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.

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.

Design and implementation of an expert system for remote fault diagnosis in ship lift

In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship lift was discussed, and the expert system developed was realized on the VPN virtual network. The system was applied to the Gaobaozhou ship lift project, and it ran successfully.

STUDY OF REAL-TIME EXPERT SYSTEM TOOL FOR INDUSTRIAL FAULT MONITORING AND DIAGNOSIS

From the requirements of industrial production,an integrated fault monitoring,diagnosis and repairing system is suggested in this paper. This new scheme of fault monitoring and diagnosis system is realized by a master-slave real-time expert system,and a real-time expert system tool for this system is also developed accordingly. As an example of application of this tool ,a realtime expert system for fault monitoring and diagnosis on DC mine hoist is developed. Experiments show that this tool possesses better supporting environment, strong knowledge acquisition ability, and convenience for use. The system developed by this tool not only meets the real-time requirement of DC hoist,but also can give correct diagnosis results.

Expert fault diagnosis system for leaching process in zinc hydrometallurgy

Leaching process is the first step in zinc hydrometallurgy, which involves the complex chemical reactions for dissolving zinc bearing material in dilute sulfuric acid. Ensuring the safe running of the process is a key point in the operation. An expert fault diagnosis system for the leaching process was proposed, which has been implemented in a nonferrous metals smeltery. The system architecture and the diagnosis procedure were presented, and the rule models with the certainty factor were constructed based on the empirical knowledge, empirical data and statistical results on past fault countermeasures, and an expert reasoning strategy was proposed which employs the rule models and Beyes presentation and combines forward chaining and backward chaining. [

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.

Knowledge Processing Method of Fault Diagnosis Expert Systems for Letter Sorting Equipment

Based on the analysis of fault diagnosis knowledge of letter sorting machine, this paper proposes a processing method by which the fault diagnosis knowledge is divided into exact knowledge, inadequate knowledge and fuzzy knowledge. Then their presenting and implementing form in fault diagnosis expert system is discussed and studied. It is proved that the expert system has good feasibility in the field of the diagnosis of letter sorting machine.

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 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.

Fault Diagnosis of Mechanical Equipments Through Spectrometric oil Analysis for Worn Off Metallic Elements

In the fault prediction of mechanical equipments through spectromectric oil analysis for worn off debris, a method for the determination of the limiting value of wear is proposed and discussed. In order to diagnose the impending failure and to predict the fault modes and locate the fault spots, a comprehensive approach is studied and outlined on the basis of methods of discriminative analysis and fuzzy logic. A fault diagnosis expert system OAFDS developed by the authors for the nonitoring of working conditions of the ND5 locomotive diesel engine Nd5 is briefly introduced.

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.

Intelligent Examination Monitoring and Maintenance for the Safety of Operation of Flexible Manufacturing Systems

Based on the system of electric power supply for flexible manufacturing systems (FMS), a study has been carried out on the intelligent safety examination, monitoring and maintenance of its running environment. On the basis of the specific feature of the power supply network of an FMS, real time monitoring system of the power supply network and the fault diagnostic expert system for the power equipment have been designed. This system can diagnose not only definite fault phenomena, but also fuzzy, uncertain fault phenomena as well. Fault diagnostic knowledge base for the power equipment has been founded hierarchy architecture model and the method of fault tree analysis. Feasibility of this system has been proved by computer simulation.

On the Application of PCA Technique to Fault Diagnosis

In this paper, we bdefly address the application of the standard principal component analysis （PCA） technique to fault detection and identification. Based on an analysis of the existing test statistic, we propose a new test statistic, which is similar to the Hawkin＇s T2 H statistic but without the numerical drawback. In comparison with the SPE index, the threshold setting associated with the new statistic is computationally simpler. Our further study is dedicated to the analysis of fault sensitivity. We consider the off-set and scaling faults, and evaluate the test statistic by viewing its sensitivity to the faults. Our final study focuses on identifying off-set and scaling faults. To this end, two algorithms are proposed. This paper also includes some critical remarks on the application of the PCA technique to fault diagnosis.

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.

Multi-layer Contribution Propagation Analysis for Fault Diagnosis

The recent development of feature extraction algorithms with multiple layers in machine learning and pattern recognition has inspired many applications in multivariate statistical process monitoring. In this work, two existing multi-layer linear approaches in fault detection are reviewed and a new one with extra layer is proposed in analogy. To provide a general framework for fault diagnosis in succession, this work also proposes the contribution propagation analysis which extends the original definition of contribution of variables in multivariate statistical process monitoring. In fault diagnosis stage, the proposed contribution propagation analysis for multilayer linear feature extraction algorithms is compared with the fault diagnosis results of original contribution plots associated with single layer feature extraction approach. Plots of variable contributions obtained by the aforementioned approaches on the data sets collected from a simulated benchmark case study(Tennessee Eastman process) as well as an industrial scale multiphase flow facility are presented as a demonstration of the usage and performance of the contribution propagation analysis on multi-layer linear algorithms.

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.

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。

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.

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。