On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

In the past decades, on-line monitoring of batch processes using multi-way independent component analysis (MICA) has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and determining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components, we introduce a novel concept of system deviation, which is able to evaluate the reconstructed observations with different independent components. The monitored statistics are transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-batch penicillin fermentation simulator, and the experimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.

Analysis and Application of the Synthetic Relative Measuring Method in On-Line Monitoring for Capacitive Equipment in Power Systems

On-line measurement for dielectric loss angle can effectively monitor the insulation condition of capacitive equipment in power systems. Synthetic relative measuring methods not only markedly overcome the shortcomings of traditional absolute measuring methods but also greatly improve the accuracy of dielectric loss angle measurement. However, synthetic relative measuring methods based on two or three pieces of capacitive equipment do not have the characteristic of generality. In this paper, a principle of synthetic relative measuring method is presented. The example of application for synthetic relative methods based on three and four pieces of capacitive equipment running in the same phase is taken to present the failure judgment matrices for N pieces of equipment. According to these matrices, the fault condition of N pieces of capacitive equipment can be watched, which is more general. Then some problems needing to be concerned along with two diagnostic methods used in diagnostic system are introduced. Finally, two programmable flow charts for the two methods are given and corresponding examples demonstrate their feasibility in practice.

Mechanics Analysis of Overhead Transmission Lines Based On-line Monitoring

At present, the on-line monitoring is widely applied to the power line monitoring. In this paper, a new mechanical calculation model is established according to the on-line monitoring. And this model is based on the parameters that tension sensors and angle sensors on suspended points detect, and combines with the parameters of the wire itself, and also considers the deflection angel of wires due to wind. In this model, mechanics parameters of wires are turned into the new coordinate plane after deflection angel of wires due to wind, or windage yaw plane. A statics tension balance equation is built in the vertical direction of the new windage yaw plane. According to the theoretical analysis and algorithm, we verify the accuracy of this newly developed mechanical calculation model.

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.

On-line Batch Process Monitoring and Diagnosing Based on Fisher Discriminant Analysis

A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis (FDA) was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the results were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.

Analysis of Effluent Outfall Problems and Their Classification and Regulation Countermeasures

Effluent outfalls are an important exit for pollutants discharged from the source flowing into environmental water bodies,as well as an important guarantee for the ecological environment of natural water bodies.In response to main problems of large and diverse effluent outfalls,as well as their monitoring analysis,tracing and regulation in China,classification and regulation countermeasures were proposed based on the characteristics of effluent outfalls.It is suggested that a comprehensive management and control system should be built by improving the management and control system,upgrading monitoring techniques and strengthening social supervision and public education,so as to provide a scientific basis for the supervision and management of effluent outfalls in China and help promote the improvement of water quality and the sustainable development and utilization of water resources.

A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Slope deformation partitioning and monitoring points optimization based on cluster analysis

The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.

Stylistic Analysis on English On-line Chat

Based on the fact that on-line chat has become the most developing language form in the information age, this article point out the stylistic features of on-line English chat. Though in written language form, such language is spoken language in nature, thus it is worthwhile to analyze this special phaenomenon in lexical and grammatical level.

Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data

At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.

Analysis and application of automatic deformation monitoring data for buildings and structures of mining area

The buildings and structures of mines were monitored automatically using modern surveying technology. Through the analysis of the monitoring data, the deformation characteristics were found out from three aspects containing points, lines and regions, which play an important role in understanding the stable state of buildings and structures. The stability and deformation of monitoring points were analysed, and time-series data of monitoring points were denoised with wavelet analysis and Kalman filtering, and exponent function and periodic function were used to get the ideal deformation trend model of monitoring points. Through calculating the monitoring data obtained, analyzing the deformation trend, and cognizing the deformation regularity, it can better service mine safety production and decision-making.

Numerical analysis and geophysical monitoring for stability assessment of the Northwest tailings dam at Westwood Mine

The Westwood Mine aims to reuse the tailings storage facility #1(TSF #1) for solid waste storage, but,downstream of the Northwest dike is considered critical in terms of stability. This paper uses numerical modeling along with geophysical monitoring for assessing the Northwest dike stability during the restoration phase. The impact of waste rock deposition in the upstream TSF #1 is considered. The geophysical monitoring is based on electrical resistivity methods and was used to investigate the internal structure of the dike embankment in different deposition stages. The numerical simulations were performed with SLOPE/W code. The results show a factor of safety well above the minimum recommended value of 1.5. Geophysical monitoring revealed a vertical variation in the electrical resistivity across the dike, which indicates a multilayer structure of the embankment. Without any current in situ data, the geophysical monitoring helped estimating the nature of the materials used and the internal structure of the embankment. These interpretations were validated by geological observation of geotechnical log of the embankment. Based on this study, it is recommended that the water polishing pond be partly filled before waste rock is deposited in TSF #1. In addition, to ensure the stability of the dike, the piezometric head monitoring prior to and during waste rock deposition is recommended.

Structural health monitoring of long-span suspension bridges using wavelet packet analysis

During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vibration-based health monitoring methods is to seek some damage indices that are sensitive to structural damage, This paper proposes an online structural health monitoring method for long-span suspension bridges using wavelet packet transform （WPT）. The WPT- based method is based on the energy variations of structural ambient vibration responses decomposed using wavelet packet analysis. The main feature of this method is that the proposed wavelet packet energy spectrum （WPES） has the ability to detect structural damage from ambient vibration tests of a long-span suspension bridge. As an example application, the WPES-based health monitoring system is used on the Runyang Suspension Bridge under daily environmental conditions. The analysis reveals that changes in environmental temperature have a long-term influence on the WPES, while the effect of traffic loadings on the measured WPES of the bridge presents instantaneous changes because of the nonstationary properties of the loadings. The condition indication indices VD reflect the influences of environmental temperature on the dynamic properties of the Runyang Suspension Bridge. The field tests demonstrate that the proposed WPES-based condition indication index VD is a good candidate index for health monitoring of long-span suspension bridges under ambient excitations.

Characteristics analyzing and parametric modeling of the arc sound in CO_2 GMAW for on-line quality monitoring

For on-line monitoring of welding quality, the characteristics of the arc sound signals in short circuit CO2 GMAW were analyzed in the time and frequency domains. The arc sound presents a series of ringing-like oscillations that occur at the end of short circuit i. e. the moment of arc re-ignition, and distributes mainly in the frequency band below 10 kHz. A concept of the arc tone channel and its equivalent electrical model were suggested, which is considered a time-dependent distributed parametric system of which the transmission properties depend upon the geometric and physical characteristics of the arc and surroundings, and is excited by the sound source results from the change of arc energy so that results in arc sound. The linear prediction coding （ LPC ） model is an estimation of the tone channel. The radial basis function （ RBF ） neural networks were built for on-line pattern recognition of the gas-lack in welding, in which the input vectors were formed with the LPC coefficients. The test results proved that the LPC model of arc sound and the RBF networks are feasible in on-line quality monitoring.

Monitoring and analysis of nonlinear dynamic damage of transport roadway supported by composite hard rock materials in Linglong Gold Mine

The study concentrates mainly on the development of failure process incomposite rock mass. By use of acoustic emission (AE), convergence inspection, pressure monitoring,level measurement techniques and the modem signal analysis technology, as well as scan electronmicroscopy (SEM) experiment, various aspects of nonlinear dynamic damage of composite rock masssurrounding the transport roadway in Linglong gold mine are discussed. According to the monitoringresults, the stability of the rock mass can be synthetically evaluated, and the intrinsic relationbetween the damage and the characteristic parameters of acoustic emission can be determined. Thelocation of the damage of rock mass can also be detected based on the acoustic emission couplemonitoring signals. Finally, the key factors which influence the stability of the transport roadwaysupported by composite hard rock materials are found out.

Monitoring and Analysis on Impact of Gezhouba Hydroproject on Downstream River Course

Gezhouba hydroproject was impounded and put into operation in 1981. In order to analyse the impact on the downstream river course, Jingjiang Hydrologic and Water Resources Survey Bureau of Changjiang Water Resources Commission has conducted prototype observation on the Yichang-Chenglingji river stretch. On basis of the observed data, the change in river regime and scour-sedimentation evolution and water surface profile below the dam are analysed systematically. The results show that the scouring of downstream river course mainly stretches from Yichang to Ouchikou, mainly occurring in 1980～1987; the scourring mainly takes place in the river channel and the water surface profile drops significantly in dry season,but not quite in high flood season.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Non-contact monitoring and analysis system for tunnel surrounding rock deformation of underground engineering

It is very important to monitor surrounding rock deformation in tunnel construction. The principle, function, development and application of the system composed of a total station and computer for monitoring and analyzing surrounding rock deformation were discussed. The new methods of two free station of 3D measurement and its mathematic adjustment mode were presented. The development of software for total station on-board and post for computer were also described. Without centering it and measuring its height, the total station controlled by the software on-board can fulfill the whole measurements to target points. Monitoring data can be processed by the post software and results of regression analysis, forecasting information of the tunnel surrounding rock deformation can be provided in time. The practical use shows that this system is practicable, highly accurate and efficient. It satisfies the needs of safety and information construction in tunnel construction of underground engineering.

Structural Stress Monitoring and FEM Analysis of the Cutting Operation of the Main Bracket of A Semi-Submersible Platform

For a semi-submersible platform in repair, the eight old main brackets which connect columns with pontoons need to be replaced by new ones. In order to ensure the safety of the cutting operation of the old main bracket and calculate the initial stress condition of new main bracket, the structural stress monitoring of eight key spots is carried out, and then the calibrated finite element model is established according to the field monitoring results. Before cutting the main bracket and all associated structures, eight rectangular rosettes were installed, and a tailored cutting scheme was proposed to release the initial stress, in which the main bracket and associated column and pontoon plates were partly cut. During the cutting procedure, the strains of the monitoring spots were measured, and then the structural stress of the monitored spots were obtained. The stress variation characteristics at different spots during the initial cutting operation were shown and the initial stress condition of the monitored spots was figured out. The loading and support conditions of the semi-submersible platform were calibrated based on the measured initial stress condition, which made the finite element model more credible. The stress condition with the main bracket and associated structures being entirely cut out is analyzed by the Finite Element Method (FEM), which demonstrates the cutting operation to be safe and feasible. In addition, the calibrated finite element model can be used to calculate the initial stress condition of the new main bracket, which will be very helpful for the long-term stress monitoring on the main bracket.