Fuzzy decision fusion system for fault classification with analytic hierarchy process approach

Switching LDS-based approach for process fault detection and classification

The problem of fault classification in industry has been studied extensively. Most classification algorithms are modeled on the premise of data balance. However, the difficulty of collecting industrial data in different modes is quite different. This inevitably leads to data imbalance, which will adversely affect the fault classification performance. This article proposes a novel data augmentation classifier (DAC) for imbalanced fault classification. Data augmentation based on generative adversarial networks (GANs) is an effective way to solve the problem of unbalanced classification. However, the randomness of the GAN generation process restricts the effect of data enhancement. DAC proposes a data selection strategy based on data filtering and data purification in model training to solve this problem. In addition, DAC combines supervised learning and data generation processes to obtain an end-to-end model. Meanwhile, multigenerator structure of DAC (MDAC) is proposed to solve the problem of incomplete learning of a single generator when data imbalances get complicated. The proposed DAC and MDAC are applied in two fault classification cases of the Tennessee Eastman (TE) benchmark process, results of which show superiority of DAC and MDAC compared to existing methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Data imbalances are common in fault classification and affect the effectiveness of modeling in industry. As a generative model, generative adversarial networks (GANs) provide new ideas for small-class data augmentation. However, the instability of its training process and the randomness of data generation affect the results of data augmentation. In this article, the GAN generation process is analyzed in detail. The results of the visualization indicate that no data generation was perfect at any one time. Based on the rules of GAN data generation, we propose a data selection strategy during training. High-quality data are selected for data augmentation through data filtering and data purification. Apart from this, we combine the training process of GAN and classification model for imbalanced data to reduce modeling time. Through industrial examples, we have evaluated the effectiveness of this method.

This study aims to improve the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy system. Three separate fuzzy inference system are designed for complete protection scheme for transmission line. The proposed technique is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location. The proposed method needs current and voltage measurements available at the relay location and can perform the fault detection and classification in about a half‐cycle time. The proposed fuzzy logic based relay has less computation complexity and is better than other AI based methods such as artificial neural network, support vector machine, and decision tree (DT) etc. which require training. The percentage error in fault location is within 1 km for most of the cases. Fault location scheme has been validated using χ2 test with 5% level of significance. Proposed scheme is a setting free method and is suitable for wide range of parameters, fault detection time is less than half cycle and relay does not show any reaching mal‐operation so it is reliable, accurate and secure.

Fuzzy systems based on fuzzy if-then rules have been applied to various problems. The main application area has been fuzzy control problems. In many cases, such fuzzy systems can handle only a few input variables. This is because the number of fuzzy if-then rules exponentially increases as the number of input variables increases. In this paper, we try to design fuzzy classification systems based on fuzzy if-then rules for multidimensional pattern classification problems with many attributes. For designing such fuzzy classification systems, we compare two frameworks in the area of genetics-based machine learning: the Michigan approach and the Pittsburgh approach. The performance of fuzzy rule-based classification systems is also compared with that of various pattern classification methods. In computer simulations, we use a wine classification problem with 13 attributes, a cancer diagnosis problem with 9 attributes, and a credit approval problem with 14 attributes. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(12): 10–19, 1997

Semi-supervised convolutional generative adversarial networks for dynamic fault classification with manifold regularization

In this paper, we address the problem of fault classification in PhotoVoltaic (PV) arrays using a semi-supervised graph signal processing approach. Traditional fault detection and classification methods require large amounts of labeled data for training. In utility scale solar arrays, obtaining labeled data for different fault classes is resource intensive. We propose a graph based classification technique that relies on a limited amount of labeled data. We compare our results with the well known supervised machine learning classifiers such as the K-nearest neighbour classifier, random forest classifier, support vector machines, and artificial neural networks. We also show that the graph-based classifiers require lower training computational cost compared to the standard supervised machine learning algorithms. The proposed method also achieves good classification performance with unseen data. We validate our method on a real-time dataset and show significant improvements over existing approaches.

The convention sector is one of the fastest growing and most lucrative areas of the tourism industry. Solving for the most salient determinants in selecting or organizing a destination for conventions and seeing out their success is an important research topic. This study proposes the analytical hierarchy process (AHP) approach, a decision-making method based on pairwise comparisons between criteria, to construct an evaluation structure with criteria and associated weights of convention site selection for meeting planners. A case study with a three-level evaluation structure is constructed and tested. The findings indicate that the AHP approach is a useful tool to help support a decision in convention site selection.

Addressing the challenges of complex signal characteristics and difficult fault type differentiation in ultrasonic detection, a fault classification method based on Particle Swarm Optimization Support Vector Machine (PSO-SVM) is proposed. This method utilizes the particle swarm optimization algorithm to globally optimize key parameters in the support vector machine model, overcoming the limitations of traditional support vector machines, such as relying on experience for parameter selection and unstable classification performance. Taking experimental data collected from ultrasonic detection as the research object, the original signals are first subjected to feature extraction and preprocessing. Subsequently, PSO-SVM and traditional SVM models are constructed for fault pattern recognition and classification experiments, respectively. By comparing and analyzing the performance of the two methods in terms of classification accuracy and other performance indicators, the experimental results demonstrate that the PSO-optimized SVM model outperforms the standalone SVM method in both fault classification accuracy and overall recognition effectiveness. This verifies the effectiveness and feasibility of the proposed method in ultrasonic detection fault classification.

Data-driven approaches are widely applied for fault detection in industrial process. Recently, a new method for fault detection called principal component pursuit(PCP) is introduced. PCP is not only robust to outliers, but also can accomplish the objectives of model building, fault detection, fault isolation and process reconstruction simultaneously. PCP divides the data matrix into two parts: a fault-free low rank matrix and a sparse matrix with sensor noise and process fault. The statistics presented in this paper fully utilize the information in data matrix. Since the low rank matrix in PCP is similar to principal components matrix in PCA, a T2 statistic is proposed for fault detection in low rank matrix. And this statistic can illustrate that PCP is more sensitive to small variations in variables than PCA. In addition, in sparse matrix, a new monitored statistic performing the online fault detection with PCP-based method is introduced. This statistic uses the mean and the correlation coefficient of variables. Monte Carlo simulation and Tennessee Eastman (TE) benchmark process are provided to illustrate the effectiveness of monitored statistics.

In the majority of the multivariable processes, analysis of process monitoring and fault diagnosis is usually based on the fundamental assumption that the monitored variables follow a Gaussian distribution. However, it is well known that many of the variables are mutually dependent in process systems. This paper proposes a new monitoring method based on independent component analysis (ICA) - sparse autoencoder. The independent information component can be extracted by ICA through higher-order statistics. Moreover, the inherent nonlinear characteristics in the residual model of ICA can be handled by a deep architecture constructed with sparse autoencoder. To overcome the problem of local minima in the optimization of sparse autoencoder, a restricted Boltzmann machine (RBM) is used to pre-train the net, and the parameters in the sparse autoencoder is updated by Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). Monitoring statistic is developed and its confidence limit is computed by kernel density estimation. A case study of the Tennessee Eastman (TE) benchmark process indicates that the proposed fault detection method is more efficient.

While the Israeli–Palestinian conflict has raged for decades, in all of its ramifications there has never been a totally structured or scientific approach to the conflict with all of its details. The Analytic Hierarchy Process (AHP) approaches the problem along these lines. There are a plethora of reasons why the traditional face to face negotiations have broken down over the years. This paper identifies a significant number of those impediments and indicates how the AHP can productively address them. A summary of the highlights of the AHP approach precedes how it has been applied to the Israeli–Palestinian conflict. To date, the participants, significant members of both communities, have derived and agreed upon a solution that includes all the major issues, except for the refugee problem. That problem is currently being worked on, but will take an extended period because of the unique factors involved. What has been provided is an agreed upon solution to virtually all of the issues impeding past negotiations, including borders, settlements, the status of Jerusalem, the Holy Places, security and expectations of each side.

Multimode processes may be operating under different statuses, revealing different process characteristics and variations. It can be an interesting issue to check how the process variations change from one mode to another and thus how they can be used for multimode analysis and monitoring. In the present work, a between-mode relative analysis algorithm is proposed to explore the between-mode relationship. Comprehensive subspace decomposition is performed in each mode regarding their relative changes from the reference mode and influences on process monitoring. Each mode is separated into three different systematic subspaces and one residual subspace based on the between-mode changes of process variations, including the increased part, the decreased part, and the unchanged part. These different variations are then modeled respectively and different monitoring statistics are defined for online monitoring to identify mode affiliation and detect the fault status. By separating and monitoring different types of between-mode relative variations, the proposed method can not only efficiently distinguish different modes and detect faults but also offer enhanced process understanding. Theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with multimode data generated from the Tennessee Eastman (TE) benchmark process.

Sensor faults are non-negligible issues for soft sensor modeling. However, existing deep learning-based soft sensors are fragile and sensitive when considering sensor faults. To improve the robustness against sensor faults, this article proposes a deep subdomain learning adaptation network (DSLAN) to develop a sensor fault-tolerant soft sensor, which is capable of handling both sensor degradation and sensor failure simultaneously. Primarily, domain adaptation works for process data with sensor degradation in industrial processes. Being founded on the basic structure of deep domain adaptation, a novel subdomain learner is added to automatically learn the subdomain division, enabling DSLAN adaptable to multimode industrial processes. Notably, the subdomain structure of each sample follows a categorical distribution parameterized by output of the subdomain learner. Based on the designed subdomain learner, a new probabilistic local maximum mean discrepancy (PLMMD) is presented to measure the difference in distribution between source and target features. In addition, a generator for failure data imputation is integrated in the framework, making DSLAN handle sensor failure simultaneously. Finally, the Tennessee Eastman (TE) benchmark process and two real industrial processes are used to verify the effectiveness of the proposed method. With the fault tolerance ability, soft sensing technology will take a step toward practical applications.

Monitoring and management of process transitions is a critical activity in chemical plants due to increased potential for abnormal operations. This activity is often hampered by the lack of a proper approach to label the transition states. In this paper, we present a systematic framework that constructs process transition states thus facilitating their monitoring for faulty operations. To address the nonstationary and non-Gaussian characteristics of the time series data collected during the transition process, an ensemble clustering method based on dynamic k-principal component analysis-independent component analysis (k-ICA-PCA) models is proposed to enable labeling of transitions. Next, we combine a PCA-based dimension reduction with a pattern classification strategy based on multiclass support vector data description (SVDD) to achieve transition process monitoring. The Tennessee Eastman (TE) benchmark process is used as a case study to evaluate the performance of the proposed framework.

Nonlinear process monitoring based on linear subspace and Bayesian inference