Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection

A study is presented to compare the performance of bearing fault detection using two different classifiers, namely, artificial neural networks (ANNs) and support vector machines (SMVs). The time-domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to the classifiers for two-class (normal or fault) recognition. The classifier parameters, e.g., the number of nodes in the hidden layer in case of ANNs and the radial basis function kernel parameter (width) in case of SVMs along with the selection of input features are optimized using genetic algorithms. The classifiers are trained with a subset of the experimental data for known machine conditions and are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and signal preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.     

Use of particle swarm optimization for machinery fault detection

A study is presented on the application of particle swarm optimization (PSO) combined with other computational intelligence (CI) techniques for bearing fault detection in machines. The performance of two CI based classifiers, namely, artificial neural networks (ANNs) and support vector machines (SVMs) are compared. The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to the classifiers for detection of machine condition. The classifier parameters, e.g., the number of nodes in the hidden layer for ANNs and the kernel parameters for SVMs are selected along with input features using PSO algorithms. The classifiers are trained with a subset of the experimental data for known machine conditions and are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of the number of features, PSO parameters and CI classifiers on the detection success are investigated. Results are compared with other techniques such as genetic algorithm (GA) and principal component analysis (PCA). The PSO based approach gave a test classification success rate of 98.6–100% which were comparable with GA and much better than with PCA. The results show the effectiveness of the selected features and the classifiers in the detection of the machine condition.     

Rotating machine fault detection based on HOS and artificial neural networks

In order to identify the faults of rotating machinery, classification process can be divided into two stages: one is the signal preprocessing and the feature extraction; the other is the recognition process. In the preprocessing and feature extraction stage, the higher-order statistics (HOS) is used to extract features from the vibration signals. In the recognition process, two kinds of neural network classifier are used to evaluate the classification results. These two classifiers are self-organizing feature mapping (SOM) network for collecting data at the initial stage and learning vector quantization (LVQ) network at the identification stage. The experimental results obtained from HOS as preprocessor to extract the features of fault are clearer than those obtained from the power spectrum. In addition, the recognizable rate by using either SOM or LVQ as classifiers is 100%.     

Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)

Vibration signals extracted from rotating parts of machineries carries lot many information with in them about the condition of the operating machine. Further processing of these raw vibration signatures measured at a convenient location of the machine unravels the condition of the component or assembly under study. This paper deals with the effectiveness of wavelet-based features for fault diagnosis of a gear box using artificial neural network (ANN) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing ANN and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared.     

基于小波-神经网络的电机振动故障诊断

针对电机振动信号的频谱特点，提出基于小波-神经网络技术的电机故障模式识别与诊断的新方法。利用小波包的多维多分辨率特性，对电机振动信号进行分解与重构，获得振动信号的突变信息，提取与电机故障相关的特征信息，将其作为特征向量输入ART2神经网络，对其进行训练。经过训练后的神经网络可对电机工作状态进行在线监测和实时故障诊断，并在转子实验台上进行了模拟故障仿真试验。通过对仿真结果的分析，证实这种诊断方法的可行性。     

APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN CONTROLLED DRUG DELIVERY SYSTEMS

Estimation of release profiles of drugs normally requires time-consuming trial-and-error experiments. Feed-forward neural networks including multilayer perceptron (MLP), radial basis function network (RBFN), and generalized regression neural network (GRNN) are used to predict the release profile of betamethasone (BTM) and betamethasone acetate (BTMA) where in situ forming systems consist of poly (lactide-co-glycolide), N-methyl-1-2-pyrolidon, and ethyl heptanoat as a polymer, solvent, and additive, respectively. The input vectors of the artificial neural networks (ANNs) include drug concentration, gamma irradiation, additive substance, and type of drug. As the outputs of the ANNs, three features are extracted using the nonlinear principal component analysis technique. Leave-one-out cross-validation approach is used to train each ANN. We show that for estimation of BTM and BTMA release profiles, MLP outperforms GRNN and RBF networks in terms of reliability and efficiency.     

An expert system for fault diagnosis in internal combustion engines using probability neural network

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.     

Intelligent diagnosis method for a centrifugal pump using features of vibration signals

In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis method for a centrifugal pump system using features of vibration signals at an early stage. The diagnosis algorithm is derived using wavelet transform, rough sets and a partially linearized neural network (PNN). ReverseBior wavelet function is used to extract fault features from measured vibration signals and to capture hidden fault information across optimum frequency regions. As the input parameters for the neural network, the non-dimensional symptom parameters that can reflect the characteristics of a signal are defined in the amplitude domain. The diagnosis knowledge for the training of the PNN can be acquired by using the rough sets. We also propose a diagnosis method based on the PNN, one which can deal with the ambiguity problem of condition diagnosis, and distinguish fault types on the basis of the possibility distributions of symptom parameters automatically. The decision method of optimum frequency region for extracting feature signals is also discussed using real plant data. Practical examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method.     

Fourier and wavelet descriptors for shape recognition using neural networks—a comparative study

This paper presents the application of three different types of neural networks to the 2-D pattern recognition on the basis of its shape. They include the multilayer perceptron (MLP), Kohonen self-organizing network and hybrid structure composed of the self-organizing layer and the MLP subnetwork connected in cascade. The recognition is based on the features extracted from the Fourier and wavelet transformations of the data, describing the shape of the pattern. Application of different neural network structures associated with different preprocessing of the data results in different accuracy of recognition and classification. The numerical experiments performed for the recognition of simulated shapes of the airplanes have shown the superiority of the wavelet preprocessing associated with the self-organizing neural network structure. The integration of the individual classifiers based on the weighted summation of the signals from the neural networks has been proposed and checked in numerical experiments.     

Intelligent sensor system for discrimination of material type using neural networks

Neural network based classification of material type even with the variation in the sensor parameter is investigated in this paper. The sensor is developed by means of a lightweight plunger probe and an optical mouse sensor. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The experiment is conducted to obtain the bouncing signals for plain surface of an objects kept at different distances from the probe. During the bouncing of the probe, time varying signals are generated from optical mouse that are recorded in data files on PC. Some dominant unique features are then extracted using signal processing tools to optimize neural network based classifier. The time and features of bouncing signal are related to the material type, and each material has a unique set of such properties. It is found that the sensor system is intelligent due to its ability to classify the material type even with the variation in the sensor parameter (distance between the sensor probe and plain objects). The classifiers are developed using two neural networks configurations, namely a well-known Multi-layer Perceptron Neural Networks (MLP NN), and Radial Basis Function Neural Networks (RBF NN). MLP NN and RBF NN models are designed to maximize accuracy under the constraints of minimum network dimension.The optimal parameters of MLP NN and RBF NN models based on various performance measures that include percentage classification accuracy (PCLA) on the testing data, and area under Receiver Operating Characteristics (ROC), and are determined. For the sensor data set, the PCLA of both the classifiers are found reasonable consistently in respect of rigorous testing using different data partitions. The areas under the ROC curves are close to unity. Performances of the two classifiers have been compared. It has been found that the RBF NN is more robust to noise, and epochs required for training are very less as compared to that for MLP NN.     

Application of an intelligent classification method to mechanical fault diagnosis

A new method for intelligent fault diagnosis of rotating machinery based on wavelet packet transform (WPT), empirical mode decomposition (EMD), dimensionless parameters, a distance evaluation technique and radial basis function (RBF) network is proposed in this paper. In this method, WPT and EMD are, respectively, used to preprocess vibration signals to mine fault characteristic information more accurately. Then, dimensionless parameters in time domain are extracted from each of the original vibration signals and preprocessed signals to form a combined feature set. Moreover, the distance evaluation technique is utilised to calculate evaluation factors of the combined feature set. Finally, according to the evaluation factors, the corresponding sensitive features are selected and input into the RBF network to automatically identify different machine operation conditions. An experiment of rolling element bearings is carried out to test the performance of the proposed method. The experimental result demonstrates that the method combining WPT, EMD, the distance evaluation technique and the RBF network may accurately extract fault information and select sensitive features, and therefore it may correctly diagnose the different fault categories occurring in the bearings. Furthermore, this method is applied to slight rub fault diagnosis of a heavy oil catalytic cracking unit, the actual result shows the method may be applied to fault diagnosis of rotating machinery effectively.     

Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network

This paper presents an intelligent diagnosis method for a rolling element bearing; the method is constructed on the basis of possibility theory and a fuzzy neural network with frequency-domain features of vibration signals. A sequential diagnosis technique is also proposed through which the fuzzy neural network realized by the partially-linearized neural network (PNN) can sequentially identify fault types. Possibility theory and the Mycin certainty factor are used to process the ambiguous relationship between symptoms and fault types. Non-dimensional symptom parameters are also defined in the frequency domain, which can reflect the characteristics of vibration signals. The PNN can sequentially and automatically distinguish fault types for a rolling bearing with high accuracy, on the basis of the possibilities of the symptom parameters. Practical examples of diagnosis for a bearing used in a centrifugal blower are given to show that bearing faults can be precisely identified by the proposed method.     

Fuzzy learning vector quantization for hyperspectral coastal vegetation classification

Artificial neural networks (ANNs) may be of significant value in extracting vegetation type information in complex vegetation mapping problems, particularly in coastal wetland environments. Unsupervised, self-organizing ANNs have not been employed as frequently as supervised ANNs for vegetation mapping tasks, and further remote sensing research involving fuzzy ANNs is also needed. In this research, the utility of a fuzzy unsupervised ANN, specifically a fuzzy learning vector quantization (FLVQ) ANN, was investigated in the context of hyperspectral AVIRIS image classification. One key feature of the neural approach is that unlike conventional hyperspectral data processing methods, endmembers for a given scene, which can be difficult to determine with confidence, are not required for neural analysis. The classification accuracy of FLVQ was comparable to a conventional supervised multi-layer perceptron, trained with backpropagation (MLP) (KHAT (Kˆ) accuracy: 82.82% and 84.66%, respectively; normalized accuracy: 74.60% and 75.85%, respectively), with no significant difference at the 95% confidence level. All neural algorithms in the experiment yielded significantly higher classification accuracies than the conventional endmember-based hyperspectral mapping method assessed (i.e., matched filtering, where Kˆ accuracy = 61.00% and normalized accuracy = 57.96%). FLVQ was also dramatically more computationally efficient than the baseline supervised and unsupervised ANN algorithms tested, including the MLP and the Kohonen self-organizing map (SOM), respectively. The 400-neuron FLVQ network required only 3.6% of the computation time used by the MLP network, and only 5.9% of the MLP time was used by the 588-neuron FLVQ network. In addition, the 400-neuron FLVQ used only 16.7% of the time used by the 400-neuron SOM for model development.     

轴承故障融合诊断方法

针对传统人工神经网络在故障诊断中应用的局限性 ,提出一种基于小波变换、遗传算法与神经网络的融合故障诊断方法。该方法先用小波变换对原始采样信号进行特征提取 ,再用遗传算法优化选择最为重要的特征作为神经网络的输入参数。最后 ,由神经网络进行状态识别和特征分类。这样不仅减少网络训练时间 ,降低网络计算量 ,而且有效提高分类的准确性及故障诊断的可靠性。轴承故障诊断实验结果表明 ,该方法是有效的。     

Probabilistic neural network for breast cancer classification

Among cancers, breast cancer causes second most number of deaths in women. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis systems have been proposed in the last years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short-term follow-up examination instead. In clinical diagnosis, the use of artificial intelligent techniques as neural networks has shown great potential in this field. In this paper, three classification algorithms, multi-layer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN), are applied for the purpose of detection and classification of breast cancer. Decision making is performed in two stages: training the classifiers with features from Wisconsin Breast Cancer database and then testing. The performance of the proposed structure is evaluated in terms of sensitivity, specificity, accuracy and ROC. The results revealed that PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively. MLP was ranked as the second classifier and was capable of achieving 97.80 and 96.34 % classification accuracy for training and validation phases, respectively, using scaled conjugate gradient learning algorithm. However, RBF performed better than MLP in the training phase, and it has achieved the lowest accuracy in the validation phase.     

Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image

This article presents a sufficient comparison of two types of advanced non-parametric classifiers implemented in remote sensing for land cover classification. A SPOT-5 HRG image of Yanqing County, Beijing, China, was used, in which agriculture and forest dominate land use. Artificial neural networks (ANNs), including the adaptive backpropagation (ABP) algorithm, Levenberg–Marquardt (LM) algorithm, Quasi-Newton (QN) algorithm and radial basis function (RBF) were carefully tested. The LM–ANN and RBF–ANN, which outperform the other two, were selected to make a detailed comparison with support vector machines (SVMs). The experiments show that those well-trained ANNs and SVMs have no significant difference in classification accuracy, but the SVM usually performs slightly better. Analysis of the effect of the training set size highlights that the SVM classifier has great tolerance on a small training set and avoids the problem of insufficient training of ANN classifiers. The testing also illustrates that the ANNs and SVMs can vary greatly with regard to training time. The LM–ANN can converge very quickly but not in a stable manner. By contrast, the training of RBF–ANN and SVM classifiers is fast and can be repeatable.     

Bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics

Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, directory path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system.     

A review of genetic algorithms applied to training radial basis function networks

The problems associated with training feedforward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.     

A non-symbolic implementation of abdominal pain estimation in childhood

The abdominal pain is a very common disease in childhood, which lurks complications. Pediatric surgeons have to estimate at least 15 clinical and laboratory factors in order to make a diagnosis and decide about performing a surgical operation of the abdomen. Artificial Neural Networks (ANNs) are particular implementations of Artificial Intelligence (AI) systems and they are used in a wide area of application fields. This study examines the implementation of ANN architectures, using Multi-Layer Perceptron (MLP) neural networks and Probabilistic Neural Networks (PNN) architectures, in order to specify the appropriate ANN structure for abdominal pain estimation in childhood. The architecture with the best performance is a fully interconnected MLP neural network with an input layer of 15 nodes, one hidden layer of 5 neurons and an output layer, with error back-propagation algorithm being used as the learning scheme. In the output layer, the estimation of appendicitis’ stage is reached automatically. The proposed ANN achieved a percentage of 88.5% of correct classification on testing set cases. Further analysis of obtained results, exhibited the ability of ANN for distinguishing the necessity of a case for operative treatment of abdominal pain based on diagnostic features, attaining a percentage of 100% of successful prognosis over the cases of testing set. The aim of proposed MLP neural network is to assist surgeons in appendicitis prediction, avoiding an unnecessary operative treatment.     

基于Boosting RBF神经网络的滚动轴承故障诊断

提出了一种新颖的基于RBF神经网络滚动轴承故障诊断方法。以滚动轴承动态信号的能量信息作为特征，RBF神经网络作为分类器进行滚动轴承故障自动分类与诊断。为了进一步提高神经网络的泛化能力和故障诊断的准确性，采用Boosting方法，进行网络集成。对七类滚动轴承进行了实验，结果表明该方法具有很好的故障诊断效果。