Li-ion batteries experience mechanical stress evolution due in part to Li intercalation into and de-intercalation out of the electrodes, ultimately resulting in performance degradation. In situ measurements of electrode stress can be used to analyze stress generation factors, verify mechanical deformation models, and validate degradation mechanisms. They can also be embedded in Li-ion battery management systems when stress sensors are either implanted in electrodes or attached on battery surfaces. This paper reviews in situ measurement methods of electrode stress based on optical principles, including digital image correlation, curvature measurement, and fiber optical sensors. Their experimental setups, principles, and applications are described and contrasted. This literature review summarizes the current status of these stress measurement methods for battery electrodes and discusses recent developments and trends.

Owing to the installation of autotransformers at regular intervals along the line, distance protection relays cannot be used with the aim of locating ground faults in 2 × 25 kV railway power supply systems. The reason is that the ratio between impedance and distance to the fault point is not linear in these electrification systems, unlike in 1 × 25 kV power systems. Therefore, the location of ground faults represents a complicated task in 2 × 25 kV railway power supply systems. Various methods have been used to localize the ground fault position in 2 × 25 kV systems. The method described here allows the location of a ground fault to be economically found in an accurate way in real time, using the modules of the circulating currents in different autotransformers when the ground fault occurs. This method first needs to know the subsection and the conductor (catenary or feeder) with the defect, then localizes the ground fault’s position.

This paper describes the development and application of a signal-based fault detection method for identifying gas leakage in hydraulic accumulators used in wind turbines [...]

‘Thermal Overload’ can be defined as a condition under which design threshold values such as the surface temperature of combustion chamber components is exceeded [...]

This study describes the development of a semi-physical, real-time nitric oxide (NO) prediction model that is capable of cycle-by-cycle prediction in a light-duty diesel engine. The model utilizes the measured in-cylinder pressure and information obtained from the engine control unit (ECU). From the inputs, the model takes into account the pilot injection burning and mixing, which affects the in-cylinder mixture formation. The representative in-cylinder temperature for NO formation was determined from the mixture composition calculation. The selected temperature and mixture composition was substituted using a simplified form of the NO formation rate equation for the cycle-by-cycle estimation. The reactive area and the duration of NO formation were assumed to be limited by the fuel quantity. The model predictability was verified not only using various steady-state conditions, including the variation of the EGR rate, the boost pressure, the rail pressure, and the injection timing, but al... [more]

Traditional current transformers (CTs) suffer from DC and AC saturation and remanent magnetization in many industrial applications. Moreover, the drawbacks of traditional CTs, such as closed iron cores, bulky volume, and heavy weight, further limit the development of an intelligent power protection system. In order to compensate for these drawbacks, we proposed a novel current measurement method by using Hall sensors, which is called the Hall-effect current transformer (HCT). The existing commercial Hall sensors are electronic components, so the reliability of the HCT is normally worse than that of the traditional CT. Therefore, our study proposes a redundancy mechanism for the HCT to strengthen its reliability. With multiple sensor modules, the method has the ability to improve the accuracy of the HCT as well. Additionally, the proposed redundancy mechanism monitoring system provides a condition-based maintenance for the HCT. We verify our method with both simulations and an experimen... [more]

In order to improve the fault monitoring performance of grounding electrode lines in ultra-high voltage DC (UHVDC) transmission systems, a novel fault monitoring approach based on the high-frequency voltage standing-wave ratio (VSWR) is proposed in this paper. The VSWR is defined considering a lossless transmission line, and the characteristics of the VSWR under different conditions are analyzed. It is shown that the VSWR equals 1 when the terminal resistance completely matches the characteristic impedance of the line, and when a short circuit fault occurs on the grounding electrode line, the VSWR will be greater than 1. The VSWR will approach positive infinity under metallic earth fault conditions, whereas the VSWR in non-metallic earth faults will be smaller. Based on these analytical results, a fault supervision criterion is formulated. The effectiveness of the proposed VSWR-based fault supervision technique is verified with a typical UHVDC project established in Power Systems Compu... [more]

Failure mode and effects analysis (FMEA) has been proven to be an effective methodology to improve system design reliability. However, the standard approach reveals some weaknesses when applied to wind turbine systems. The conventional criticality assessment method has been criticized as having many limitations such as the weighting of severity and detection factors. In this paper, we aim to overcome these drawbacks and develop a hybrid cost-FMEA by integrating cost factors to assess the criticality, these costs vary from replacement costs to expected failure costs. Then, a quantitative comparative study is carried out to point out average failure rate, main cause of failure, expected failure costs and failure detection techniques. A special reliability analysis of gearbox and rotor-blades are presented.

The existing charts for monitoring the variance are designed under the assumption that all production data must consist of exact, precise, and determined observations. This paper presents the design of a new neutrosophic exponentially weighted moving average (NEWMA) combining with a neutrosophic logarithmic transformation chart for monitoring the variance having neutrosophic numbers. The computation of the neutrosophic control chart parameters is done through the neutrosophic Monte Carlo simulation (NMCS). The performance of the proposed chart is discussed with the existing charts.

The high capacity and voltage properties demonstrated by lithium-ion batteries render them as the preferred energy carrier in portable electronic devices. The application of the lithium-ion batteries which previously circulating and contained around small-scale electronics is now expanding into large scale emerging markets such as electromobility and stationary energy storage. Therefore, the understanding of the risk involved is imperative. Thermal runaway is the most common failure mode of lithium-ion battery which may lead to safety incidents. Transport process of immense amounts of heat released during thermal runaway of lithium-ion battery to neighboring batteries in a module can lead to cascade failure of the whole energy storage system. In this work, a model is developed to predict the propagation of lithium-ion battery in a module for large scale applications. For this purpose, kinetic of material thermal decomposition is combined with heat transfer modelling. The simulation is... [more]

Center spring wear faults in hydraulic pumps can cause fluid pressure fluctuations at the outlet, and the fault feature information on fluctuations is often contaminated by different types of fluid flow interferences. Aiming to resolve the above problems, a fluid pressure signal method for hydraulic pumps based on Autogram was applied to extract the fault feature information. Firstly, maximal overlap discrete wavelet packet transform (MODWPT) was adopted to decompose the contaminated fault pressure signal of center spring wear. Secondly, based on the squared envelope of each node, three kinds of kurtosis of unbiased autocorrelation (AC) were computed in order to describe the fault feature information comprehensively. These are known as standard Autogram, upper Autogram and lower Autogram. Then a node corresponding to the biggest kurtosis value was selected as a data source for further spectrum analysis. Lastly, the data source was processed by threshold values, and then the fault could... [more]

Chromatography is widely used in biotherapeutics manufacturing, and the corresponding underlying mechanisms are well understood. To enable process control and automation, spectroscopic techniques are very convenient as on-line sensors, but their application is often limited by their sensitivity. In this work, we investigate the implementation of Raman spectroscopy to monitor monoclonal antibody (mAb) breakthrough (BT) curves in chromatographic operations with a low titer harvest. A state estimation procedure is developed by combining information coming from a lumped kinetic model (LKM) and a Raman analyzer in the frame of an extended Kalman filter approach (EKF). A comparison with suitable experimental data shows that this approach allows for the obtainment of reliable estimates of antibody concentrations with reduced noise and increased robustness.

Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water. However, pipelines are often subject to leakage due to pipe corrosion, pipe aging, pipe weld defects, or damage by a third-party, resulting in huge economic losses and environmental degradation. Therefore, effective pipeline leak detection methods are important research issues to ensure pipeline integrity management and accident prevention. The conventional methods for pipeline leak detection generally need to extract the features of leak signal to establish a leak detection model. However, it is difficult to obtain actual leakage signal data samples in most applications. In addition, the operating modes of pipeline fluid transportation process often have frequent changes, such as regulating valves and pump operation. Aiming at these issues, this paper proposes a hybrid intelligent method that integrates kernel principal component analysis (KPCA) and cascade suppo... [more]

A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And... [more]

This work aimed to investigate the carbonaceous deposits on the surface of the coking chamber. Scanning electron microscopy (SEM), X-ray fluorescence spectrum (XRF), Fourier transform infrared spectrometer (FTIR), Raman spectroscopy, X-ray diffraction spectrum (XRD), and X-ray photoelectron spectroscopy (XPS) were applied to investigate the carbonaceous deposits. FTIR revealed the existence of carboxyl, hydroxyl, and carbonyl groups in the carbonaceous deposits. SEM showed that different carbonaceous deposit layers presented significant differences in morphology. XRF and XPS revealed that the carbonaceous deposits mainly contained C, O, and N elements, with smaller amounts of Al, Si, and Ca elements. It was found that in the formation of carbonaceous deposits, the C content gradually increased while the O and N elements gradually decreased. It was also found that the absorbed O2 and H2O took part in the oxidation process of the carbon skeleton to form the =O and −O− structure. The oxid... [more]

In recent years, magnetic nanoparticles (MNPs) have been widely used as a new material in biomedicine and other fields due to their broad versatility, and the quantitative detection method of MNPs is significantly important due to its advantages in immunoassay and single-molecule detection. In this study, a method and device for detecting the number of MNPs based on weak magnetic signal were proposed and machine learning methods were applied to the design of MNPs number detection method and optimization of detection device. Genetic Algorithm was used to optimize the MNPs detection platform and Simulated Annealing Neural Network was used to explore the relationship between different positions of magnetic signals and the number of MNPs so as to obtain the optimal measurement position of MNPs. Finally, Radial Basis Function Neural Network, Simulated Annealing Neural Network, and partial least squares multivariate regression analysis were used to establish the MNPs number detection model,... [more]

As the optimal linear filter and estimator, the Kalman filter has been extensively utilized for state estimation and prediction in the realm of lumped parameter systems. However, the dynamics of complex industrial systems often vary in both spatial and temporal domains, which take the forms of partial differential equations (PDEs) and/or delay equations. State estimation for these systems is quite challenging due to the mathematical complexity. This work addresses discrete-time Kalman filter design and realization for linear distributed parameter systems. In particular, the structural- and energy-preserving Crank−Nicolson framework is applied for model time discretization without spatial approximation or model order reduction. In order to ensure the time instance consistency in Kalman filter design, a new discrete model configuration is derived. To verify the feasibility of the proposed design, two widely-used PDEs models are considered, i.e., a pipeline hydraulic model and a 1D bounda... [more]

In this article, the statistical process monitoring problem of the Tennessee Eastman process is considered using deep learning techniques. This work is motivated by three limitations of the existing works for such problem. First, although deep learning has been used for process monitoring extensively, in the majority of the existing works, the neural networks were trained in a supervised manner assuming that the normal/fault labels were available. However, this is not always the case in real applications. Thus, in this work, autoassociative neural networks are used, which are trained in an unsupervised fashion. Another limitation is that the typical dataset used for the monitoring of the Tennessee Eastman process is comprised of just a small number of data samples, which can be highly limiting for deep learning. The dataset used in this work is 500-times larger than the typically-used dataset and is large enough for deep learning. Lastly, an alternative neural network architecture, whi... [more]

Monitoring the thermal behavior of distributed energy resources (DERs) network explores the dualism between thermal effects and electrical power flow. This paper proposes a design concept that monitors thermal conditions of DER grids, using ZigBee/GSM wireless sensor networks (WSNs) for real-time monitoring in rural and remote areas. The concept seeks to improve upon existing designs by integrating composite functions. The functions comprise temperature conditions monitoring, data acquisition, and wireless data transmission including data storage and abnormal conditions alert/notification for control solutions. Thus, the concept determines the thermal impact on the DERs integrated network. WSNs with temperature sensors LM35 are utilized to complement ZigBee and Global System for Mobile Communications (GSM) technologies as a communication assisted link. Temperatures are measured from solar Photovoltaic PV modules, wind turbine, distribution cables, protection control units, and energy s... [more]

Waterflooding is less effective at expanding reservoir production due to interwell thief zones. The thief zones may form during high water cut periods in the case of interconnected injectors and producers or lead to a total loss of injector fluid. We propose to identify the thief zone by using a support vector machine method. Considering the geological factors and development factors of the formation of the thief zone, the signal-to-noise ratio and correlation analysis method were used to select the relevant evaluation indices of the thief zone. The selected evaluation indices of the thief zone were taken as the input of the support vector machine model, and the corresponding recognition results of the thief zone were taken as the output of the support vector machine model. Through the training and learning of sample sets, the response relationship between thief zone and evaluation indices was determined. This method was used to identify 82 well groups in M oilfield, and the identifica... [more]

Data with characteristics like nonlinear and non-Gaussian are common in industrial processes. As a non-parametric method, k-nearest neighbor (kNN) rule has shown its superiority in handling the data set with these complex characteristics. Once a fault is detected, to further identify the faulty variables is useful for finding the root cause and important for the process recovery. Without prior fault information, due to the increasing number of process variables, the existing kNN reconstruction-based identification methods need to exhaust all the combinations of variables, which is extremely time-consuming. Our previous work finds that the variable contribution by kNN (VCkNN), which defined in original variable space, can significantly reduce the ratio of false diagnosis. This reliable ranking of the variable contribution can be used to guide the variable selection in the identification procedure. In this paper, we propose a fast kNN reconstruction method by virtue of the ranking of VCk... [more]

Waste cooking oils (WCO) recycling companies usually have economic losses for buying WCO not suitable for biodiesel production, e.g., WCO with high free acidity (FA). For this reason, the determination of FA of WCO by near infrared (NIR) spectroscopy was studied in this work to assess its potential for in situ application. To do this, FA of 45 WCO was measured by the classical titration method, which ranged between 0.15 and 3.77%. Then, the NIR spectra from 800 to 2200 nm of these WCO were acquired, and a partial least squares model was built, relating the NIR spectra to FA values. The accuracy of the model was quite high, providing r2 of 0.970 and a ratio of performance to deviation (RPD) of 4.05. Subsequently, a model using an NIR range similar to that provided by portable NIR spectrometers (950−1650 nm) was built. The performance was lower (r2 = 0.905; RPD = 2.66), but even so, with good accuracy, which demonstrates the potential of NIR spectroscopy for the in situ determination of... [more]

Changes in water level between the mainstems and tributaries of a river can create backflow effects that alter the representativeness of water-quality monitoring data. In South Korea, 16 multi-functional weirs intended to manage water levels were constructed on 4 major rivers as part of a restoration project. However, they are causing backwater effects in tributaries that coincide with poorer water-quality measurements at monitoring stations along these tributaries despite there being no change in upstream pollution sources. Therefore, this study developed a new methodology for verifying the representativeness of a water-quality monitoring network via spatiotemporal observations of electrical conductivity, self-organizing maps for monthly pattern analysis, locally weighted scatter plot smoothing for trend analysis, load duration curves, and numerical modeling. This approach was tested on the Wicheon River, a primary tributary of the Nakdong River, because the measured decline in water... [more]

In the last few decades, the Smart Grid paradigm presence has increased within power systems. These new kinds of networks demand new Operations and Planning approaches, following improvements in the quality of service. In this sense, the role of the Distribution Management System, through its Outage Management System, is essential to guarantee the network reliability. This system is responsible for minimizing the consequences arising from a fault event (or network failure). Obviously, knowing where the fault appears is critical for a good reaction of this system. Therefore, several fault location techniques have been proposed. However, most of them provide individual results, associated with specific testbeds, which make the comparison between them difficult. Due to this, a review of fault location methods has been done in this paper, analyzing them for their use on underground distribution lines. Specifically, this study is focused on an impedance-based method because their requiremen... [more]

This paper presents the results of studies aiming at the assessment and classification of wastewater using an electronic nose. During the experiment, an attempt was made to classify the medium based on an analysis of signals from a gas sensor array, the intensity of which depended on the levels of volatile compounds in the headspace gas mixture above the wastewater table. The research involved samples collected from the mechanical and biological treatment devices of a full-scale wastewater treatment plant (WWTP), as well as wastewater analysis. The measurements were carried out with a metal-oxide-semiconductor (MOS) gas sensor array, when coupled with a computing unit (e.g., a computer with suitable software for the analysis of signals and their interpretation), it formed an e-nose—that is, an imitation of the mammalian olfactory sense. While conducting the research it was observed that the intensity of signals sent by sensors changed with drops in the level of wastewater pollution; th... [more]