Nonlinear online process monitoring and fault diagnosis of condenser based on kernel PCA plus FDA

A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed.The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calculated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or tilling in the unknown portion of the process variables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of penicillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.

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.

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calcu- lated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process vari- ables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of peni- cillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

An Incremental Model Transfer Method for Complex Process Fault Diagnosis

Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.

Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

Research on the Correlation Between Oil Menitoring and Vibration Monitoring in Information Collecting and Processing Monitoring

Oil monitoring and vibration monitoring are two principal techniques for mechanical fault diagnosis and condition monitoring at present. They monitor the mechanical condition by different approaches, nevertheless, oil and vibration monitoring are related in information collecting and processing. In the same mechanical system, the information obtained from the same information source can be described with the same expression form. The expressions are constituted of a structure matrix, a relative matrix and a system matrix. For oil and vibration monitoring, the information source is correlation and the collection is independent and complementary. And oil monitoring and vibration monitoring have the same process method when they yield their information. This research has provided a reasonable and useful approach to combine oil monitoring and vibration monitoring.

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 resuits 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.

Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors

Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown.Recently,Motor Current Signature Analysis(MCSA)is widely reported as a condition monitoring technique in the detection and identification of individual andmultiple Induction Motor(IM)faults.However,checking the fault detection and classification with deep learning models and its comparison among them selves or conventional approaches is rarely reported in the literature.Therefore,in this work,wepresent the detection and identification of induction motor faults with MCSA and three Deep Learning(DL)models namely MLP,LSTM,and 1D-CNN.Initially,we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults(SWF)using Fast Fourier Transform(FFT),Short Time Fourier Transform(STFT),and Continuous Wavelet Transform(CWT)to detect and identify the healthy and unhealthy conditions with phase to ground,single phasing and in multiple fault conditions using Motor Current Signature Analysis.The faults impact on stator current is presented in the time and frequency domain(i.e.,power spectrum).The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault conditions.This is further investigated with three deep learning models(i.e.,MLP,LSTM,and 1D-CNN)for checking the fault detection and identification(i.e.,classification)improvement in a three-phase induction motor.By simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB,we have collected current signature data in the time domain,labeled them accordingly and created the 50 thousand samples dataset for DL models.All the DL models are trained and validated with a suitable number of architecture layers.By simulation,the multiclass confusion matrix,precision,recall,and F1-score are obtained in several conditions.The result shows that the stator current signature of the motor can be used to detect individual and multiple faults.Moreover,deep learning models can efficiently classify the induction motor faults based on time-domain data of the stator current signature.In deep learning(DL)models,the LSTM has shown better accuracy among all other three models.These results show that employing deep learning in fault detection and identification of induction motors can be very useful in predictive maintenance to avoid shutdown and production cycle stoppage in the industry.

Monitoring and diagnosis of complex production process based on free energy of Gaussian–Bernoulli restricted Boltzmann machine

Online monitoring and diagnosis of production processes face great challenges due to the nonlinearity and multivariate of complex industrial processes.Traditional process monitoring methods employ kernel function or multilayer neural networks to solve the nonlinear mapping problem of data.However,the above methods increase the model complexity and are not interpretable,leading to difficulties in subsequent fault recognition/diagnosis/location.A process monitoring and diagnosis method based on the free energy of Gaussian-Bernoulli restricted Boltzmann machine(GBRBM-FE)was proposed.Firstly,a GBRBM network was established to make the probability distribution of the reconstructed data as close as possible to the probability distribution of the raw data.On this basis,the weights and biases in GBRBM network were used to construct F statistics,which represents the free energy of the sample.The smaller the energy of the sample is,the more normal the sample is.Therefore,F statistics can be used to monitor the production process.To diagnose fault variables,the F statistic for each sample was decomposed to obtain the Fv statistic for each variable.By analyzing the deviation degree between the corresponding variables of abnormal samples and normal samples,the cause of process abnormalities can be accurately located.The application of converter steelmaking process demonstrates that the proposed method outperforms the traditional methods,in terms of fault monitoring and diagnosis performance.

Variable selection-based SPC procedures for high-dimensional multistage processes

Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average(MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length(ARL) for the detection of process mean shift.

基于双层自适应集成残差主成分分析的复杂非线性过程监测

多元统计监测方法常使用正常数据选取特征,而现实过程中,不同的故障将影响不同的特征,并且这些特征可能随着时间和控制系统的作用而变化。当故障发生并随时间变化时,要想获得更好的故障检测能力,就需要聚集有效的故障敏感特征。本文提出了一种双层自适应集成残差主成分分析(AERPCA)模型,其子模型包含不同的特征,并突出地呈现一个或多个相关故障。首先,根据正常数据计算主成分分析(PCA)特征,利用不同特征构建线性子模型和相应的残差空间。考虑到残差空间的非线性特性及有效特征更为分散,采用核PCA(KPCA)提取不同的特征并组成同一残差空间下不同KPCA子模型。然后,利用贝叶斯方法获取集成KPCA子模型,完成各残差空间的划分和集成。最后,在主空间中获得多个线性子模型以及在残差空间中获得多个集成的非线性子模型后,利用滑动窗口确定当前时刻监控效果最好的模型。采用田纳西-伊士曼过程验证了AERPCA的有效性。

课程思政融入研究生课程的教学改革与探索——以复杂过程监测与故障诊断课程为例

过程状态监测与故障诊断在现代制造业和生产系统中扮演着至关重要的角色。该文以研究生课程复杂过程监测与故障诊断为例,聚焦课程思政元素挖掘不充分、教学形式缺乏创新、实施效果难以评价等难点问题,探索课程思政教学改革模式,提出明确思政育人目标、深挖课程思政元素、创新思政教学形式、开拓产学研赛育人模式和完善思政教学评价体系等改革策略,全面推动课程思政建设,提升学生的综合能力和思政素养,促进学生的全方面发展。

基于预测模糊FLC控制下农业生产过程监控与优化

该文以温室监控系统为案例,针对传统PLC控制在复杂环境下的局限性,提出了一种基于预测模糊FLC控制的优化技术,该技术利用模糊逻辑对环境变量进行模糊化处理,并结合预测算法对未来状态进行预测,从而实现对温室环境的精准控制和优化调节。通过试验验证,该优化技术能够提高温室生产效率、降低能耗成本,并保障农作物的生长质量和产量稳定性。研究结果可以为农业生产过程的智能化监控与优化提供新的思路和方法。

致密气田大井组井场工况智能监控优化研究与应用

随着气田大井组井场建设、气井排水措施和集中监控模式推广,生产监控范围、监控数据点数呈规模化扩大,常规数字化监控技术已不能满足井场工况判断、故障识别要求。通过分析大井组井场不同气井生产方式对监控的影响,提出工况动态分析方法,提高了判断效率和准确性,降低了人工分析判断强度。

电网的智能监测与故障诊断

随着电网的不断发展,确保电力系统的稳定运行成为至关重要的任务。文章探讨了电网智能监测与故障诊断技术的应用,突出其在提高电网运行可靠性方面的关键作用。研究方法主要包括数据采集、处理分析以及故障检测方法的应用。通过先进的数据采集方式和工具,强调数据的实时性和全面性,电网智能监测系统能够更好地适应复杂多变的电力系统环境,为电网的安全稳定运行提供有力的支持。

基于简化区间核全局−局部特征融合的采煤机智能故障诊断

采煤机作为煤炭开采的主要装置之一,其健康状态受工作环境恶劣、操作空间狭窄等因素影响而难以准确监测,且极易受到煤岩的冲击而发生故障,直接影响采煤机工作效率。此外,由于采煤机特殊的工作环境,使采集的振动数据极易受到各种因素的干扰而变的难以使用,影响其监测可靠性和智能化水平。为准确监测采煤机健康状态,以采煤机正常状态下的电流、温度、流量等容易获取的数据为基础,综合考虑数据集的全局和局部特征以避免其结构信息的丢失。利用主成分分析PCA(Principal Component Analysis)和局部保持投影LPP(Locality Preserving Projec-tions)构建的目标函数,结合互信息、核函数、区间内积估计和重构贡献的方法建立了一种基于简化区间核全局-局部特征融合SIKGLFF(Simplified interval kernel global-local feature fusion)的智能故障诊断方法,用于对表征采煤机状态的非线性不确定数据进行特征提取。并使用山西斜沟煤矿采煤机实际运行数据模拟故障和沙曲二号煤矿实际故障数据对所提方法的性能进行评估实验。结果表明,与中点-半径核PCA、核局部保持投影和区间核全局-局部特征融合算法相比,所提方法对采煤机的单变量模拟故障、多变量的截齿损耗和水路堵塞故障具有良好的监测效果,其故障监测准确率分别达到了99.90%、99.40%和98.70%,计算时间分别只有0.324、0.367和0.345 s,而且可以准确识别产生故障的相关变量,为采煤机故障位置的确定提供理论依据,也为工作人员维护性决策的准确实施指明了方向。

基于图嵌入长短时记忆神经网络的非线性动态过程监控与诊断

针对复杂工业过程存在的非线性、动态性,以及故障标签难获取等特征,提出一种图嵌入长短时记忆神经网络在线监控与故障诊断方法。首先,对正常工况下采集的多维时序数据进行图嵌入,获得结构信息。其次,采用图注意力神经网络融合结构信息,并将融合后的结构信息输入用于预测的长短时记忆神经网络中。最后,提出一种新的基于预测误差指标的非线性动态过程在线监控方法和基于因果分析图的故障诊断方法。采用田纳西-伊斯曼数据集进行实验验证,结果表明了所提方法的有效性。

煤矿供电安全监测系统应用

介绍了煤矿供电安全监测系统的定义和作用,详细阐述了系统的组成部分,探讨了系统的功能,分析了系统在实际应用中面临的问题和挑战,提出了进一步完善和发展煤矿供电安全监测系统的建议。