On-line Monitoring Technique Research Articles

Supervised classification and fault detection in grid-connected PV systems using 1D-CNN: Simulation and real-time validation

Photovoltaic (PV) systems are prone to various faults, including short-circuit, open-circuit, partial shading, and inverter bypass diode issues, which reduce power output and can damage components. This study presents an innovative fault detection and online monitoring technique for grid-connected PV (GCPV) systems, combining Internet of Things (IoT) technology with a one-dimensional convolutional neural network (1D-CNN) deep learning approach. The method involves developing a temperature-dependent PV system model using series resistance and ideality factor, capturing real-time data from a 15kWp GCPV system with optimally placed sensors to minimize sensor count while maintaining data accuracy, and validating the model through MATLAB/Simulink simulations and real-time experiments under various fault scenarios. The collected data is used to train the 1D-CNN model to classify different fault types. The model is then implemented on an IoT platform for real-time monitoring and fault detection, displaying system status and alerts via a dashboard. The proposed system achieves a high fault detection accuracy of 98.15 % and 93.12 % during cyberattacks, with an uncertainty of ±4 %, significantly enhancing fault detection reliability and efficiency compared to existing methods. The IoT dashboard provides an effective tool for monitoring system performance and issuing alerts under abnormal conditions.

Condition monitoring and warning of a belt drive system based on a logical analysis of data regression-based residual control chart

The belt drive system is commonly used to transmit power in different industrial systems to maintain high performance and safety. Online condition monitoring techniques (CMTs) are used to monitor the operational conditions of such systems. Vibration-based monitoring techniques (VMT) are among the CMTs that are used in the analysis and diagnosis of the state of a belt drive system. Machine learning techniques are integrated with the VMT based on Industry 4.0 aspects for vibration analysis and fault diagnosis. Most of these techniques are based on the collection of vibration data from the belt drive system under known normal and different known faulty operations. This enables a fault to be diagnosed when it is detected during the operation of a system. In this paper, a new condition monitoring and warning mechanism is proposed to monitor the operational conditions of a belt drive system. The mechanism is based on an integration of a logical analysis of data regression (LADR) with a residual control chart (RCC). It uses vibration data from the belt drive system under normal operation only. This mechanism exhibits better performance in fault detection and also in interpreting the root cause of the faults in a belt drive system. Experimental investigations on a belt drive test rig have been carried out to collect vibration data based on a design of experiment for operational factors during normal operation. The LADR-RCC is implemented to monitor the operation of the belt drive system and detect faulty states. The accuracy of LADR is compared with multiple linear regression-based RCC, support vector regression-based RCC and random forest-based RCC. The LADR-RCC demonstrates significant enhancements in fault detection. The advantage of LADR-RCC over other model-based RCC is that it finds the root cause of a fault that is experienced in the system.

Research on the online monitoring technique for transformer oil level based on ultrasonic sensors

AbstractIn order to solve the problem of remote oil level measurement for unmanned transformers, this paper proposes an online monitoring technology for transformer oil level based on ultrasonic sensors. Subsequently, a finite element model and experimental testing platform were constructed to analyse and verify the influencing factors of the transformer oil level sensors. The results indicated that there is a lower measurement error at 140 kHz in the ultrasonic frequency range of 20–320 kHz, which can be selected as the recommended frequency. The impurities in the oil and the thickness of the tank wall have a slight impact on the accuracy of oil level measurement, and can lead to a decrease in the signal‐to‐noise ratio of the echo. Furthermore, as the speed of sound increases by about 4 m/s, for every 1°C increase in oil temperature, it is necessary to calibrate the measurement results based on the oil temperature. The test results of actual experimental transformers showed that the designed online monitoring device can achieve high‐precision monitoring of transformer oil level, with a relative error generally less than 3%.

Diffusion-driven fed-batch fermentation in perforated ring flasks

PurposeSimultaneous membrane-based feeding and monitoring of the oxygen transfer rate shall be introduced to the newly established perforated ring flask, which consists of a cylindrical glass flask with an additional perforated inner glass ring, for rapid bioprocess development.Methods A 3D-printed adapter was constructed to enable monitoring of the oxygen transfer rate in the perforated ring flasks. Escherichia coli experiments in batch were performed to validate the adapter. Fed-batch experiments with different diffusion rates and feed solutions were performed.ResultsThe adapter and the performed experiments allowed a direct comparison of the perforated ring flasks with Erlenmeyer flasks. In batch cultivations, maximum oxygen transfer capacities of 80 mmol L−1 h−1 were reached with perforated ring flasks, corresponding to a 3.5 times higher capacity than in Erlenmeyer flasks. Fed-batch experiments with a feed reservoir concentration of 500 g glucose L−1 were successfully conducted. Based on the oxygen transfer rate, an ammonium limitation could be observed. By adding 40 g ammonium sulfate L−1 to the feed reservoir, the limitation could be prevented.Conclusion The membrane-based feeding, an online monitoring technique, and the perforated ring flask were successfully combined and offer a new and promising tool for screening and process development in biotechnology.

Elaborating the Crystal Water of Prussian Blue for Outstanding Performance of Sodium Ion Batteries.

Prussian blue (PB) is one of the main cathode materials with industrial prospects for the sodium ion battery. The structural stability of PB materials is directly associated with the presence of crystal water within the open 3D framework. However, there remains a lack of consensus regarding whether all forms of crystal water have detrimental effects on the structural stability of the PB materials. Currently, it is widely accepted that interstitial water is the stability troublemaker, whereas the role of coordination water remains elusive. In this work, the dynamic evolution of PB structures is investigated during the crystal water (in all forms) removal process through a variety of online monitoring techniques. It can be inferred that the PB-130 °C retains trace coordination water (1.3%) and original structural integrity, whereas PB-180 °C eliminates almost all of crystal water (∼12.1%, including both interstitial and coordinated water), but inevitably suffers from structural collapse. This is mainly because the coordinated water within the PB material plays a crucial role in maintaining structural stability via forming the -N≡C-FeLS-C≡N- conjugate bridge. Consequently, PB-130 °C with trace coordination water delivers superior reversible capacity (113.6 mAh g-1), high rate capability (charge to >80% capacity in 3 min), and long cycling stability (only 0.012% fading per cycle), demonstrating its promising prospect in practical applications.

Highly sensitive resistance spectroscopy technique for online monitoring of biofilm growth on metallic surfaces

Online techniques for monitoring biofilm formation and evolution are limited, especially as regards its application in flowing water systems. This is chiefly due to the absence of efficient non-destructive and non-invasive sensing methods. In this study, a sensitive electrical resistance spectroscopy technique is developed to monitor non-invasively and in real time the growth of biofilms over metallic surfaces inside water flow systems. To this aim, Pseudomonas fluorescens strain is used for biofilm development lasting 72 h in a laboratory-scale test channel of orthogonal cross section. Biofilm development corresponds to a progressively increasing coverage of the metallic surface area up to full coverage and a progressively increasing thickness. Biofilm development is registered by continuous recording of electrical impedance signals (time series). Proper configuration and tuning of the electronics promote the resistive contribution to the signal whereas careful grounding diminishes electrical interferences and yields superb sensing sensitivity. An increase of relative electrical resistance of around 15% is noticed in 72 h flow experiments which is attributed to both an increase of metallic surface area coverage and an increase of biofilm thickness. An independent estimation of these quantities using imaging tools and microscopy analysis, indicates that full coverage of the metallic surface occurs after only 48 h of the flow experiment, whereas biofilm thickness increases gradually along the entire 72 h of the experiment. Cross-examination of electrical signals with biofilm characteristics (metallic surface coverage and biofilm thickness) reveals that, qualitatively speaking, electrical signals are rather more sensitive to metallic surface coverage than biofilm thickness.

Flaw Detection in Wire and Arc Additive Manufacturing Using In-Situ Wide Frequency Bandwidth Acoustic Pressure

Wire and arc additive manufacturing (WAAM) is an additive manufacturing (AM) process that can produce large metallic components with low material waste and high production rates. However, WAAM’s high deposition rates require high heat input which can result in potential defects such as pores, cracks, lack of fusion or distortion. For practical implementation of the WAAM process in an industrial environment it is necessary to ensure defects-free production. However, the NDT inspection using traditional NDT techniques (ultrasound, eddy currents, x-ray, for example) is a very demanding task, especially during part production. Therefore, reliable online NDT inspection and monitoring techniques are needed for the industrial spread of WAAM. The objective of this work is to detect flaw formation on WAAM produced parts using in-situ acquired acoustic data with a frequency bandwidth from 10 to 1MHz. WAAM parts were processed with deliberately introduced contaminations while its acoustic signal was obtained to correlate different signal characteristics with defects. To identify flaw formation, two distinct types of microphones were employed to acquire data from the same deposition process. The processing of the signal consisted of applying time and frequency domain techniques, namely, Power Spectral Density and Short Time Fourier Transform. The acoustic signatures obtained allowed for the differentiation between flawed and flaw free signals and for the spatial location of the contaminations. The acoustic signal acquired also showed that the data acquired by conventional microphones is not enough to fully characterize the acoustic spectrum emitted by the WAAM process. This work demonstrates the potential of acoustic data and signal processing in the online inspection of WAAM produced parts.

Modelling and monitoring social network change based on exponential random graph models

This paper aims to detect anomalous changes in social network structure in real time and to offer early warnings by phase II monitoring social networks. First, the exponential random graph model is used to model social networks. Then, a test and online monitoring technique of the exponential random graph model is developed based on the split likelihood-ratio test after determining the model and its parameters for a specific data set. This proposed approach uses pseudo-maximum likelihood estimation and likelihood ratio to construct the test statistics, avoiding the several steps of discovering Monte Carlo Markov Chain maximum likelihood estimation through an iterative method. A bisection algorithm for the control limit is given. Simulations on three data sets Flobusiness, Kapferer and Faux.mesa.high are presented to study the performance of the procedure. Different change points and shift sizes are compared to see how they affect the average run length. A real application example on the MIT reality mining social proximity network is used to illustrate the proposed modelling and online monitoring methods.

A New ATPG and Online Monitoring based Technique for Hardware Trojan Detection

Hardware Trojan (HT) is the most severe security threat during an integrated circuit (IC) development. The logic testing based pre-silicon techniques fail to detect the HT inserted during fabrication, whereas the existing ATPG based approaches provide low coverage and are inefficient to trigger hard-to-activate sites. Further, the existing online monitoring techniques fail to detect the Trojans effectively and do not provide any opportunity to fix/reject the design. Therefore, a new HT detection technique is proposed that combines ATPG and online monitoring to detect all the stealthy Trojans effectively. This paper first proposes a new ATPG algorithm that generates the patterns to activate the Trojan. Further, a new modified HT detection (checker) logic is proposed that is selectively inserted in the original design to effectively observe the effect of activated Trojan during post-silicon testing and online monitoring. The proposed checker logic eliminates the masking problem and can also observe the effects of those HTs, which could not propagate to the output. The experimental evaluation on ISCAS benchmarks shows that our algorithm provides 4.5× higher trigger coverage and does not leave any feasible rare-node inactivated. Further, the proposed checker effectively observes malicious behavior without additional overhead over the existing checker. The insertion of our checker at 25% low observability nodes in ISCAS benchmarks requires only 0.189% area overhead.

Embedded Ultrasonic Inspection on the Mechanical Properties of Cold Region Ice under Varying Temperatures.

The mechanical properties of ice in cold regions are significantly affected by the variation in temperature. The existing methods to determine ice properties commonly rely on one-off and destructive compression and strength experiments, which are unable to acquire the varying properties of ice due to temperature variations. To this end, an embedded ultrasonic system is proposed to inspect the mechanical properties of ice in an online and real-time mode. With this system, ultrasonic experiments are conducted to testify to the validity of the system in continuously inspecting the mechanical properties of ice and, in particular, to verify its capabilities to obtain ice properties for various temperature conditions. As an extension of the experiment, an associated refined numerical model is elaborated by mimicking the number, size, and agglomeration of bubbles using a stochastic distribution. This system can continuously record the wave propagation velocity in the ice, giving rise to ice properties through the intrinsic mechanics relationship. In addition, this model facilitates having insights into the effect of properties, e.g., porosity, on ice properties. The proposed embedded ultrasonic system largely outperforms the existing methods to obtain ice properties, holding promise for developing online and real-time monitoring techniques to assess the ice condition closely related to structures in cold regions.

Online remaining fatigue life estimation of curved steel connection using nonlinear ultrasonic modulation

An online monitoring technique was developed to continuously estimate the remaining fatigue life of a padeye. Piezoceramic ultrasonic transducers were surface-mounted on the padeye for ultrasound generation and sensing. A fatigue index is defined and calculated from the experimentally measured nonlinear ultrasonic modulation components. The fatigue index is also derived as a simple power function of the loading cycles based on the Nazarov–Sutin and Paris–Erdogan theories. Then, the remaining fatigue life is estimated and continuously updated by fitting the power function to the ultrasonic modulation data accumulated up to the present time.

Electrical Method for the On-Line Monitoring of Zeolite-Based Thermochemical Storage

Zeolites are used to store sunlight energy in the form of latent heat of adsorption. The energy is stored by dehydration of the substance and released by its rehydration. The availability of an online monitoring technique for this hydration/dehydration process is an extremely useful potentiality for an optimal exploitation of such technology, since it allows establishment of the degree of activation and saturation of the material. Here, an electrical method has been developed and used for monitoring the hydration/dehydration process of a sample of natural clinoptilolite. Clinoptilolite has been selected as a model zeolitic material for testing this monitoring technique since it is a widely spread, very inexpensive, and highly mechanically stable zeolite type, that could be used for such a purpose. The study has been performed in the presence of pure water vapor and wet air (75RH) after having dehydrated the sample by exposition to sunlight for 12 h. The developed monitoring method has also allowed us to have information on the kinetics of the process (Lagergren pseudo-first order), to establish the specific rate of hydration (3.3 × 10−3 min−1), and to have an idea of the involved adsorption mechanism. The sample of natural clinoptilolite was also chemically and structurally characterized by EDS, XRD, DSC, and TGA.

Spatio-temporal progression and influencing mechanism of local wetting in membrane distillation

Membrane wetting is the bottleneck of membrane distillation (MD) in real applications. However, rare information is attainable on the spatial inhomogeneity and temporal instability of local wetting behavior along the membrane. In current study, the influences of operating conditions (i.e., feed temperature, velocity, and concentration) and membrane hydrophobicity on the spatio-temporal progression of local wetting along the membrane were evaluated using a non-invasive and online monitoring technique – ultrasonic time-domain reflectometry (UTDR). UTDR measurements could successfully distinguish local wetting dynamics and were validated by the off-line characterization of the wetting layer thickness via a scanning electron microscopy with energy-dispersive x-ray spectroscopy. Observations showed that the local wetting depth decreases along the feed flow direction. This wetting heterogeneity would be impaired with increasing surfactant concentration, feed temperature, and crossflow velocity. However, enhancing membrane hydrophobicity obviously reduced the rate of local wetting but hardly affected its nonuniform distribution. The wetting heterogeneous propagation at different locations is dominated by the nonuniform mass and heat transfer characteristics along the membrane based on the computational fluid dynamics (CFD) simulation. Localized wetting prevention near the module inlet is therefore critical to ensure stable MD operation.

Employing machine learning to assess the accuracy of near-infrared spectroscopy of spent dialysate fluid in monitoring the blood concentrations of uremic toxins

Hemodialysis (HD) removes nitrogenous waste products from patients? blood through a semipermeable membrane along a concentration gradient. Near-infrared spectroscopy (NIRS) is an underexplored method of monitoring the concentrations of several molecules that reflect the efficacy of the HD process in dialysate samples. In this study, we aimed to evaluate NIRS as a technique for the non-invasive detection of uremic solutes by assessing the correlations between the spectrum of the spent dialysate and the serum levels of urea, creatinine, and uric acid. Blood and dialysate samples were taken from 35 patients on maintenance HD. The absorption spectrum of each dialysate sample was measured three times in the wavelength range of 700-1700 nm, resulting in a dataset with 315 spectra. The artificial neural network (ANN) learning technique was used to assess the correlations between the recorded NIR-absorbance spectra of the spent dialysate and serum levels of selected uremic toxins. Very good correlations between the NIR-absorbance spectra of the spent dialysate fluid with serum urea (R=0.91) and uric acid (R=0.91) and an excellent correlation with serum creatinine (R=0.97) were obtained. These results support the application of NIRS as a non-invasive, safe, accurate, and repetitive technique for online monitoring of uremic toxins to assist clinicians in assessing HD efficiency and individualization of HD treatments.

Online monitoring of oil film thickness of journal bearing in aviation fuel gear pump

As the key support of aviation fuel pumps, journal bearings are operated under extreme conditions of high temperature, high pressure and low viscosity media, therefore, the broken of the ultra-thin lubrication film is the main cause of lubrication failure and abnormal wear. However, the dynamic hydraulic lubrication (DHL) film is still blind due to the lack of non-destructive online monitoring techniques. This paper presents the study of ultrasonic-based measurement for the dynamic oil film thickness of fuel pump. The circumferential oil film thicknesses in a journal bearing are measured with the complex model and the resonance model, meanwhile, the minimum oil film thickness is extracted by fitting the measured points with the initial circle. Under varying operating conditions, the measured results are verified with classical DHL simulations, and the maximum relative error is shown to be lower than 15%.

FcγRIIIA affinity chromatography complements conventional functional characterization of rituximab.

Analytical and functional characterization of batches of biologics/biosimilar products are imperative towards qualifying them for pre-clinical and clinical investigations. Several orthogonal strategies are employed to characterize the functional attributes of these drugs. However, the use of conventional techniques for online monitoring of functional attributes is not feasible. Liquid chromatography is one of the crucial unit operations during the downstream processing of biopharmaceuticals. In this work, we have demonstrated the utility of FcγRIIIA affinity chromatography as an independent quantitative functional characterization tool. FcγRIIIA affinity chromatography aided in sequential elution of Rituximab glycoform mixtures, based on varying levels of galactosylation, and thereby the affinity for the receptor protein. The predominant glycans present in the three Rituximab glycoform mixture peaks were G0F, G1F, and G2F, respectively. Dissociation rate constants were derived from the chromatographic elution profiles by the peak profiling method, for the control and glucose stress conditions. The glucose stress conditions did not result in unfavorable binding kinetics of Rituximab and FcγRIIIA. The dissociation rate constants of the glycoform mixture 2, predominantly consisting of G1F, were similar to the dissociation rate constants obtained by surface plasmon resonance. Moreover, the glycosylation profiles obtained from chromatographic estimation can be corroborated with the ADCC activity. However, the ex vivo ADCC reporter assay indicated that there was an increase in the effector activity with increasing glucose stress. Thus, FcγRIIIA affinity chromatography permitted three independent assessments via a single analysis. Such approaches can be utilized as potential process analytical technology (PAT) tools in the biosimilar development process.

Study on the Natural Ventilation Characteristics of a Solar Greenhouse in a High-Altitude Area

The ventilation rate of a greenhouse is one of the major factors to consider when assessing its ventilation performance. Compared with plain areas, high-altitude areas have lower air pressure, thinner air, and stronger solar radiation, which in turn affect the magnitude of the local greenhouse ventilation rate. This paper is based on the use of online monitoring and computational fluid dynamics (CFD) techniques for modeling and model validation. The average relative error (ARE), mean absolute error (MAE), root-mean-square error (RMSE), and determination coefficient (R2) of the temperature were 4.88%, 1.396 °C, 1.428 °C, and 0.9982, respectively. The ARE, MAE, RMSE, and R2 of the velocity were 9.525%, 0.035 m/s, 0.049 m/s, and 0.9869, respectively. Then, the distributions of the wind pressure, Reynolds number (Re), thermal pressure, air density, air speed, and temperature in greenhouses in high-altitude and plain areas were researched to obtain the relevant factors affecting the ventilation rates of greenhouses in high-altitude areas. In addition, correlation analyses were conducted for five variables affecting the ventilation rate: the inlet velocity, the temperature difference between the inside and outside of the greenhouse, the air density difference between the inside and outside of the greenhouse, total indoor radiation, and the internal heat source of the crop, and the coefficients of their correlations with the greenhouse ventilation rate were 1.0, −0.83, −0.72, −0.72, and 0.68, respectively. A natural ventilation rate model for plateau areas was developed, with the ARE, RMSE, and R2 between the sample values and fitted values determined to be 4.55%, 0.543 m3/s, and 0.9997, respectively. The model was validated by predicting the greenhouse ventilation rate in winter (3 January 2022), and the ARE, RMSE, and R2 of the sample values and predicted values were 9.726%, 8.435 m3/s, and 0.9901, respectively. This study provides a theoretical basis for further research on greenhouse ventilation characteristics in high-altitude areas.

Resonant photoacoustic spectrometer enhanced by multipass absorption for detecting atmospheric CH4 at the ppb-level

A resonant photoacoustic spectrometer (PAS) was developed for detecting trace atmospheric CH4. The sensitivity of the PAS was significantly increased via a Herriott-type multipass cell with a beam pattern concentrated in the cavity. The effective optical pathlength of the PAS can be optimized to 6.8 m with 34 reflections and a diameter of 6 mm. A distributed feedback diode laser at 1,653 nm was employed as the light source, and wavelength modulation spectroscopy was used for the 2nd harmonic signal to reduce the noise of the system. The resonant cell of PA and optimal modulation frequency were obtained by varying the measurements. In comparison with a single path, the sensitivity of the multipass strategy was improved 13 times. To evaluate the long-term stability and minimum detection limit (MDL) of the system, an Allan variance analysis was performed, and the analysis illustrated that the MDL accomplished 116 ppb at an average time of 84 s. The system was utilized for 2 days test campaign to validate the feasibility and robustness of the sensor. The system provides a promising technique for online monitoring of greenhouse gasses.

Study of Ultra High Frequency Measurement Techniques for Online Monitoring of Partial Discharges in High Voltage Systems

Partial Discharge (PD) activity is a pre-cursor for insulation degradation which may eventually lead to catastrophic failure of the electrical equipment with severe social and economic consequences. It is therefore imperative that PD is detected at its early stages to permit repair or replacement, prior to total failure. The bulky non-planar construction of existing ultra-high frequency (UHF) sensors coupled with the lack of proper feeding techniques and the ultrawide signal bandwidth of PD signals which necessitates the use of digital storage oscilloscope have restricted field deployment of UHF technique for online PD detection. In this work, we present a compact ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.58 \boldsymbol {\lambda } $ </tex-math></inline-formula> ) wideband (0.5-3 GHz) dual arm Archimedean slot spiral with coplanar waveguide feed for PD detection, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {\lambda } $ </tex-math></inline-formula> is the wavelength at center frequency. Numerical simulations of the antenna were compared with the measurements of the fabricated antenna. The fabricated wideband antenna was used to detect PD emissions from a mineral oil test cell connected to 10 kVA 50 Hz transformer. We also present different UHF signal measurement techniques ranging from PD pulse detection to reconstruction of the bandlimited PD signal in the digital domain for online structural health monitoring of high voltage systems. The proposed UHF PD measurement techniques are low cost compared to the conventional approach and indicate the feasibility of an embedded system for online PD monitoring using the fabricated UHF sensor.

Insights into Streptomyces coelicolor A3(2) growth and pigment formation with high-throughput online monitoring.

Streptomyces species are intensively studied for their ability to produce a variety of natural products. However, conditions influencing and leading to product formation are often not completely recognized. Therefore, in this study, high-throughput online monitoring is presented as a powerful tool to gain in-depth understanding of the cultivation of the model organism Streptomyces coelicolor A3(2). Through online measurements of oxygen transfer rate and autofluorescence, valuable information about availability of nutrients and product formation patterns of the pigments actinorhodin and undecylprodigiosin can be obtained and explained. Therefore, it is possible to determine the onset of pigmentation and to study in detail the influencing factors thereof. One factor identified in this study is the filling volume of the cultivation vessel. Slight variations led to varying pigmentation levels. By combining optical and metabolic online monitoring techniques, the correlation of the filling volume with pigmentation could be explained as a result of different growth trajectories caused by varying specific power inputs and their influence on the pellet formation of the filamentous system. Finally, experiments with the addition of supernatant from unpigmented and pigmented cultures could highlight the applicability of the presented approach to study quorum sensing and cell-cell interaction.