Robust machine fault detection with independent component analysis and support vector data description

At present, most researchers consider that the number of types of collision factors are limited when researching vehicle collision prediction. In addition, most studies are based on two-class or multi-class methods to study and predict vehicle collisions, but few researchers consider applying single-class classification methods to predict vehicle collisions. In this paper, the least squares method is used to select the 12 factors that affect the collision, and the single category classification method is used to predict the vehicle collision. SVDD (Support Vector Domain Description) was used as a model method for the experiments in this article, the factors (such as weather, traffic flow) that affect vehicle collision are used as the input of the model to train a minimum hypersphere. The validation sample set is validated, and the sample points falling within the sphere are the support vectors trained by the model, that is to say, if the (New) samples similar to the training sample set belong to the same class, those falling outside the sphere do not belong to the training sample set. In this paper, one class SVM is used as a comparative experiment. Finally, the experiment shows that SVDD obtains better prediction results, and the accuracy is better than one class SYTI model.

Dynamic is another critical behaviour of the process data, which has also caught much attention in process monitoring. Based on the previous works on dynamic process monitoring in the past years, this chapter introduces two different methods. These approaches rely on (i) recent work on independent component analysis and support vector data description that is applied to a dynamic data structure and (ii) the incorporation of the statistical local approach into a dynamic data representation. The analysis of experimental data from a gearbox system confirms (i) significant auto- and cross-correlation within and among these signals and (ii) that they cannot be assumed to follow Gaussian distributions. The application of both approaches showed that they are more sensitive to incipient faults than conventional multivariate statistical methods.

Fault detection in dynamic plant-wide process by multi-block slow feature analysis and support vector data description

Recently, principal component analysis (PCA) and independent component analysis (ICA) have attracted increasing attention for fault detection in industrial processes. However, industrial processes usually have multimodal characteristics, in which case, the performance of traditional PCA and ICA methods is poor. A fault detection method based on the local entropy double subspace (LEDS) is proposed that effectively supervises the multimodal process with multiple steady modes. The proposed method uses the local probability density (LPD) to eliminate the multimodal characteristics of the data. Furthermore, local entropy (LE) is applied to mine the statistical information associated with the process data. The process variables are classified into Gaussian and non-Gaussian subspaces by the Kolmogorov–Smirnov (KS) test. PCA and ICA models are built in Gaussian and non-Gaussian subspaces, respectively, for fault detection. A Bayesian decision method is employed to transform the detection results into fault probabilities, and the detection results are combined with the final statistical information. This method effectively eliminates the multicentre structure of the data and improves the fault detection performance of traditional PCA and ICA. The proposed method is applied to the Tennessee Eastman multimodal process. The simulation results show that the fault detection rate of LEDS is better than the rates of traditional methods when the false alarm rate is low for most faults.

It is of critical importance to keep a steady operation in the blast furnace to facilitate the production of high quality hot metal. In order to monitor the state of blast furnace, this article proposes a fault detection and identification method based on the multidimensional Gated Recurrent Unit (GRU) network, which is a kind of recurrent neural network and is highly effective in handling process dynamics. Comparing to conventional recurrent neural networks, GRU has a simpler structure and involves fewer parameters. In fault detection, a moving window approach is applied and a GRU model is constructed for each process variable to generate a series of residuals, which is further monitored using the support vector data description (SVDD) method. Once a fault is detected, fault identification is performed using the contribution analysis. Application to a real blast furnace fault shows that the proposed method is effective.

Support vector data description (SVDD) is an effective algorithm for nonlinear process monitoring. However, the conventional SVDD method can't deal with the incipient faults well, which has the small fault amplitude and is easy to be overlapped by industrial noises. Aiming at this problem, an improved SVDD, called probability related SVDD in local variable field (LVPSVDD), is proposed to detect the incipient faults in nonlinear processes. Firstly, the method divides process variables into several local variable fields by hierarchical clustering, and the SVDD model of each variable field is built. Then the sliding window technology is applied to the SVDD distance statistics, and the probability distribution change in the sliding window is measured by Kullback Leibler divergence (KLD). For each local variable field, the corresponding probability related monitoring statistic is developed to replace the original distance statistic. Finally, the global monitoring statistic is obtained by integrating the monitoring results of all local variable fields with Bayesian inference strategy. The method is illustrated with simulation on the continuous stirred tank reactor (CSTR).

Fault signal is always a problem that interferes with the operation of fiber board gluing control system. This paper proposes a Possibilistic C-Means (PCM) improved support vector data description (SVDD) method to construct a multi-classifier for the fault diagnosis of fiber gluing control system. In the proposed method, SVDD is employed to construct a multi-classifier for the fault diagnosis in the gluing system. Because the original SVDD is sensitive to the noises and outliers, we use PCM to improve the accuracy of classifier and then conduct a series of experiments. The results of the experiments show that the method proposed can identify the actual situation accurately and provide a high identification rate up to 97%, which is better than original SVDD.

Fault detection based on the vibration signal of an engine is an effective non-disassembly method for engine diagnosis because a vibration signal includes a lot of information about the condition of the engine. To obtain multi-information for this article, three vibration sensors were placed at different test points to collect vibration information about the engine operating process. A method combining support vector data description and Dempster–Shafer evidence theory was developed for engine fault detection, where support vector data description is used to recognize the data from a single sensor and Dempster–Shafer evidence theory is used to classify the information from the three vibration sensors in detail. The experimental results show that the fault detection accuracy using three sensors is higher than using a single sensor. The multi-complementary sensor information can be adopted in the proposed method, which will increase the reliability of fault detection and reduce uncertainty in the recognition of a fault.

In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scarce. The original contribution in this work is the industrial context of application and the comparison of swarm intelligence algorithms to optimize the SVDD hyper-parameters. Four recent metaheuristics are compared hereby to solve the corresponding optimization problem in an efficient manner. These optimization techniques are then implemented for fault detection in a multivariate industrial process with non-balanced data. The obtained numerical results seem to be promising when the considered optimization techniques are combined with SVDD. In particular, the Spotted Hyena algorithm outperforms other metaheuristics reaching values of F1 score near 100% in fault detection.

Detection and classification are two significant tasks for industrial fault diagnosis. However, conventional methods typically treat these tasks as separate and independent problems, and necessitate a retraining process when new fault samples or classes are collected. Therefore, an incremental support vector data description scheme using Gaussian kernel function is pro-posed for industrial process fault diagnosis in a unified frame-work. In this framework, the decision boundary is updated incrementally only based on the specific original support vectors and newly collected samples. An adaptive threshold and a restructured radius are proposed to promote accuracy in the fault detection. In the classification procedure, the hyperspheres for all known classes are constructed by decision tree. The new sample that does not belong to any known class is identified as an unknown class. Without a time-consuming retraining process, the proposed diagnosis method with the incremental learning capability can synchronously achieve the fault detection and classification task. Experimental results demonstrate the effectiveness and superiority in terms of diagnosis performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical industrial processes, new fault samples are collected and new fault classes emerge continually. Under such scenarios, fault diagnosis with incremental learning capability is becoming increasingly important. This work proposes a unified incremental framework for fault diagnosis based on support vector data description, which is able to achieve fault detection and classification synchronously. For fault detection, an adaptive threshold and a restructured radius are developed. For fault classification, hypersphere-shaped boundaries of all fault classes are given via the decision tree-based strategy. The proposed method is updated to include new fault samples and fault classes without dimensionality reduction or any distributional assumptions.

As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.

Not alike to principal component analysis (PCA) based monitoring statistics (T2 and SPE), the control limits for independent component analysis (ICA) monitoring statistics (I2 , Ie 2 and SPE) cannot be determined directly from a particular approximation distribution due to latent variables do not follow Gaussian distribution. Lee et al. (2004) proposed to use kernel density estimation (KDE) to obtain the control limits. However, the KDE method is very sensitive to the choice of smoothing parameter. Therefore, this study utilizes the support vector machine (SVM) for process fault detection by taking information of ICA extracted statistics as inputs of SVM. The proposed method (named, ICA-SVM) will be implemented in the Tennessee Eastman Process. In which, several multivariate monitoring schemes such as PCA, ICA, modified ICA and ICA- PCA will be also compared to demonstrate the efficiency of proposed method.

In process monitoring, a representative out‐of‐control class of samples cannot be generated. Here, it is assumed that it is possible to obtain a representative subset of samples from a single ‘in‐control class’ and one class classifiers namely Q and D statistics (respectively the residual distance to the disjoint PC model and the Mahalanobis distance to the centre of the QDA model in the projected PC space), as well as support vector domain description (SVDD) are applied to disjoint PC models of the normal operating conditions (NOC) region, to categorise whether the process is in‐control or out‐of‐control. To define the NOC region, the cumulative relative standard deviation (CRSD) and a test of multivariate normality are described and used as joint criteria. These calculations were based on the application of window principal components analysis (WPCA) which can be used to define a NOC region. The D and Q statistics and SVDD models were calculated for the NOC region and percentage predictive ability (%PA), percentage model stability (%MS) and percentage correctly classified (%CC) obtained to determine the quality of models from 100 training/test set splits. Q, D and SVDD control charts were obtained, and 90% confidence limits set up based on multivariate normality (D and Q) or SVDD D value (which does not require assumptions of normality). We introduce a method for finding an optimal radial basis function for the SVDD model and two new indices of percentage classification index (%CI) and percentage predictive index (%PI) for non‐NOC samples are also defined. The methods in this paper are exemplified by a continuous process studied over 105.11 h using online HPLC. Copyright © 2010 John Wiley &amp; Sons, Ltd.

In order to solve the problems of difficult and complex instruments fault detection in process industry, an instruments fault detection method based on independent component analysis (ICA) is proposed. The proposed method uses ICA arithmetic to establish the faults detection model with normal data recorded by instruments, and then calculate the statistic variables as on-line fault detecting strategy. An improved algorithm of contribution is proposed to locate the fault instrument. Simulation result with the data from Tennessee Eastman process verifies the effectiveness of proposed faults detection method and the improved contribution algorithm.

This paper exposes automatic classification for wooden board according to its knot defects. A four-parameter-classification vector, which includes the knot size and red component pixels of interior and exterior and boundary part of the knot, is formed to recognize the knot type. A possibilistic c-means (PCM) improved support vector data description (SVDD) method was proposed to construct a multi-classifier to classify four types of wood knots. The results obtained with our method show a real improvement of the recognition rate, which is 94%, compared to the original SVDD classifier, which recognition rate is just 86%, and experiments also testify PCM can help SVDD overcome the shortage of being sensitive to the noises and outliers.