. Finally, the data in each wear stage are selected to verify the model, and the validity of the model is proved by setting the threshold beyond the boundary and the alarm trigger condition, in real-time, and accurately monitoring the wear state of the milling cutter online. [ABSTRACT FROM AUTHOR]

Industrial robot machining systems are used extensively in processing and manufacturing sectors including aerospace, large ships, rail transit, engineering machinery, semiconductor processing, medical equipment, and new energy vehicles. However, chatter during the machining process can considerably diminish the machining accuracy and surface quality. In severe cases, it can cause damage to the tool and the machining robot. Robot machining chatter research is required to improve the stability of the machining process for high-efficiency, high-quality robotic machining. This review comprehensively summarizes research progress in three areas: robot machining chatter mechanisms, chatter stability prediction and online monitoring, and chatter suppression strategies. First, the generation mechanisms of regenerative chattering and modal-coupling chattering are clarified. Second, the methods of chattering stability prediction and online monitoring of robot machining are summarized. The differences between passive suppression, semi-active suppression, and active suppression strategies are explained. Existing problems and future directions in robot machining chattering research are summarized and prospected. [ABSTRACT FROM AUTHOR]

Online monitoring of lanthanide fission product concentrations has been a great challenge in the pyrochemical reprocessing of spent nuclear fuels. Electrochemical techniques, with the advantages of accuracy and timeliness, provide a feasible strategy. In this work, the relationship between lanthanum ion (La(III)) concentration and reaction current was investigated by various electrochemical methods in LiCl–KCl eutectic salt. Tafel test results show that there is a linear relationship between the exchange current density (i0) and ion concentration (i0 = 0.06618x + 0.04409). In addition, according to the comparative analysis of electrochemical test results, it is found that anodic stripping voltammetry is a potential ion concentration detection technology. The peak current (ipc) and ion concentration satisfy the relationship: ipc = 0.08503x − 0.02245. According to the equation, the calculated La(III) concentration is almost consistent with that analyzed by inductively coupled plasma atomic spectrometer (ICP-AES), which provides a plausible way for ion concentration monitoring in high-temperature molten salt. [ABSTRACT FROM AUTHOR]

In atmospheric pressure chemical ionization mass spectrometry (APCI-MS), [M−3H+H2O]+ ions can deliver analyte-specific signals that enable direct analysis of volatile n-alkane mixtures. The underlying ionization mechanisms have been the subject of open debate, and in particular the role of water is insufficiently clarified to allow for reliable process analytics when the humidity level changes over time. This can be a problem, particularly in online monitoring, where analyte accumulation in the ion source can also occur. Here, we investigated the role of water during APCI-MS of volatile n-alkanes by changing the carrier gas for sample injection from a dry to a wetted state as well as by using 18O-labeled water. This allowed for a distinction between gaseous and surface-adsorbed water molecules. While adsorbed water seems to be responsible for the desired [M−3H+H2O]+ signals through surface reactions with the analyte molecules, gaseous water was found to promote the formation of CnH2n+1O+ of different (and analyte-independent) hydrocarbons, revealing a reaction with hydrocarbon species which accumulated in the ion source during continuous operation. At the same time, gaseous water competed with analyte molecules for ionization and thus suppressed the formation of alkyl (CnH2n+1+) and alkenyl (CnH2n−1+) ions. The results reveal a memory effect due to hydrocarbon adsorption, which may cause severe interpretation difficulties when the ionization chamber undergoes sudden humidity changes. The use of [M−3H+H2O]+ for n-alkane analysis in alkane/water mixtures can be facilitated by constantly maintaining high humidity and hence stabilizing the ionization conditions. [ABSTRACT FROM AUTHOR]

A key aspect of sustainable bioeconomy is the recirculation of renewable, agricultural waste streams as substrates for microbial production of high-value compounds. One approach is the bioconversion of corn stover, an abundant maize crop byproduct, using the fungal maize pathogen Ustilago maydis. U. maydis is already used as a unicellular biocatalyst in the production of several industrially-relevant compounds using plant biomass hydrolysates. In this study, we demonstrate that U. maydis can grow using untreated corn stover as its sole carbon source. We developed a small-scale bioreactor platform to investigate U. maydis processing of corn stover, combining online monitoring of fungal growth and metabolic activity profiles with biochemical analyses of the pre- and post-fermentation residues. Our results reveal that U. maydis primarily utilizes soluble sugars i.e., glucose, sucrose and fructose present in corn stover, with only limited exploitation of the abundant lignocellulosic carbohydrates. Thus, we further explored the biotechnological potential of enhancing U. maydis´ lignocellulosic utilization. Additive performance improvements of up to 120 % were achieved when using a maize mutant with increased biomass digestibility, co-fermentation with a commercial cellulolytic enzyme cocktail, and exploiting engineered fungal strains expressing diverse lignocellulose-degrading enzymes. This work represents a key step towards scaling up the production of sustainable compounds from corn stover using U. maydis and provides a tool for the detailed monitoring of the fungal processing of plant biomass substrates. [ABSTRACT FROM AUTHOR]

Online monitoring of public places could prevent the occurrence of violent actions, while the efficiency of utilized methods plays a key role in detecting abnormal events in a short time. Even though some methods have been dedicated to violence detection, all exploit raw video information. Since information is usually transmitted in a compressed format, utilizing available compressed domain data, which obviates the need for further feature extraction, is advantageous. This paper presents a compressed domain method through which residual information obtained by partial decoding is utilized as spatiotemporal features instead of other raw-domain time-consuming approaches. Using residual data enables us to save time in the laborious feature extraction step, which is one of the imperative factors in real-time applications. Furthermore, a new approach for accumulating similar adjacent residuals has been presented, which can considerably drop the number of processed frames for detection. Comparing the proposed methods with the latest state-of-the-art methods reveals their high effectiveness. The entire implementation of this work can be found at https://github.com/hnarges91/violence-detection. [ABSTRACT FROM AUTHOR]

This study considers the online monitoring problem for detecting the parameter change in time series of counts. For this task, we construct a monitoring process based on the residuals obtained from integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models. We consider this problem within a more general framework using martingale difference sequences as the monitoring problem on GARCH-type processes based on the residuals or score vectors can be viewed as a special case of the monitoring problems on martingale differences. The limiting behavior of the stopping rule is investigated in this general set-up and is applied to the INGARCH processes. To assess the performance of our method, we conduct Monte Carlo simulations. A real data analysis is also provided for illustration. Our findings in this empirical study demonstrate the validity of the proposed monitoring process. [ABSTRACT FROM AUTHOR]

In an internal combustion (IC) engine, the failure of engine parts due to friction and wear is a serious issue. This can be reduced by employing suitable surface modification techniques such as thermal barrier coatings, laser-assisted surface engineering techniques, carbon nanotubes, etc. These techniques help to decrease the frictional forces and improve the tribological, mechanical and corrosion-resistant properties of the engine components. This paper highlights the distinct causes of the failure of engine parts and the role of different surface modification techniques in improving the tribological and mechanical properties of the engine parts. After that, the preventive measures for engine parts failure including online condition monitoring and maintenance strategies are presented. Finally, the future scope and challenges are discussed. [ABSTRACT FROM AUTHOR]

The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers. Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. The model is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) µm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity. [ABSTRACT FROM AUTHOR]

Online monitoring of dynamic chemical processes involving a wide volatility range of hydrocarbon species is challenging due to long chromatographic measurement times. Mass spectrometry (MS) overcomes chromatographic delays. However, the analysis of n-alkane mixtures by MS is difficult because many fragment ions are formed, which leads to overlapping signals of the homologous series. Atmospheric pressure chemical ionization (APCI) is suitable for the analysis of saturated hydrocarbons and is the subject of current research. Still, although APCI is a "soft ionization" technique, fragmentation is typically inevitable. Moreover, it is usually applied for liquid samples, while an application for online gas-phase monitoring is widely unexplored. Here, we present an automated APCI-MS method for an online gas-phase analysis of volatile and semi-volatile n-alkanes. Mass spectra for n-heptane and n-decane reveal [M-H]+, [M-3H]+ and [M-3H+H2O]+ as abundant ions. While [M-H]+ and [M-3H]+ show an excessive fragmentation pattern to smaller CnH2n+1+ and CnH2n-1+ cations, [M-3H+H2O]+ is the only relevant signal within the CnH2n+1O+ ion group, i.e., no chain cleavage is observed. This makes [M-3H+H2O]+ an analyte-specific ion that is suitable for the quantification of n-alkane mixtures. A calibration confirms the linearity of C7 and C10 signals up to concentrations of ~1000–1500 ppm. Moreover, validated concentration profiles are measured for a binary C7/C10 mixture and a five-alkane C7/C10/C12/C14/C20 mixture. Compared to the 40-min sampling interval of the reference gas chromatograph, MS sampling is performed within 5 min and allows dynamic changes to be monitored. [ABSTRACT FROM AUTHOR]

So far, this pandemic has severely affected the education sector. As education undergoes a brilliant transformation with advancing technology, the digital acquisition of knowledge has yet to find widespread use - virtual exams. Faraway Proctoring offers several advantages of using manual and primarily based technology. Although this allows students to take an exam in any field with specific technical requirements, it eliminates the need for physical research centers. It is cost-effective and easy to plan, which can be challenging to manage, especially during aggressive trials. Finally, the paper discusses the performance characteristics of different styles of web-based inspection systems, along with their limitations and challenges. [ABSTRACT FROM AUTHOR]

Retrospective analyses of experience sampling (ESM) data have shown that changes in mean and variance levels may serve as early warning signs of an imminent depression. Detecting such early warning signs prospectively would pave the way for timely intervention and prevention. The exponentially weighted moving average (EWMA) procedure seems a promising method to scan ESM data for the presence of mean changes in real-time. Based on simulation and empirical studies, computing and monitoring day averages using EWMA works particularly well. We therefore expand this idea to the detection of variance changes and propose to use EWMA to prospectively scan for mean changes in day variability statistics (i.e., s 2 , s , ln(s )). When both mean and variance changes are of interest, the multivariate extension of EWMA (MEWMA) can be applied to both the day averages and a day statistic of variability. We evaluate these novel approaches to detecting variance changes by comparing them to EWMA-type procedures that have been specifically developed to detect a combination of mean and variance changes in the raw data: EWMA- S 2 , EWMA-ln( S 2 ), and EWMA- X ¯ - S 2 . We ran a simulation study to examine the performance of the two approaches in detecting mean, variance, or both types of changes. The results indicate that monitoring day statistics using (M)EWMA works well and outperforms EWMA- S 2 and EWMA-ln( S 2 ); the performance difference with EWMA- X ¯ - S 2 is smaller but notable. Based on the results, we provide recommendations on which statistic of variability to monitor based on the type of change (i.e., variance increase or decrease) one expects. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) is widely used for elemental analysis. However, its application for monitoring and analyzing a laser machining process by examining the changes in spectral information warrants further investigation. In this study, we investigate the effect of laser parameters on the spectra, variations in the time-resolved plasma emission spectra, and the relationship between the morphology of craters and plasma plume evolution during the femtosecond (fs) laser ablation of sapphire. The Boltzmann plot method and Stark's broadening model are employed to estimate the temporal temperature and electron density of the plasma plume, revealing the process of plasma evolution. This study aims to demonstrate the feasibility of LIBS for online monitoring of laser processing through experimental data and theoretical explanations. [ABSTRACT FROM AUTHOR]

Grinding is an important part of precision manufacturing, and grinding wheel wear is a crucial factor affecting the processing quality. It will be of great significance to monitor the grinding wheel wear status in real-time and provide timely warnings during processing. To realize the real-time and effective monitoring of grinding wheel wear, a new grinding wheel wear state monitoring method based on the principle of electrostatic induction is proposed. The principle of electrostatic induction in the machining process is analyzed, and two methods of grinding wheel wear surface monitoring and chip-abrasive grain monitoring are proposed. The time domain signal is processed by Fourier transform and noise reduction, and the influence of grinding wheel wear on the electrostatic signals of grinding wheel, abrasive grains, and chips is analyzed. The results of titanium alloy grinding show that with the increase in the wear degree of the grinding wheel, the electrostatic quantity of the grinding wheel increases by 5.6 times, and the number of electrostatic pulses of abrasive grains decreases by 74%. The degree of grinding wheel wear can be characterized by the electrostatic quantity of the grinding wheel and the number of electrostatic pulses of abrasive grains. In contrast, the number of electrostatic pulses of chips, the electrostatic pulse peaks of abrasive grains, and chips have no apparent relationship with grinding wheel wear. The electrostatic monitoring method can effectively monitor the grinding wheel wear state in real time and provide a new solution for the online monitoring of grinding wheel wear. [ABSTRACT FROM AUTHOR]

In this study, we consider an online monitoring procedure to detect a parameter change for bivariate time series of counts, following bivariate integer-valued generalized autoregressive heteroscedastic (BIGARCH) and autoregressive (BINAR) models. To handle this problem, we employ the cumulative sum (CUSUM) process constructed from the (standardized) residuals obtained from those models. To attain control limits, we develop limit theorems for the proposed monitoring process. A simulation study and real data analysis are conducted to affirm the validity of the proposed method. [ABSTRACT FROM AUTHOR]

We investigate online monitoring algorithms over dense-time and continuous-time signals for properties written in metric temporal logic (MTL). We consider an abstract algebraic semantics based on complete lattices. This semantics includes as particular cases the standard Boolean (qualitative) semantics and the widely used real-valued robustness (quantitative) semantics. Our semantics also extends to truth values that are partially ordered and allows the modeling of uncertainty in satisfaction. We propose a compositional approach for the construction of online monitors that transform exact representations of piecewise constant (dense-time and continuous-time) signals. These monitors are based on a class of infinite-state deterministic signal transducers that (1) are allowed to produce the output signal with some bounded delay relative to the input signal, and (2) do not introduce unbounded variability in the output signal. A key ingredient of our monitoring framework is an efficient algorithm for sliding-window aggregation over dense-time signals. We have implemented and experimentally evaluated our monitoring framework by comparing it to the recently proposed online monitoring tools Reelay and RTAMT. [ABSTRACT FROM AUTHOR]

Real-time online monitoring of volatile organic compounds (VOCs) in ambient air is crucial for timely and effective human health protection. Here, we developed an innovative, automated two-staged adsorption/thermal desorption gas chromatography/mass spectrometry (GC/MS) system for real-time online monitoring of 117 regulated volatile organic compounds (VOCs). This system comprised a sampling unit, water management trap, two-staged adsorption/thermal desorption unit, thermoelectric coolers (TECs), and a commercial GC/MS system. By implementing a micro-purge-and-trap (MP & T) step and a two-staged adsorption/thermal desorption unit, the presence of interfering substances was effectively minimized. The utilization of a heart-cutting GC, combined with a single MS detector, facilitated the precise separation and detection of 117 C2–C12 VOCs, while circumventing the identification and coelution challenges commonly associated with traditional GC-FID or GC-FID/MS methods. The performance of our newly developed online system was meticulously optimized and evaluated using standard gas mixtures. Under optimal conditions, we achieved impressive results, with R2 values ≥ 0.9946 for the standard linear curves of all 117 VOCs, demonstrating a precision (RSD) ranging from 0.2% to 6.4%. When applied in the field monitoring, the concentration drifts for 10 ppbv standard gas mixtures were 0.01–5.64% within 24 h. Our study developed a system for online monitoring of 117 atmospheric VOCs with relatively high accuracy and robustness. [ABSTRACT FROM AUTHOR]

Design principles are developed for online monitoring and vibroacoustic diagnostics in milling complex surfaces. The vibroacoustic signal is very sensitive when the mill's cutting edges pass through the machining zone. The influence of the mill trajectory on the surface roughness is assessed. [ABSTRACT FROM AUTHOR]

The study presents statistical procedures that monitor functioning of items over time. We propose generalized likelihood ratio tests that surveil multiple item parameters and implement with various sampling techniques to perform continuous or intermittent monitoring. The procedures examine stability of item parameters across time and inform compromise as soon as they identify significant parameter shift. The performance of the monitoring procedures was validated using simulated and real-assessment data. The empirical evaluation suggests that the proposed procedures perform adequately well in identifying the parameter drift. They showed satisfactory detection power and gave timely signals while regulating error rates reasonably low. The procedures also showed superior performance when compared with the existent methods. The empirical findings suggest that multivariate parametric monitoring can provide an efficient and powerful control tool for maintaining the quality of items. The procedures allow joint monitoring of multiple item parameters and achieve sufficient power using powerful likelihood-ratio tests. Based on the findings from the empirical experimentation, we suggest some practical strategies for performing online item monitoring. [ABSTRACT FROM AUTHOR]

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Anaerobic ammon ium oxidizing (anammox) bacteria oxidize ammonium and reduce nitrite, producing N2, and could play a major role in energy-optimized wastewater treatment. However, sensitivity to various environmental conditions and slow growth currently hinder their wide application. Here, we attempted to determine online the effect of environmental stresses on anammox bacteria by using an overnight batch activity test with whole cells, in which anammox activity was calculated by quantifying N2 production via headspace-pressure monitoring. A planktonic mixed culture dominated by "Candidatus Kuenenia stuttgartiensis" strain CSTR1 was cultivated in a 30-L semi-continuous stirring tank reactor. In overnight resting-cell anammox activity tests, oxygen caused strong inhibition of anammox activity, which was reversed by sodium sulfite (30 µM). The tested antibiotics sulfamethoxazole, kanamycin, and ciprofloxacin elicited their effect on a dose-dependent manner; however, strain CSTR1 was highly resistant to sulfamethoxazole. Anammox activity was improved by activated carbon and Fe2O3. Protein expression analysis from resting cells after anammox activity stimulation revealed that NapC/NirT family cytochrome c (KsCSTR_12840), hydrazine synthase, hydrazine dehydrogenase, hydroxylamine oxidase, and nitrate:nitrite oxidoreductase were upregulated, while a putative hydroxylamine oxidoreductase HAO (KsCSTR_49490) was downregulated. These findings contribute to the growing knowledge on anammox bacteria physiology, eventually leading to the control of anammox bacteria growth and activity in real-world application. Key Points: • Sulfite additions can reverse oxygen inhibition of the anammox process • Anammox activity was improved by activated carbon and ferric oxide • Sulfamethoxazole marginally affected anammox activity [ABSTRACT FROM AUTHOR]

CMT cycle-step (CMT-CS) is a novel mode in CMT welding. It has the characteristics of minimal heat input, droplet size control and a high degree of automation. This work proposed a torch-offset CMT-CS welding method to join 5052 Al alloy to T2 copper with Al-12Si filler in different joint configurations. The influence of joint configuration was investigated, in terms of weld heat input, weld appearance, weld defect, interfacial microstructure, joint mechanical properties and fracture mode. The results indicated that offset distance to copper increased the weld heat input and promoted the wettability of Al-Si liquid filler wire on the substrate. The change of offset distance and joint configuration will change the melting amount and heat transfer form of copper, thus changing the composition of the reaction layer. The average joint strength of the Al/Cu (Aluminum alloy on top) joint was slightly higher than that of the Cu/Al (pure copper on top) joint. The maximum values were 129 N/mm and 127 N/mm, respectively. As the offset increases, the joint failure shifts from the fusion zone to the interface; moreover, the fracture mode was changed from brittle fracture to ductile fracture. [ABSTRACT FROM AUTHOR]

Different linear transfer functions (TFs) models are compared to the traditional non-linear rating curve (RC) model in a novel event-based exploratory frame. This approach aims to scrutinize for alternative representations of the stormwater TSS load dynamics at the output of a 185-ha urban catchment, when not considering explicitly a virtual state of available pollutant mass accumulated during the dry weather and subsequently washed by rainfall (as a simplification of the widespread accumulation/wash-off model). The advantages of using TFs remain unproved compared to the RC model, which shows more performant results in terms of parsimony and simulation uncertainties with a dataset of 255 calibration and 110 verification online-monitored rainfall events. This can be explained by the non-linear properties of RC. Modeling performance when rainfall is used as the input remains lower than for the case of flow rate (from mean NS of 0.6 to 0.4 in verification events). On the other hand, the RC by itself can be still considered an unsatisfactory model (e.g., NS about 0.6 in verification with the flow rate as input), suggesting the lack of an essential missing process in this formulation. However, this missing process (if there is one) might not be necessarily linked to a time-decreasing state of available pollutant mass. [ABSTRACT FROM AUTHOR]

An online intelligent band saw blade wear status monitoring system based on multi-feature fusion of acoustic emission signals is proposed to address the problem about sawing efficiency and quality degradation because of band saw blade wear. Firstly, the acoustic emission raw signal is obtained through the whole life cycle experiment. A variety of features highly correlated to the wear state of the band saw blade are extracted through the study on the wear mechanism of the band saw blade. Secondly, a cost-sensitive support vector machine based multi-feature fusion model of acoustic emission signals is established to implement real-time determination of the wear status of band saw blades. A database for online monitoring of the wear status of band saw blades was established considering the diversity and complexity of sawing parameters, processing materials, and saw machine models in different sawing scenarios. An online intelligent monitoring system based on LabVIEW and MATLAB are established for band saw blade wear status. Validation experiment results show that the online monitoring can achieve an accuracy of 97.71%. [ABSTRACT FROM AUTHOR]

The electrode state is an important factor affecting the welding quality in the process of resistance spot welding, but there is still lack of an effective method to monitor the electrode wear state. In this paper, a novel online monitoring method of electrode wear state is proposed by exploring the variation pattern of dynamic resistance. The evaluation method of time series similarity is important for dynamic resistance data processing, and two types of evaluation methods including static evaluation method and dynamic evaluation method are proposed in this paper. The static evaluation methods include shape change factor, dynamic resistance decrease ratio and peak time delay, and the dynamic evaluation method refers to the trend change factor. The welding process parameters and the welding material are kept unchanged during the experiment, the newly polished electrode is used to weld 1300 times continuously, and dynamic resistance is collected. According to the results of data processing, the change of the electrode state can be divided into three stages: The electrode state is stable when the welding number is less than 360. When the welding number is in the range of 360–800, the electrode state is in the transition stage, and the electrode state rapidly deteriorates with the increase of the welding number. When the welding number is more than 800, the electrode state is completely deteriorated, and the electrode needs to be dressed. This study may pave the way for online monitoring of electrode wear state. [ABSTRACT FROM AUTHOR]

The detection of cracks in spot welding is a major issue especially online. This paper deals with this topic using acoustic emission monitoring. It proposes an approach based on Shannon entropy with a novel criterion that allows increasing the detection sensitivity. A data-based model is built, coded, and then applied on acoustic emission signals. The obtained crack detection results are satisfactory. The cracks are revealed using fluorescent dye penetrant, which enables validating the method. A flowchart of whole the proposed approach is described so that practitioners can benefit from the authors experience, towards successful implementation in laboratory as well as in production line. The current approach is applied on resistance spot welding as a case of study, but it could be applicable in nonstationary environment such as other kinds of spot welding, and more hopefully for other manufacturing processes. [ABSTRACT FROM AUTHOR]

Online control of industrial processes by lean principle increases productivity and yields higher product quality. Polysaccharides are dissolved in liquids, such as water, in many industrial products, like paints, cosmetics and culinary products. In these products, it is important to control viscosity or create thixotropy and yield stress for product functionality. Electromagnetic field and electrical conductivity techniques were applied to a meter polysaccharide dissolution process online up to a 0.5 wt% concentration, and the resultant solution was also further tested by UV–Vis spectrophotometry. The electromagnetic field technique measures changes in the local electrical permittivity of the liquid and the interest in this research were to find out whether the changes correlate to the concentration changes during the dissolution of polymer polysaccharide. The results that were obtained showed good consistency, suggesting the feasibility of the electromagnetic field technique in online monitoring of a polysaccharide suspension concentration. Moreover, this technique gives the advantage of instant monitoring of a polysaccharide dissolution for improved process control. [ABSTRACT FROM AUTHOR]

Flow cytometry is a promising tool used to identify the phenotypic features of bacterial communities in aquatic ecosystems by measuring the physical and chemical properties of cells based on their light scattering behavior and fluorescence. Compared to molecular or culture-based approaches, flow cytometry is suitable for the online monitoring of microbial water quality because of its relatively simple sample preparation process, rapid analysis time, and high-resolution phenotypic data. Advanced statistical techniques (e.g., denoising and binning) can be utilized to successfully calculate phenotypic diversity by processing the scatter data obtained from flow cytometry. These phenotypic diversities were well correlated with taxonomic-based diversity computed using next-generation 16S RNA gene sequencing. The protocol provided in this paper should be a useful guide for a fast and reliable flow cytometric monitoring of bacterial phenotypic diversity in aquatic ecosystems. [ABSTRACT FROM AUTHOR]

In this investigation, the welding quality of TC2 titanium alloy with 0.4 mm thickness was predicted using two regression models and an artificial neural network model. The welding current and the voltage between the upper and lower electrodes were obtained using the Rogowski coil and a line voltage sensor. And then the variations of the dynamic resistance curve and the effects of the welding current and welding time on the dynamic resistance signals were investigated. The principal component analysis (PCA) was employed to eliminate the redundant information in the dynamic resistance curve and characterize the shape information of the entire dynamic resistance. A linear regression model quantifying the relationship between the nugget diameter and the principal components was established. The results of the analysis of variance indicated that the performance of this regression equation was very good. Some statistical characteristics of the dynamic resistance signal were also extracted to investigate the relationship between the nugget diameter and dynamic resistance. The results indicated that the regression model established based on the PCA technique was much more robust than the model developed on the basis of the features manually extracted from the dynamic resistance signal. The neural network model was also used to predict the nugget diameter of the welding joints utilizing the extracted features. The performances of the three established prediction models were compared and their behavioral discrepancies were also investigated. The PCA technique not only can minimize the prior assumptions about the certain shape of the dynamic resistance curve and remove the subjective factors caused by the manual extraction method, but it also can assess and monitor the welding quality with a good level of reliability. [ABSTRACT FROM AUTHOR]

Carrying out supervising of sawing equipment through advanced sensor is an important study in modern automatic sawing technology. The application of monitoring techniques can greatly increase the life of saw blades, lower the cost, reduce the secondary machining, and improve work quality and productivity in sawing. The acoustic emission (AE) monitoring techniques are studied mainly in this paper. Aiming at the advantages such as high sensitivity, wide application range, and good correlation, the basic principles and key technologies of AE monitoring technology are introduced. Then the study status is reviewed in the fields of online monitoring in sawing surface quality, sawing vibration, saw blade conditions, and its application in automation control technology by AE monitoring technology. The disadvantages and future development trend of AE monitoring techniques in sawing are demonstrated. [ABSTRACT FROM AUTHOR]

Soil and water loss monitoring is one of the important tasks in the construction of soil and water conservation ecology, which plays an important role in the national macro decision-making, and also has an important influence in the research of visual experience art. The existing soil and water loss monitoring facilities are out of date, the monitoring cycle is long, time-consuming, and laborious, and become less reliable, unable to achieve real-time online monitoring. Human factors have relatively great influence on the science of observation data. In this paper, based on the periodic structure and homogenization theory, for some drought resistant shrubs, grasses, and trees, we refer to the simulation technology of steel and concrete, and show the cross-section pictures of the root simulation model, and select the soil composite as the model of composite composition relationship. Through the "single package" model and numerical simulation, the root system in soil can obtain synergistic effect. In order to further confirm the accuracy of numerical results, the "single package" model is based on the periodic arrangement of root complex. Sophora root, cypress root, pine root, and loess collected from the test site were used in the experiment, and the parameters were input as "single package" model, and compared with the simulation data, which confirmed its high accuracy. Through the research on the sensor, this paper introduces the research results into the soil erosion of mountain plants and the art of visual experience, which promotes the deeper interaction of the new mass media. [ABSTRACT FROM AUTHOR]

This paper provides a new approach that simplifies the monitoring of 214Pb activity concentration on aerosols in the atmospheric surface layer. The approach allows obtaining data on 214Pb activity concentration with the discreteness of 2 h. The experimental setup described in the paper made it possible to achieve a minimum detectable activity level of 0.4 Bq m−3. Using this approach, the data on the diurnal variability of 214Pb activity concentration in the atmosphere of Sevastopol city for a period of 18 months were obtained. The 214Pb activity concentration varied from < 0.4 (less than 1% of the data series) to 8.9 Bq m−3, mean value 2.0 ± 1.0 Bq m−3. The analysis of the temporal variability of 214Pb activity concentration on different time scales (diurnal, seasonal) was carried out. Annually averaged diurnal variation curve of 214Pb activity concentration showed a peak at 6:00 local time and a minimum at 18:00. The maximum variability in the seasonal averaged diurnal cycle of 214Pb activity concentration is observed in summer (± 30% of the daily average value) and the minimum in winter (± 13%). The maximum seasonal average value of 214Pb activity concentration is observed in winter (2.5 Bq m−3) and the minimum in summer (1.4 Bq m−3). A quantitative estimate of the annual effective dose due to exposure to outdoor radon was obtained by using 214Pb data, and it was 0.03 mSv a−1. [ABSTRACT FROM AUTHOR]

Because of the popularity of electric vehicles, large-scale charging piles are connected to the distribution network, so it is necessary to build an online platform for monitoring charging pile operation safety. In this paper, an online platform for monitoring charging pile operation safety was constructed from three aspects: hardware, database, and software functions using big data and related technologies. Firstly, the hardware platform was optimized by modifying the microcontroller and charging pile sensor equipment, adjusting the connection mode of the data acquisition circuit, and optimizing the topology of the communication network. Secondly, the mathematical model of the charging pile was built with the support of hardware equipment and a database, and the online operation data of the charging pile were collected by using big data technology and monitoring indicators such as voltage deviation and electric quantity deviation was selected to calculate the specific values of monitoring indicators. Finally, by comparing with the normal data of charging piles, the visual online monitoring results of charging piles were obtained, and it is concluded that the platform can provide decision support for the safe operation of charging piles. [ABSTRACT FROM AUTHOR]

When small- and medium-sized motors are running, abnormal vibration phenomenon caused by various reasons often occur, and continuous development may cause serious motor failure. Therefore, online monitoring and warning for motor vibration is very important. Among them, the looseness of the base fixing screw is the most common and typical cause of abnormal vibration of the motor. Based on the similarity measurement theory, a new method for predicting the fault characteristics of the motor base screw loosening online vibration is proposed in this paper. Firstly, the causes of motor vibration signals are analyzed, and the vibration characteristics of different causes are classified. The vibration fault characteristics of the pedestal fixing screws are mainly studied. The vibration signal generated by the looseness of the base fixing screw is collected by the designed real-time online vibration monitoring system. Then, a fast Fourier transform of the vibration signal is carried out to obtain the corresponding amplitude and frequency characteristic curve. By using the two schemes of 'Cosine similarity and modulus ratio combination' and 'Correlation coefficient combined with vector norm,' the vibration fault characteristics are analyzed and compared, the vibration fault characteristic signal is extracted, and the vibration reason is determined and alerted by combining fault characteristic classification. Finally, this method is used to compare and test the working condition of the motor frame fixing screw loosening and the normal operation of the motor. The experimental results show that the algorithm which combines correlation coefficient with 1-norm has the advantages of simple algorithm and high accuracy. The method also provides new solutions and ideas for online warning of abnormal vibration caused by other reasons. [ABSTRACT FROM AUTHOR]

Online monitoring is the task of identifying complex temporal patterns while incrementally processing streams of data-carrying events. Existing state-of-the-art monitors for first-order patterns, which may refer to and quantify over data values, can process streams of modest velocity in real-time. We show how to scale up first-order monitoring to substantially higher velocities by slicing the stream, based on the events' data values, into substreams that can be monitored independently. Because monitoring is not embarrassingly parallel in general, slicing can lead to data duplication. To reduce this overhead, we adapt hash-based partitioning techniques from databases to the monitoring setting. We implement these techniques in an automatic data slicer based on Apache Flink and empirically evaluate its performance using two tools—MonPoly and DejaVu—to monitor the substreams. Our evaluation attests to substantial scalability improvements for both tools. [ABSTRACT FROM AUTHOR]

In order to realize the effective monitoring of non-point source pollution of water resources in river tourist attractions, improve the accuracy of monitoring results, and ensure the safety of water resources, an online monitoring method of non-point source pollution of water resources in river tourist attractions is proposed. Analyze the causes of non-point source pollution of water resources, design a water pollution data collection and transmission device based on the analysis results, and use this device to realize the synchronous collection of non-point source water pollution data. Data is transmitted through the platform to manage monitoring point information, monitoring point distribution, and realizing historical records and other monitoring information, performing the trend fitting of the monitoring data in the server to issue an early warning, and displaying the fitting results through the software client, completing the online monitoring. The experimental results show that this method has higher monitoring efficiency and better authenticity. [ABSTRACT FROM AUTHOR]

We present three types of optical ammonia sensors suitable for environmental, bioprocess, and reaction monitoring. A respective fluorescent BF2-chelated tetraarylazadipyrromethene dye (aza-BODIPYs) is physically entrapped in a polyurethane hydrogel (HydroMed D4) forming an emulsion system with vinyl-terminated polydimethylsiloxane (PDMS). The analyte-sensitive layer is covered by a hydrophobic membrane which excludes hydrophilic substances. Three different protection layers are tested, whereby the Teflon and the hydrophobic PES layers outperform a PDMS/TiO2 layer. Response times within their dynamic range of 15 s can be achieved, whereas the PDMS/TiO2-covered sensor requires at least 390 s. The three sensors entail the following concentration areas: first sensor 3 μg L−1–3 mg L−1 (LOD 0.23 μg L−1), second sensor 0.1–30 mg L−1 (LOD 28 μg L−1), and third sensor 3 mg L−1–1 g L−1 (LOD 0.51 mg L−1). Readout is performed with a commercially available phase fluorimeter combined with optical fibers. Dual-lifetime referencing (DLR) is used as referencing method and Egyptian blue acts as an inert reference material. No cross-sensitivity to pH changes can be detected. [ABSTRACT FROM AUTHOR]

The unfluctuating running of on-orbit spacecraft equipment has a decisive impact on the smooth implementation of space exploration mission. However, due to the adverse work conditions and complex running states, it is really a challenge for the online monitoring of aerospace equipment. In this paper, an improved growing neural gas method based on incremental learning is proposed, which is dedicated to solving the problem of online anomaly detection. The learning rate of the proposed method is adaptively adjusted according to the process of model training, ensuring the weights update quickly at the beginning of model construction and converge steady at the end of model training. The optimized insertion mechanisms of neurons ensure that the necessary new neurons are inserted at the right time and location dynamically, while the innovative deletion mechanisms of neurons ensure that the worthless neurons be deleted timely and at the same time guarantee the representation ability of model. The comparison results with the conventional methods on public datasets show that the proposed method achieves the better performance obviously, both in the aspects of detection accuracy and computational efficiency, respectively. At last, as a case study, the proposed method is used for online anomaly detection of a real aerospace device, i.e., a gamma ray detector, and the final F1 score of anomaly detection is as high as 98.78%. The results show that the proposed method can be applied to online detection of aerospace equipment health conditions effectively. [ABSTRACT FROM AUTHOR]

Tool wear prediction is an important research in metal cutting, which aims to improve machining accuracy and production efficiency, maximize tool utilization, and reduce machining cost. However, due to high complexity and nonlinearity of tool wear process, it is difficult to establish a general tool wear prediction model, which limits its application in industrial production. To solve this problem, an unsupervised online prediction method for tool wear values is proposed. In the method, a milling force model considering tool wear is established by using analytical method, and parameters varying with tool wear in the force model are integrated into five force model coefficients. The coefficients are solved and updated continuously using the least square estimation method according to the cutting force signals collected in real time. Based on study of relationship between the coefficients and tool wear, a tool flank wear value estimation model is constructed, combined with a time series analysis model, to achieve prediction of tool flank wear values. Experiments are conducted to test the prediction accuracy of tool wear values using the proposed method, and the results show that the average online prediction accuracy reached 72.0%, without supervision. The method has the advantages of low cost and strong adaptability, and can be used for online prediction of tool wear in machining industry. [ABSTRACT FROM AUTHOR]

Issues in the practical application of continuous monitoring tools for assessing the technical condition and identifying defects of turbine generators are considered. In order to ensure an effective monitoring solution, it is necessary to take a specific approach to individual installations. Particular attention should be paid to the generator "trouble spots," i.e., important structural elements prone to defects. Using specific examples from diagnostic examination practice, the most typical problems affecting the structural elements of various turbine generators are discussed. The current state of regulatory and technical documentation is analyzed. Inadequacies affecting some existing documents in terms of supporting the needs of operating personnel for solving contemporary problems of online monitoring and diagnostics of turbine generators are noted. [ABSTRACT FROM AUTHOR]

Fermentation processes are still compromised by a lack of monitoring strategies providing integrated process data online, ensuring process understanding, control, and thus, optimal reactor efficiency. The crucial demand for online monitoring strategies, not only encouraged by the PAT initiative but also motivated by modern paradigms such as circular economy and sustainability, has driven research and industry to provide "next-generation process technology": in other words, technology tailored toward industrial needs. Mid-infrared (MIR) spectroscopy as such is superior to near-infrared (NIR) spectroscopy since it provides significantly enhanced selectivity. However, due to high costs and a lack of instrumental robustness, MIR spectroscopy is outcompeted by NIR when it comes to industrial application. The lack of chemometric expertise, model understanding, and practical guidance might add to the slow acceptance of industrial MIR application. This work demonstrates the use of novel MIR, so-called non-linear infrared (NLIR) technology and the importance of model understanding, exemplarily investigated on a lab-scale yeast fermentation process. The six analytes glucose, ethanol, glycerol, acetate, ammonium, and phosphate were modeled by partial least squares (PLS) based on spectral data, demonstrating the potential of the novel technology facilitating online data acquisition and the necessity of investigating indirect predictions. Key points: • NLIR spectra were acquired online during a yeast fermentation process • PLS models were constructed for six components based on uncorrelated samples • Glucose, ethanol, ammonium, and phosphates were modeled with errors of less than 15% • Acetate and glycerol were shown to rely on indirect predictions [ABSTRACT FROM AUTHOR]

When a disaster occurs, a huge amount of inconsistent victim or damage information data is received by many different sources. Disaster management systems achieve the completion of a significantly vital task, which is to reduce the number of victims or amount of damage caused by a disaster, with real-time information monitoring infrastructure. A fundamental role of these systems that could help rescue teams is to make a quick and accurate decision about the region that will be affected by the disaster and the possible effects of the tragedy. Employing IoT solutions in these systems provides the possibility of rapidly and precisely orienting rescue teams to be dispatched to the disaster area and also quickly receive specific information about the effects of the disaster. To achieve this purpose, we present a post-disaster framework using the IoT communication technologies for disaster management based on the proposed crowd sensing clustering algorithm in this paper. The proposed framework provides information about the damage status of buildings with crowd density data along with efficient real-time data collection, data aggregation, and the process of monitoring dissemination stages. This framework realizes clustering of resident density by using the cellular networks and Wi-Fi connections and calculating the damage status of buildings through the designed and specifically implemented IoT unit data. Furthermore, it employs a fuzzy logic-based decision support system to manage the resources. The proposed framework, on real base stations and access points dataset, has shown significant results for identifying crowd densities with the highlighting status of buildings in the disaster area. [ABSTRACT FROM AUTHOR]

In this research, a data-driven model is developed to monitor the seam during the laser beam welding under the influence of an external magnetic field (LBW-AMF). Firstly, a visible LBW-AMF system is built for tracking the laser melting pool and keyhole. Then, the features of the laser melting pool and keyhole are extracted with image processing techniques. The approach for an ensemble of different neural networks which includes radial basis function neural network, back-propagation neural network, and generalized regression neural network is proposed to establish the correlations of the characteristics of the laser melting pool and keyhole and the welding seam. Finally, LBW-AMF experimental results are obtained to validate the performance of the proposed data-driven model. Results illustrate that the developed model can provide a reliable result for monitoring the weld bead, which could give guidance for controlling the processing parameters in real time to improve the weld quality for practical LBW-AMF. [ABSTRACT FROM AUTHOR]

Tool wear, and its online monitoring, plays an important role in increasing productivity and improving product quality. We describe an unsupervised method to monitor the wear state of milling cutter by tracking an error sequence generated by reconstructing monitoring signals from a sparse auto-encoder (SAE). The monitoring signals consist of the force and vibration signals collected during the cutting process. We establish a well-structured SAE model, which can adaptively extract the characteristics of the signal and complete the training of the model without supervision of the empirical label and investigate the reconstruction performance of the model for cutting signal. On this basis, an automatic online tool wear state identification strategy is designed to monitor the milling process. The mean reconstruction error (MRE) sequence associated with tool wear is recorded in real time by reconstructing the next signal segment from the SAE model, which is trained and updated using the current signal segment. Monitoring criteria and thresholds are recommended to automate the identification of tool wear conditions based on the filtered MRE curve. Five experiments with two different milling environments are run to confirm the feasibility of tool wear monitoring using this method, and the results show that the method can be used to monitor tool wear conditions online under different milling conditions without being supervised by any empirical labels. [ABSTRACT FROM AUTHOR]

A method is described to determine viable populations of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). The method employs aptamer-magnetic separation combined with resistivity based detection. The bacteria were separated by means of aptamer-functionalized magnetic beads. They were then quantified by measuring their growth kinetics through time-dependent conductivity changes of culture media. The time-course of growth was logged by real-time and contactless measurements that yielded starting concentrations from the duration of lag intervals prior to the log phase of growth. In pure water samples, the linear ranges for measuring E. coli and S. aureus cells are 2.5 × 103–2.5 × 108 CFU·mL−1 and 4.1 × 103–4.1 × 108 CFU·mL−1, respectively. In spiked tap water samples, the lower limits of detection are 2.3 × 104 CFU·mL−1 and 4.0 × 103 CFU·mL−1 for E. coli and S. aureus, with recoveries of 87.0–108.7% and 92.5–105.0%, respectively. The relative standard deviation of these measurements (10.0%) is below that of plate counting method (13.9%). The presence of micro/nanoparticles such as magnetic beads or selenium nanoparticles in the culture media does not interfere, unlike in case of automatted optical density monitoring. The E. coli and S. aureus cells captured on the aptamer-functionalized magnetic beads can be directly tested for their susceptibility to antibiotics. The process of magnetic separation and determination of load burden requires neither bulky, sophisticated equipment nor expensive reagents. [ABSTRACT FROM AUTHOR]

With the advent of modern genetic engineering methods, microcultivation systems have become increasingly important tools for accelerated strain phenotyping and bioprocess engineering. While these systems offer sophisticated capabilities to screen batch processes, they lack the ability to realize fed-batch processes, which are used more frequently in industrial bioprocessing. In this study, a novel approach to realize a feedback-regulated enzyme-based slow-release system (FeedER), allowing exponential fed-batch for microscale cultivations, was realized by extending our existing Mini Pilot Plant technology with a customized process control system. By continuously comparing the experimental growth rates with predefined set points, the automated dosage of Amyloglucosidase enzyme for the cleavage of dextrin polymers into d-glucose monomers is triggered. As a prerequisite for stable fed-batch operation, a constant pH is maintained by automated addition of ammonium hydroxide. We show the successful application of FeedER to study fed-batch growth of different industrial model organisms including Corynebacterium glutamicum, Pichia pastoris, and Escherichia coli. Moreover, the comparative analysis of a C. glutamicum GFP producer strain, cultivated under microscale batch and fed-batch conditions, revealed two times higher product yields under slow growing fed-batch operation. In summary, FeedER enables to run 48 parallel fed-batch experiments in an automated and miniaturized manner, and thereby accelerates industrial bioprocess development at the screening stage. [ABSTRACT FROM AUTHOR]

Online monitoring and closed-loop control of laser welding offer great possibilities for achieving better weld quality. Earlier work on visual laser welding monitoring has mainly focused on aluminum and fairly thin steel used, for example, in car production. We extend this work by focusing on the automated analysis of the phenomena present in the laser welding of thick steel, where all of the phenomena related to the weld quality are still not well understood or controlled. This paper presents the implementation, test results and analysis for weld monitoring methods implemented on a compact smart camera system. The applied embedded sensor–processor platform allows for high-speed implementation of image capture and dynamic range compression, real-time seam tracking and spatter feature extraction. The paper describes experimental results from implemented real-time algorithms for seam tracking and spatter extraction and additional off-line analysis of methods for spatter tracking and seam widening detection, which are also feasible for future online hardware implementation. The results suggest that it is possible to integrate a compact laser welding analysis system, which achieves analysis rates that are sufficient for real-time process control. [ABSTRACT FROM AUTHOR]

The basic algorithms for determining the thicknesses of layers of deposited multilayer optical coatings are discussed and compared. Using a series of model numerical experiments, the advantage of one of these algorithms—the modified T-algorithm—is demonstrated; this algorithm reduces the influence of the effect of error accumulation in the determined thicknesses of layers. [ABSTRACT FROM AUTHOR]

There has been a growing concern on how to monitor the operation conditions of flood discharge structure in recent decades. However, the online monitoring process is always interfered by ambient excitation which leads to inaccurate and uncertain structural characteristic evaluation. To mitigate the interference, a valid operation conditions monitoring method based on variance dedication rate (VDR) and permutation entropy (VDR-PE) is proposed. Firstly, a de-noising method combining wavelet threshold and empirical mode decomposition is used to remove heavy background noises, reducing the interference of ambient excitation to structural characteristic information. Then VDR method is used to realize the dynamic fusion of multi-channel vibration signals, extracting the vibration characteristic of the overall structure in an accurate and comprehensive way. Finally, permutation entropy is used to extract the entropy value of the fused signal. Through evaluating the operation conditions with coefficient of variation, the online monitoring of flood discharge structure can be realized. The effectiveness of permutation entropy algorithm on signal dynamic monitoring is validated by a simulation experiment. Furthermore, VDR-PE method is applied to Three Gorges dam to compare differences between analytical simulation and finite element simulation. The comparison results show that VDR-PE method can be applied to detect the dynamic changes and reveal the vibration characteristic of the overall structure accurately, which provides a new direction for the online monitoring of flood discharge structure. [ABSTRACT FROM AUTHOR]

The main purpose of drinking water network is to ensure safe quality of water to users. However, accidental contamination and malicious attacks in water networks can degrade the water quality. Such critical events threat the human health and lead to harmful diseases. Water utilities are concerned by the control of water quality. Since conventional methods based on laboratory analyses require several days, online control technology presents good opportunity to rapid detection of any water contamination. Devices based on smart technology have been developed for real-time control of the water quality. However, the use of these devices is recent and yet requires investigations. This paper presents feedback of the use of the smart technology in a large-scale experimentation conducted at the campus of Lille University within the European project “SmartWater4Europe”. Two devices are used: S::CAN, which measures various parameters such as Conductivity and Turbidity and EventLab, which measures the variation of the refractive index. This paper presents the implementation of these devices, data storage and management as well as analysis of recorded data. [ABSTRACT FROM AUTHOR]