Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount

Quantitative analysis of spectral data based on stochastic configuration networks

In quantitative analysis of spectral data, traditional linear models have fewer parameters and faster computation speed. However, when encountering nonlinear problems, their predictive accuracy tends to be lower. Nonlinear models provide higher computational accuracy in such situations but may suffer from drawbacks such as slow convergence speed and susceptibility to get stuck in local optima. Taking into account the advantages of these two algorithms, this paper introduces the single-hidden layer feedforward neural network named stochastic configuration networks (SCNs) into chemometrics analysis. Firstly, the model termination parameters, that is, the error tolerance and the allowed maximum number of hidden nodes are analyzed. Secondly, times of random configuration are discussed and analyzed, and then the appropriate number is determined by considering the efficiency and stability comprehensively. Finally, predictions made by the SCN are tested on two public datasets. The performance of the SCN is then compared with that of other techniques, including principal component regression (PCR), partial least squares (PLS), back propagation neural network (BPNN), and extreme learning machine (ELM). Experimental results show that the SCN has good stability, high prediction accuracy and efficiency, making it suitable for quantitative analysis of spectral data.

Fault Detection of Urban Wastewater Treatment Process Based on Combination of Deep Information and Transformer Network

As one of the hot issues of concerns during modern social development, the wastewater treatment process is acknowledged to be a process with complex biochemical reactions and susceptible to an external environment, featuring strong nonlinear and time correlation characteristics, which are difficult for traditional mechanism-based models to tackle. For many classical data-driven fault detection methods, a complete retraining process is necessary to monitor every new fault, and most of the current neural network-based strategies rarely achieve satisfactory monitoring accuracy or robustness either. Giving full consideration to the aforementioned problems, this article takes advantage of position encoding, residual connection, and multihead attention mechanism embedded in the Transformer structure to establish an effective and efficient wastewater treatment process fault detection model, where offline modeling and online monitoring are performed successively to achieve accurate detection of the faults. In the experimental part, the advantages of the proposed method are strongly verified through the simulation monitoring of 27 faults on the benchmark simulation model 1 (BSM1), where the false alarm rate (FAR) and miss alarm rate (MAR) of the established method are proved to be significantly lower than those of the compared state-of-the-art methods.

HSELL-Net: A Heterogeneous Sample Enhancement Network With Lifelong Learning Under Industrial Small Samples

Small sample size leads to low accuracy and poor generalization of industrial fault diagnosis modeling. Domain adaptation (DA) attempts to enhance small samples by transferring samples in other similar domains, but it has limited application in industrial fault diagnosis, since the differences in working conditions lead to large variations of fault samples. To address the above issues, this article proposes a heterogeneous sample enhancement network with lifelong learning (HSELL-Net). First, a heterogeneous DA subnet (HDA-subnet) is presented, in which the designed heterogeneous supporting domain ensures dimension alignment and the designed distribution jointly matching improves the performance of distribution matching; thus, fault samples from other working conditions can be employed to reliably enhance small samples. Second, a lifelong learning subnet (LL-subnet) is designed, in which the proposed Admixup and shared knowledge repository enable incremental samples to further enhance small samples without retraining the network. The two subnets are mutually embedded and reinforced to enhance the number and types of small samples; thus, the accuracy and generalization of fault diagnosis under industrial small samples are improved. Finally, benchmark simulated experiments and real-world application experiments are conducted to evaluate the proposed method. Experimental results show the HSELL-Net outperforms the existing works under industrial small samples.

Wavelet Packet Decomposition-Based Multiscale CNN for Fault Diagnosis of Wind Turbine Gearbox

This article presents an intelligent fault diagnosis method for wind turbine (WT) gearbox by using wavelet packet decomposition (WPD) and deep learning. Specifically, the vibration signals from the gearbox are decomposed using WPD and the decomposed signal components are fed into a hierarchical convolutional neural network (CNN) to extract multiscale features adaptively and classify faults effectively. The presented method combines the multiscale characteristic of WPD with the strong classification capacity of CNNs, and it does not need complex manual feature extraction steps as usually adopted in existing results. The presented CNN with multiple characteristic scales based on WPD (WPD-MSCNN) has three advantages: 1) the added WPD layer can legitimately process the nonstationary vibration data to obtain components at multiple characteristic scales adaptively, it takes full advantage of WPD and, thus, enables the CNN to extract multiscale features; 2) the WPD layer directly sends multiscale components to the hierarchical CNN to extract rich fault information effectively, and it avoids the loss of useful information due to hand-crafted feature extraction; and 3) even if the scale changes, the lengths of components remain the same, which shows that the proposed method is robust to scale uncertainties in the vibration signals. Experiments with vibration data from a production wind farm provided by a company using condition monitoring system (CMS) show that the presented WPD-MSCNN method is superior to traditional CNN and multiscale CNN (MSCNN) for fault diagnosis.

Multisource-Refined Transfer Network for Industrial Fault Diagnosis Under Domain and Category Inconsistencies

Unsupervised cross-domain fault diagnosis has been actively researched in recent years. It learns transferable features that reduce distribution inconsistency between source and target domains without target supervision. Most of the existing cross-domain fault diagnosis approaches are developed based on the consistency assumption of the source and target fault category sets. This assumption, however, is generally challenged in practice, as different working conditions can have different fault category sets. To solve the fault diagnosis problem under both domain and category inconsistencies, a multisource-refined transfer network is proposed in this article. First, a multisource-domain-refined adversarial adaptation strategy is designed to reduce the refined categorywise distribution inconsistency within each source-target domain pair. It avoids the negative transfer trap caused by conventional global-domainwise-forced alignments. Then, a multiple classifier complementation module is developed by complementing and transferring the source classifiers to the target domain to leverage different diagnostic knowledge existing in various sources. Different classifiers are complemented by the similarity scores produced by the adaptation module, and the complemented smooth predictions are used to guide the refined adaptation. Thus, the refined adversarial adaptation and the classifier complementation can benefit from each other in the training stage, yielding target-faults-discriminative and domain-refined-indistinguishable feature representations. Extensive experiments on two cases demonstrate the superiority of the proposed method when domain and category inconsistencies coexist.

Intelligent Fault Diagnosis for Chemical Processes Using Deep Learning Multimodel Fusion

Deep learning technology has been widely used in fault diagnosis for chemical processes. However, most deep learning technologies currently adopted only use a single network stack or a certain network stack with multilayer perceptron (MLP) behind it. Compared with traditional fault diagnosis technologies, this method has made progress in both the diagnosis accuracy and speed, but due to the limited performance of a single network, the accuracy or speed cannot meet the requirements to the greatest extent. In order to overcome such problems, this article proposes a fault diagnosis method using deep learning multimodel fusion. Different from previous deep learning diagnosis methods, this method uses long short-term memory (LSTM) and convolutional neural network (CNN) to extract features separately. The extracted features are then fused and MLP is taken as the input for further feature compression and extraction, and finally the diagnosis results will be obtained. LSTM has long-term memory capabilities, the extracted features have temporal characteristics, and CNNs have a good effect on the extraction of spatial features. The proposed method integrates these two aspects for diagnosis such that the features finally extracted by the network have both spatial and temporal characteristics, thereby improving the network's diagnostic performance. Finally, a TE chemical process and an industrial coking furnace process are taken for simulation testing. It is proved that the performance of this method is superior to existing deep learning fault diagnosis methods with simple sequential stacking for unilateral feature extraction.

An Advanced Broyden–Fletcher–Goldfarb–Shanno Algorithm for Prediction and Output-Related Fault Monitoring in Case of Outliers

In the process industry, fault prediction and product-related fault monitoring are important links to ensure product quality and improve economic benefits. In this paper, under the framework of the BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm, a new and more accurate data-driven method, the ABFGS algorithm, is proposed. Compared with the BFGS algorithm, the ABFGS algorithm adds output-related fault monitoring capabilities and has strong robustness, which can eliminate the influence of outliers on measurement data. The effectiveness of this method has been verified by the Eastman benchmark program in Tennessee. The simulation results show that this method can eliminate the influence of outliers and effectively predict the process. Compared with the other three algorithms, the ABFGS algorithm can not only clearly and accurately indicate whether the detected fault is related to the output but also provide a higher fault monitoring rate.

Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring

Concurrent process-quality monitoring helps discover quality-relevant process anomalies and quality-irrelevant process anomalies. It especially works well in chemical plants with faults that cause quality problems. Traditional monitoring strategies are limitedly applied in chemical plants because quality targets in training data are insufficient. It is hard for inflexible models to fully capture the strongly nonlinear process-quality correlations. Also, deterministic models are mapped from process variables to qualities without any consideration of uncertainties. Simultaneously, a slow sampling rate for quality variables is ubiquitous in chemical plants since a product quality test is often time-consuming and expensive. Motivated by these limitations, this paper proposes a new concurrent process-quality monitoring scheme based on a probabilistic generative deep learning model developed from variational autoencoder. The supervised model is firstly developed and then the semi-supervised version is extended to solve the issue of missing targets. Especially, the semi-supervised learning algorithm is accomplished with an optimal parameter estimation in the light of maximum likelihood principle and no any hyperparameters are introduced. Two case studies validate that the proposed method effectively outperforms the other comparative methods in concurrent process-quality monitoring.

The Model and Training Algorithm of Compact Drone Autonomous Visual Navigation System

Trainable visual navigation systems based on deep learning demonstrate potential for robustness of onboard camera parameters and challenging environment. However, a deep model requires substantial computational resources and large labelled training sets for successful training. Implementation of the autonomous navigation and training-based fast adaptation to the new environment for a compact drone is a complicated task. The article describes an original model and training algorithms adapted to the limited volume of labelled training set and constrained computational resource. This model consists of a convolutional neural network for visual feature extraction, extreme-learning machine for estimating the position displacement and boosted information-extreme classifier for obstacle prediction. To perform unsupervised training of the convolution filters with a growing sparse-coding neural gas algorithm, supervised learning algorithms to construct the decision rules with simulated annealing search algorithm used for finetuning are proposed. The use of complex criterion for parameter optimization of the feature extractor model is considered. The resulting approach performs better trajectory reconstruction than the well-known ORB-SLAM. In particular, for sequence 7 from the KITTI dataset, the translation error is reduced by nearly 65.6% under the frame rate 10 frame per second. Besides, testing on the independent TUM sequence shot outdoors produces a translation error not exceeding 6% and a rotation error not exceeding 3.68 degrees per 100 m. Testing was carried out on the Raspberry Pi 3+ single-board computer.