Diagnosis of process faults and sensor faults in a class of nonlinear uncertain systems

This paper presents a fault diagnosis method for process faults and sensor faults in a class of nonlinear uncertain systems.The fault detection and isolation architecture consists of a fault detection estimator and a bank of adaptive isolation estimators,each corresponding to a particular fault type.Adaptive thresholds for fault detection and isolation are presented.Fault detectability conditions characterizing the class of process faults and sensor faults that are detectable by the presented method are derived.A simulation example of robotic arm is used to illustrate the effectiveness of the fault diagnosis method.

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.

Advancements in Photovoltaic Panel Fault Detection Techniques

This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.

The Dihedral Angle and Intersection Processes of a Conjugate Strike-Slip Fault System in the Tarim Basin, NW China

Recent studies, focused on dihedral angles and intersection processes, have increased understandings of conjugate fault mechanisms. We present new 3-D seismic data and microstructural core analysis in a case study of a large conjugate strike-slip fault system from the intracratonic Tarim Basin, NW China. Within our study area, ＂X＂ type NE and NW trending faults occur within Cambrian- Ordovician carbonates. The dihedral angles of these conjugate faults have narrow ranges, 19~ to 62~ in the Cambrian and 26~ to 51~ in the Ordovician, and their modes are 42~ and 44~ respectively. These data are significantly different from the ~60~ predicted by the Coulomb fracture criterion. It is concluded that： （1） The dihedral angles of the conjugate faults were not controlled by confining pressure, which was low and associated with shallow burial; （2） As dihedral angles were not controlled by pressure they can be used to determine the shortening direction during faulting; （3） Sequential slip may have played an important role in forming conjugate fault intersections; （4） The conjugate fault system of the Tarim basin initiated as rhombic joints; these subsequently developed into sequentially active ＂X＂ type conjugate faults; followed by preferential development of the NW-trending faults; then reactivation of the NE trending faults. This intact rhombic conjugate fault system presents new insights into mechanisms of dihedral angle development, with particular relevance to intracratonic basins.

Fault diagnosis for distillation process based on CNN–DAE

Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.

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. [

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 Dynamic Model Correction Based Fault Diagnosis in Chemical Processes

<Abstract>A novel fault detection and diagnosis method was proposed, using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur. It aims at parameter failures, and the parameters are updated via on-line correction. As it can predict the trend of process and determine the existence of malfunctions simultaneously, this method does not need to design problem-specific observer to estimate unmeasured state variables. Application of the proposed method is presented on one water tank and one aromatization reactor, and the results are compared with those from the traditional method.

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.

Nonlinear Statistical Process Monitoring and Fault Detection Using Kernel ICA

A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis(ICA) is proposed.The kernel ICA method is a two-phase algorithm:whitened kernel principal component(KPCA) plus ICA.KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel.ICA seeks the projection directions in the KPCA whitened space,making the distribution of the projected data as non-gaussian as possible.The application to the fluid catalytic cracking unit(FCCU) simulated process indicates that the proposed process monitoring method based on kernel ICA can effectively capture the nonlinear relationship in process variables.Its performance significantly outperforms monitoring method based on ICA or KPCA.

Data Processing of Fault Recorder in Power System

This article studies the fault recorder in power system and introduces the Comtrade format. Andituses C++ programming to read recorded fault data and adopts Fourier analysis and symmetrical component method to filter and extract fundamental waves. Finally the effectiveness of the data processing method introduced in this paper is verified by CAAP software.

The research on the space-time coherence of Xianshuihe fault zone in the process of seismogeny

In this paper, a kind of data analysis method is used to research the space time coherence of Xianshuihe fault zone in the process of seimogeny. It is proved that, the space time coherence of Xianshuihe fault zone, in the process of seismogeny, really exists. The greater the earthquake magnitude of a fault section, the greater is its influence on the 'neighboring' sections. To a strike fault zone which bears compressive stress, the fault sections are 'series connected'. That is to say, once an earthquake happens on a fault section, the time of seismogeny in the neighboring fault sections will be shortened, therefore, this earthquake has 'an effect of hastening the occurrence of earthquakes in neighboring fault sections'. It is an inevitable rule for the Xianshuihe fault zone that the earthquake quiescent period and the earthquake active period occur alternatively. To the Xianshuihe fault zone, the slip predictable model does not tally with the actual situation, while the improved time predictable model tallies with the fact quite well. The data analysis method used here, which is used to examine the earthquake prediction models, is put forward on the basis of historical earthquake records. This kind of method and the relative conclusions have important reference value on the establishment of earthquake prediction models.

Study and Application of Case-based Extension Fault Diagnosis for Chemical Process

In chemical processes, fault diagnosis is relatively difficult due to the incomplete prior-knowledge and unpredictable production changes. To solve the problem, a case-based extension fault diagnosis (CEFD) method is proposed combining with extension theory, in which the basic-element model is used for the unified and deep fault description, the distance concept is applied to quantify the correlation degree between the new fault and the original fault cases, and the extension transformation is used to expand and obtain the solution of unknown faults. With the application in Tennessee Eastman process, the result indicates that CEFD method has a flexible fault representation, objective fault retrieve performance and good ability for fault study, providing a new way for diagnosing production faults accurately.

Fault monitoring based on mutual information feature engineering modeling in chemical process

A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.

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.

On the Kinematic Characteristics and Dynamic Process of Boundary faults of the Nansha Ultra-crust Layer-Block

Abstract The Nansha ultra-crust layer-block is confined by ultra-crustal boundary faults of distinctive features, bordering the Kangtai-Shuangzi-Xiongnan extensional faulted zone on the north, the Baxian-Baram-Yoca-Cuyo nappe faulted zone on the south, the Wan'an-Natuna strike-slip tensional faulted zone on the west and the Mondoro-Panay strike-slip compressive faulted zone on the east. These faults take the top of the Nansha asthenosphere as their common detachmental surface. The Cenozoic dynamic process of the ultra-crust layer-block can be divided into four stages: K2-E21, during which the northern boundary faults extended, this ultra-crust layer-block was separated from the South China-Indosinian continental margin, the Palaeo-South China Sea subducted southwards and the Sibu accretion wedge was formed; E22-E31, during which the Southwest sub-sea basin extended and orogeny was active due to the collision of the Sibu accretion wedge; E32-N11, during which the central sub-sea basin extended, the Miri accretion wedge was formed and “A-type” subduction of the southern margin of the north Balawan occurred; N12-the present, during which large-scale thrusting and napping of the boundary faults in the south and mountain-building have taken place and the South China Sea stopped its extension.

SEDIMENTARY PROCESS OF THE CENOZOIC BASIN AND ITS RESPONSE TO THE SLIP-HISTORY OF THE ALTYN TAGH FAULT, NW CHINA

The NEE\|striking Altyn Tagh Fault (ATF) has been well known as one major point to know the growth history of the Tibetan plateau. Lots of investigations done since 1970’s were mostly focus on active features, particularly on determining slip, slip rate and their distribution along the fault. However, Cenozoic slip\|history of this fault remains poorly understood, and the age of initiation and total offset are controversial. Several Cenozoic sedimentary basins develop in Suo’erkulinan to Mangya regions (Fig.1). Their sedimentary processes are closely related with the ATF. The studies of the Neogene sedimentary sequences and the reconstruction of the paleo\|geography are essential to establish the displacement history of the fault during Late Cenozoic.Located at the southern side of the ATF, the Suo’erkulinan basin consists of more than 600\|meter\|thick Pliocene Shizigou Formation below and about 120\|meter\|thick Early to Middle Pleistocene Qigequan Formation above according to the 1∶200000 geological map by Xinjiang Province. An obvious erosional surface can be seen on the top of the lower sequence. Sediments in the Shizigou Formation are characterized by 400\|meter\|thick yellow to red cobble\|sized conglomerates in the bottom, up\|grading to sandstones and grey\|green mudstones. This indicated that the sedimentary facies changed from alluvial fan to fluvial fan and sediments became more and more mature. The upper sequence, the Qigequan Formation, corresponds to an alluvial facies series composed of yellow to white cobble\|sized conglomerates intercalated with lenticular sandstones. Paleo\|current indicators showed that the Shizhigou conglomeratic series were sourced from northwest. Well\|developed syn\|sedimentary faults, normal faults mostly inherited from syn\|sedimentary faults, and some striation lineations on the surface indicated transtensional tectonic environment of the strike\|slip faulting.

Active Fault Tolerant Control of a Class of Nonlinear Time-Delay Processes

Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor biaz, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a 'soft sensor', which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.