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and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution.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.

A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region

Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.

Overview of the CMIP6 Historical Experiment Datasets with the Climate System Model CAS FGOALS-f3-L

The three-member historical simulations by the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System model,version f3-L(CAS FGOALS-f3-L),which is contributing to phase 6 of the Coupled Model Intercomparison Project(CMIP6),are described in this study.The details of the CAS FGOALS-f3-L model,experiment settings and output datasets are briefly introduced.The datasets include monthly and daily outputs from the atmospheric,oceanic,land and sea-ice component models of CAS FGOALS-f3-L,and all these data have been published online in the Earth System Grid Federation(ESGF,https://esgf-node.llnl.gov/projects/cmip6/).The three ensembles are initialized from the 600th,650th and 700th model year of the preindustrial experiment(piControl)and forced by the same historical forcing provided by CMIP6 from 1850 to 2014.The performance of the coupled model is validated in comparison with some recent observed atmospheric and oceanic datasets.It is shown that CAS FGOALS-f3-L is able to reproduce the main features of the modern climate,including the climatology of air surface temperature and precipitation,the long-term changes in global mean surface air temperature,ocean heat content and sea surface steric height,and the horizontal and vertical distribution of temperature in the ocean and atmosphere.Meanwhile,like other state-of-the-art coupled GCMs,there are still some obvious biases in the historical simulations,which are also illustrated.This paper can help users to better understand the advantages and biases of the model and the datasets。

The CMIP6 Historical Simulation Datasets Produced by the Climate System Model CAMS-CSM

This paper describes the historical simulations produced by the Chinese Academy of Meteorological Sciences(CAMS)climate system model(CAMS-CSM),which are contributing to phase 6 of the Coupled Model Intercomparison Project(CMIP6).The model description,experiment design and model outputs are presented.Three members’historical experiments are conducted by CAMS-CSM,with two members starting from different initial conditions,and one excluding the stratospheric aerosol to identify the effect of volcanic eruptions.The outputs of the historical experiments are also validated using observational data.It is found that the model can reproduce the climatological mean states and seasonal cycle of the major climate system quantities,including the surface air temperature,precipitation,and the equatorial thermocline.The long-term trend of air temperature and precipitation is also reasonably captured by CAMS-CSM.There are still some biases in the model that need further improvement.This paper can help the users to better understand the performance and the datasets of CAMS-CSM.

China Seismo-Electromagnetic Satellite search coil magnetometerdata and initial results

Four levels of the data from the search coil magnetometer(SCM) onboard the China Seismo-Electromagnetic Satellite(CSES)are defined and described. The data in different levels all contain three components of the waveform and/or spectrum of the induced magnetic field around the orbit in the frequency range of 10 Hz to 20 kHz; these are divided into an ultra-low-frequency band(ULF,10–200 Hz), an extremely low frequency band(ELF, 200–2200 Hz), and a very low frequency band(VLF, 1.8–20 kHz). Examples of data products for Level-2, Level-3, and Level-4 are presented. The initial results obtained in the commission test phase demonstrated that the SCM was in a normal operational status and that the data are of high enough quality to reliably capture most space weather events related to low-frequency geomagnetic disturbances.

CAS FGOALS-f3-H Dataset for the High-Resolution Model Intercomparison Project(HighResMIP)Tier 2

Following the High-Resolution Model Intercomparison Project(HighResMIP)Tier 2 protocol under the Coupled Model Intercomparison Project Phase 6(CMIP6),three numerical experiments are conducted with the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System Model,version f3-H(CAS FGOALS-f3-H),and a 101-year(1950–2050)global high-resolution simulation dataset is presented in this study.The basic configuration of the FGOALSf3-H model and numerical experiments design are briefly described,and then the historical simulation is validated.Forced by observed radiative agents from 1950 to 2014,the coupled model essentially reproduces the observed long-term trends of temperature,precipitation,and sea ice extent,as well as the large-scale pattern of temperature and precipitation.With an approximate 0.25°horizontal resolution in the atmosphere and 0.1°in the ocean,the coupled models also simulate energetic western boundary currents and the Antarctic Circulation Current(ACC),reasonable characteristics of extreme precipitation,and realistic frontal scale air-sea interaction.The dataset and supporting detailed information have been published in the Earth System Grid Federation.

Datasets for the CMIP6 Scenario Model Intercomparison Project (ScenarioMIP) Simulations with the Coupled Model CAS FGOALS-f3-L

The datasets for the tier-1 Scenario Model Intercomparison Project(ScenarioMIP)experiments from the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System model,finite-volume version 3(CAS FGOALS-f3-L)are described in this study.ScenarioMIP is one of the core MIP experiments in phase 6 of the Coupled Model Intercomparison Project(CMIP6).Considering future CO2,CH4,N2O and other gases’concentrations,as well as land use,the design of ScenarioMIP involves eight pathways,including two tiers(tier-1 and tier-2)of priority.Tier-1 includes four combined Shared Socioeconomic Pathways(SSPs)with radiative forcing,i.e.,SSP1-2.6,SSP2-4.5,SSP3-7.0 and SSP5-8.5,in which the globally averaged radiative forcing at the top of the atmosphere around the year 2100 is approximately 2.6,4.5,7.0 and 8.5 W m−2,respectively.This study provides an introduction to the ScenarioMIP datasets of this model,such as their storage location,sizes,variables,etc.Preliminary analysis indicates that surface air temperatures will increase by about 1.89℃,3.07℃,4.06℃ and 5.17℃ by around 2100 under these four scenarios,respectively.Meanwhile,some other key climate variables,such as sea-ice extension,precipitation,heat content,and sea level rise,also show significant long-term trends associated with the radiative forcing increases.These datasets will help us understand how the climate will change under different anthropogenic and radiative forcings.

ESSENTIAL RELATIONSHIP BETWEEN DOMAIN-BASED ONE-CLASS CLASSIFIERS AND DENSITY ESTIMATION

One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships.

Interactive early warning technique based on SVDD

After reviewing current researches on early warning, it is found that “bad”data of some systems is not easy to obtain, which makes methods proposed by these researches unsuitable for monitored systems. An interactive early warning technique based on SVDD （support vector data description） is proposed to adopt “good” data as samples to overcome the difficulty in obtaining the “bad” data. The process consists of two parts： （1） A hypersphere is fitted on “good” data using SVDD. If the data object are outside the hypersphere, it would be taken as “suspicious”; （2） A group of experts would decide whether the suspicious data is “bad” or “good”, early warning messages would be issued according to the decisions. And the detailed process of implementation is proposed. At last, an experiment based on data of a macroeconomic system is conducted to verify the proposed technique.

Rolling bearing performance degradation evaluation by VMD and embedding selection-based NPE

In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding(ESNPE)method is proposed.Firstly,the acquired vibration signals are decomposed by variational mode decomposition(VMD),and the singular value and relative energy of each intrinsic mode function(IMF)are extracted to form a high-dimensional feature set.Then,the NPE manifold learning method is used to extract the embedded features in the feature space.Considering the problem that useful embedding information is easily suppressed in NPE,an embedding selection strategy is built based on the Spearman correlation coefficient.The effectiveness of embeddings is measured by the coefficient absolute value,and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method.Finally,the degradation index is established using the support vector data description(SVDD)model and bearing performance degradation evaluation is achieved.The proposed method was tested with the whole life experiment data of a rolling bearing,and the result was compared with the feature extraction methods of traditional principal component analysis(PCA)and NPE.The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index.

Life prediction of ZPW-2000A track circuit equipment based on SVDD and gray prediction

Evaluation of the health state and prediction of the remaining life of the track circuit are important for the safe operation of the equipment of railway signal system.Based on support vector data description(SVDD)and gray prediction,this paper illustrates a method of life prediction for ZPW-2000A track circuit,which combines entropy weight method,SVDD,Mahalanobis distance and negative conversion function to set up a health state assessment model.The model transforms multiple factors affecting the health state into a health index named H to reflect the health state of the equipment.According to H,the life prediction model of ZPW-2000A track circuit equipment is established by means of gray prediction so as to predict the trend of health state of the equipment.The certification of the example shows that the method can visually reflect the health state and effectively predict the remaining life of the equipment.It also provides a theoretical basis to further improve the maintenance and management for ZPW-2000A track circuit.

A Method of Shield Attitude Working Condition Classification

Aiming at solving shield attitude rectification failure problem,a method of shield working condition classification based on support vector data description( SVDD) was introduced. Shield attitude mechanics model containing priori knowledge was helpful to feature selection. SVDD handled the one class classification problem and a decision function for attitude rectification was proposed. Experimental results indicate that the method is able to accomplish the shield attitude working condition classification.

Power transmission risk assessment considering component condition

This paper proposes a new method for power transmission risk assessment considering historical failure statistics of transmission systems and operation failure risks of system components.Component failure risks are integrated into the new method based on operational condition assessment of components using the support vector data description(SVDD)approach.The traditional outage probability model of transmission lines has been modified to build a new framework for power transmission system risk assessment.The proposed SVDD approach can provide a suitable mechanism to map component assessment grades to failure risks based on probabilistic behaviors of power system failures.Under the new method,both up-todate component failure risks and traditional system risk indices can be processed with the proposed outage model.As a result,component failure probabilities are not only related to historical statistic data but also operational data of components,and derived risk indices can reflect current operational conditions of components.In simulation studies,the SVDD approach is employed to evaluate component conditions and link such conditions to failure rates using up-to-date component operational data,including both on-line and off-line data of components.The IEEE 24-bus RTS-1979 system is used to demonstrate that component operational conditions can greatly affect the overall transmission system failure risks.