Irregular initial solidification by mold thermal monitoring in the continuous casting of steels:A review

Occasional irregular initial solidification phenomena,including stickers,deep oscillation marks,depressions,and surface cracks of strand shells in continuous casting molds,are important limitations for developing the high-efficiency continuous casting of steels.The application of mold thermal monitoring(MTM) systems,which use thermocouples to detect and respond to temperature variations in molds,has become an effective method to address irregular initial solidification phenomena.Such systems are widely applied in numerous steel companies for sticker breakout prediction.However,monitoring the surface defects of strands remains immature.Hence,indepth research is necessary to utilize the potential advantages and comprehensive monitoring of MTM systems.This paper summarizes what is included in the irregular initial solidification phenomena and systematically reviews the current state of research on these phenomena by the MTM systems.Furthermore,the influences of mold slag behavior on monitoring these phenomena are analyzed.Finally,the remaining problems of the formation mechanisms and investigations of irregular initial solidification phenomena are discussed,and future research directions are proposed.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine

Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.

Initial support distance of a non-circular tunnel based on convergence constraint method and integral failure criteria of rock

For deep tunnel projects,selecting an appropriate initial support distance is critical to improving the self-supporting capacity of surrounding rock.In this work,an intuitive method for determining the tunnel’s initial support distance was proposed.First,based on the convergence-confinement method,a three-dimensional analytical model was constructed by combining an analytical solution of a non-circular tunnel with the Tecplot software.Then,according to the integral failure criteria of rock,the failure tendency coefficients of hard surrounding rock were computed and the spatial distribution plots of that were constructed.On this basis,the tunnel’s key failure positions were identified,and the relationship between the failure tendency coefficient at key failure positions and their distances from the working face was established.Finally,the distance from the working face that corresponds to the critical failure tendency coefficient was taken as the optimal support distance.A practical project was used as an example,and a reasonable initial support distance was successfully determined by applying the developed method.Moreover,it is found that the stability of hard surrounding rock decreases rapidly within the range of 1.0D(D is the tunnel diameter)from the working face,and tends to be stable outside the range of 1.0D.

Sensor monitoring of a newly designed foundation pit supporting structure

A new type of pit supporting structure, which was tested and verified using the sensor monitoring technology, was presented. The new supporting structure is assembled by prefabricated steel structural units. The adjacent steel structural units are jointed with fasteners, and each steel structural unit has a certain radian and is welded by two steel tubes and one piece of steel disc. In order to test and verify the reliability of the new supporting structure, the field tests are designed. The main monitoring programs include the hoop stress of supporting structure, lateral earth pressure, and soil deformation. The monitoring data of the field tests show that the new supporting structure is convenient, reliable and safe.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

Artificial Intelligence(AI)is finding increasing application in healthcare monitoring.Machine learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health data.Thus,early detection of any disease or derangement can aid doctors in saving patients’lives.However,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper classification.We propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health status.To begin with,various patient datasets were collected and trained into the system using IoT sensors.As a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result visibility.Additionally,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant information.Then,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a prescription.We found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.

Smart Monitoring of Solar Photovoltaic Panels by the Approach of Machine Learning

The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovoltaic(PV)market,thereby making the management and maintenance of solar photovoltaic(SPV)panels a new area of business as neglecting it may lead to significant financial losses and failure to combat climate change and the energy crisis.SPV panels face many risks that may degrade their power generation performance,damage their structures,or even cause the complete loss of their power generation capacity during their long service life.It is hoped that these problems can be identified and resolved as soon as possible.However,this is a challenging task as a solar power plant(SPP)may contain hundreds even thousands of SPV panels.To provide a potential solution for this issue,a smart drone-based SPV panel condition monitoring(CM)technique has been studied in this paper.In the study,the U-Net neural network(UNNN),which is ideal for undertaking image segmentation tasks and good at handling small sample size problem,is adopted to automatically create mask images from the collected true color thermal infrared images.The support vector machine(SVM),which performs very well in highdimensional feature spaces and is therefore good at image recognition,is employed to classifying the mask images generated by the UNNN.The research result has shown that with the aid of the UNNN and SVM,the thermal infrared images that are remotely collected by drones from SPPs can be automatically and effectively processed,analyzed,and classified with reasonable accuracy(over 80%).Particularly,the mask images produced by the trained UNNN,which contain less interference items than true color thermal infrared images,significantly benefit the assessing accuracy of the health state of SPV panels.It is anticipated that the technical approach presented in this paper will serve as an inspiration for the exploration of more advanced and dependable smart asset management techniques within the solar power industry.

An Initial Residual Stress Inference Method by Incorporating Monitoring Data and Mechanism Model

Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality issues.The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control.Due to the principle error of existing residual stress detection methods,there are still challenges in practical applications.Aiming at the detection problem of the initial residual stress field,an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper.Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress,and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish;the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model.Finally,the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model.Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.

Information Monitoring Technology for Support Structure of Railway Tunnel During Operation

In the process of railway construction, because of the inconvenience ofgeological condition, water bursting and mud surging happen frequently, and the laterdeformation of support structure on the happening geology section would threaten thenormal running of railway. The limit difference of deformation control value of thesupport structure section where geological accidents frequently happen, is small, andartificial half-automatic supervisory technology cannot get the health condition of tunnelin time, resulting many cars speed-down accidents due to deformation of supportstructure. Through design innovation, we introduce TGMIS in the later period ofYanzishan railway construction to quickly capture the deformation of support structure,the strain of lining concrete, the strain of steel frame, stress of surrounding soil, stress ofsurrounding water, strain of second lining steel bar and other situ data. Also we setobservation prism and measuring robot device in specific position inside tunnel, androbot laser locator laser spot is projected onto reflection target surface, by graphicprocessing algorithm, the receiver calculates the measured value and standard value ofthe 3D coordinates of the laser spot. Then the information is transmitted throughtransmitting device, transducer and USB-485 to computer to predict and evaluate thehealth condition of the support structure of the tunnel so as to provide safety warninginformation. Provide timely and reliable data for the operation company to avoid theoccurrence of vicious accidents.

Multimode Process Monitoring Based on the Density-Based Support Vector Data Description

Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.

Vibration Deformation Monitoring of Offshore Wind Turbines Based on GBIR

In view of the disadvantages of vibration safety monitoring technology for offshore wind turbines,a new method is proposed to obtain deformation information of towering and dynamic targets in real-time by the ground-based interferometric ra-dar(GBIR).First,the working principle and unique advantages of the GBIR system are introduced.Second,the offshore wind turbines in Rongcheng,Shandong Province are selected as the monitoring objects for vibration safety monitoring,and the GPRI-II portable radar interferometer is used for the health diagnosis of these wind turbines.Finally,the interpretation method and key processing flow of data acquisition are described in detail.This experiment shows that the GBIR system can accurately identify the millimeter-scale vibration deformation of offshore wind turbines and can quickly obtain overall time series deformation images of the target bodies,which demonstrate the high-precision deformation monitoring ability of the GBIR technology.The accuracy meets the requirements of wind turbine vibration monitoring,and the method is an effective spatial deformation monitoring means for high-rise and dynamic targets.This study is beneficial for the further enrichment and improvement of the technical system of wind turbine vibration safety monitoring in China.It also provides data and technical support for offshore power engineering management and control,health diagnosis,and disaster prevention and mitigation.

An in situ monitoring campaign of a hard rock pillar at great depth within a Canadian mine

A recent research campaign at a Canadian nickel-copper mine involved instrumenting a hard rock sill drift pillar with an array of multi-point rod extensometers,distributed optical fibre strain sensors,and borehole pressure cells(BHPCs).The instrumentation spanned across a 15.24 m lengthwise segment of the relatively massive granitic pillar situated at a depth of 2.44 km within the mine.Between May 2016 and March 2017,the pillar’s displacement and pressure response were measured and correlated with mining activities on the same level as the pillar,including:(1)mine-by of the pillar,(2)footwall drift development,and(3)ore body stoping operations.Regarding displacements of the pillar,the extensometers provided high temporal resolution(logged hourly)and the optical fibre strain sensors provide high spatial resolution(measured every 0.65 mm along the length of each sensor).The combination of sensing techniques allowed centimetre-scale rock mass bulking near the pillar sidewalls to be distinguished from microstrain-scale fracturing towards the core of the pillar.Additionally,the influence and extent of a mine-scale schistose shear zone transecting the pillar was identified.By converting measured rock mass displacement to velocity,a process was demonstrated which allowed mining activities inducing displacements to be categorised by time-duration and cumulative displacement.In over half of the analysed mining activities,displacements were determined to prolong for over an hour,predominately resulting in submillimetre cumulative displacements,but in some cases multi-centimetre cumulative displacements were observed.This time-dependent behaviour was more pronounced within the vicinity of the plumb shear zone.Displacement measurements were also used to assess selected support member load and elongation mobilisation per mining activity.It was found that a combined static load and elongation capacity of reinforcing members was essential to maintaining excavation stability,while permitting gradual shedding of stress through controlled pillar sidewall displacements.

Evaluation and Forecasting of Elapsed Fatigue Life of Ship Structures by Analyzing Data from Full Scale Ship Structural Monitoring

This paper describes the activities carried out by CETENA in collaboration with the Italian Navy to assess the behavior of new FREMM frigates by means of an automatic hull monitoring system and to predict the expected fatigue life of ship structure by analyzing recorded data through a specifically developed post-processing tool.

The Aeronautic Equipment RMS Management, Monitoring System and Software Realization

This paper introduces the theory of system engineering on materiel into the management and monitoring of reliability, maintainability and supportability (RMS) activities in the aeronautic equipment's life cycle. In order to assure the science of RMS management, it analyzes the contents of RMS activities in a life cycle, provides the model of management and monitoring, and discusses the software realization of the management and monitoring system.

Batch process monitoring based on WGNPE–GSVDD related and independent variables

In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independent method, and in the feature extraction there is not fully taken into account the characterization of fault information, it will make the process monitoring ineffective, so a fault monitoring method based on WGNPE(weighted global neighborhood preserving embedding)–GSVDD(greedy support vector data description) related and independent variables is proposed. First, mutual information method is used to separate the related variables and independent variables. Secondly, WGNPE method is used to extract the local and global structures of the related variables in batch process and highlight the fault information, GSVDD method is used to extract the process information of the independent variables quickly and effectively. Finally, the statistical monitoring model is established to achieve process monitoring based on WGNPE and GSVDD. The effectiveness of the proposed method was verified by the penicillin fermentation process.

Gastroesophageal manometry and 24-hour double pH monitoring in neonates with birth asphyxia

INTRODUCTIONBirth asphyxia may lead to disturbances of gastroenteric motility of newborn infants[1.2] . The change of gut pressure and reflux are the major manifestations of the motor disturbance [3-9] . To evaluate the effects of perinatal asphyxia on the gastroenteric motility, gastric and esophageal pressure and double pH were measured in a group of asphyxiated newborns. And. their pathophysiological and anatomical effects on gastroenteric function were discussed.

Damage evaluation of notched aluminum alloy and weld based on acoustic emission and digital image monitoring

Acoustic emission （ AE ） features during the fracture process of notched wrought aluminum alloy 7N01 and weld were investigated under the three-point bending load. Wavelet transform is used to investigate the time-frequency features of AE signals during the test. The experimental results showed that AE energy was effective indicators to detect the crack initiation for 7N01 aluminum. The digital images from monitoring the notch tip region of 7 NO1 aluminum sample verify the prediction of AE signals. The weld emits low energy, weak signal strength, and low peak amplitude, while stronger AE energy, amplitude, and more AE event counts for the base metal. In short, the AE technique was more sensitive to the changes in the fracture mode and could be used to monitor the damage development in welded structures.