Identification Of Abnormal Conditions Research Articles

Sustainable manufacturing process thermal energy environment detection and green economy development based on image classification

With a growing global focus on sustainability, manufacturing needs to ensure productivity while reducing resource consumption and environmental impact. This paper discusses the thermal energy environment detection method based on image classification in order to improve the energy efficiency monitoring in the manufacturing process and promote the development of green economy. In this study, deep learning technology was used to train convolutional neural network (CNN) to classify thermal environment data, collect thermal data images from multiple manufacturing processes, and label them. The pretreatment technology is used to improve the data quality, and the classification effect is optimized through multi-level network structure. The training and verification process of the model ensures its reliability in practical application scenarios. The experimental results show that the proposed image classification method achieves high accuracy in thermal environment detection, which is obviously superior to the traditional method. Through real-time monitoring of thermal energy status, timely identification of abnormal conditions, so as to provide effective data support for the manufacturing process, and promote the realization of energy conservation and emission reduction goals. Therefore, the thermal environment detection based on image classification provides a new technical path for the sustainable development of the manufacturing industry, and helps enterprises realize the transformation and upgrading of the green economy.

How to mine the abnormal information of power transformers: An efficient tool for quantifying the fault characteristics via multi-vibration signals

The new power system puts forward higher requirements for the reliability of power equipment. Mining the intrinsic information of equipment operation from massive data is the key challenge to determining fault early warning. The transformer fault diagnosis through vibration signal analysis has gained increasing prominence. However, the vibration signals produced by power transformers exhibit complex nonlinear and multivariate fluctuations due to the interaction of electromagnetic and mechanical factors. Unfortunately, the traditional fault diagnosis models lack the capability to capture extensive global fault information, resulting in inadequate performance in fault identification. This paper proposes the enhanced hierarchical multivariate multiscale fuzzy entropy (EHMvMFE) as an effective method for quantifying defect information in multivariate transformer signals to overcome the challenge. Firstly, the enhanced hierarchical decomposition method and time series coarse-graining theory are introduced into multivariate fuzzy entropy to develop EHMvMFE, which can comprehensively reflect the feature information of multivariate signals from the full range of frequencies. Then, EHMvMFE is utilized to extract the features via multivariate vibration signals of the transformer. Finally, the extreme learning machine is conducted to achieve effective identification of abnormal conditions. The proposed method is applied to two categories of transformer fault cases, and the diagnostic indicators are at least higher than 99.70 % and 91.82 %, which indicates a strong advantage in comparison with other popular models. The approach could be flexibly extended to the fault diagnosis of other power equipment, which is a potentially effective tool for improving the reliability of power systems.

Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information

Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information

Prediction of abnormal conditions for fused magnesium furnace based on improved LSTM model and rule-based reasoning

There are some abnormal conditions during the fused magnesia smelting process (FMSP), making it crucial for the operators to predict their occurrence to ensure proper guidance of the operation. In this paper, a prediction method is proposed for current and sound signals in the FMSP based on the Temporal convolutional network-Bidirectional-Long short-term memory-Attention (TCN-Bi-LSTM-Attention) model. Through analysis of abnormal conditions in the FMSP, according to the characteristics of abnormal conditions and the change of current change rate before and after prediction, an abnormal condition identification scheme based on rule-based reasoning (RBR) that utilizes current and sound signals is constructed. The results of the experiment showed that when considering the same prediction steps, the proposed model had higher prediction accuracy compared to the TCN-Attention model and the Bi-LSTM-Attention model. The proposed abnormal condition identification scheme can predict the type of abnormal condition that will occur in the next 60 s, with recall, precision, and F1 scores of over 90% for four different abnormal conditions.

Identification of abnormal conditions for gold flotation process based on multivariate information fusion and double‐channel convolutional neural network

AbstractAccurate and in‐time working condition identification plays a great role in industrial processes. However, most of the current flotation process identification models only use the characteristics of flotation froth as an identification basis, which often causes identification errors due to the instability of the froth, and a large amount of process data is not fully utilized. In this paper, an abnormal condition identification method based on multivariate information fusion and double‐channel convolutional neural network (double‐channel CNN) is proposed to achieve higher accuracy. First, a double‐channel CNN is used to extract depth features from different distributions of froth images and process data in parallel. Then, double normalized attention mechanism (double normalized AM) and multivariate information fusion methods are used to attach weights to the features and fuse them so as to ensure a higher response of key features and increase the reliability of the identification model. The method shows better performance than existing methods in offline simulations and has been validated online at a mineral processing plant in Shandong.

Identification of Abnormal Conditions for Fused Magnesium Melting Process Based on Deep Learning and Multisource Information Fusion

Fused magnesium furnace (FMF) is the vital equipment for magnesia refractory production. The melting process of FMF is subject to melting temperature, magnesite quality and composition, and much more dynamic factors, which are prone to abnormal conditions such as overheating, abnormal exhausting, semi-molten, and affect the stability and safety of the production seriously. Due to the difficulties of effectively monitoring and accurately identifying the abnormal conditions with the existing methods, this article proposes a novel method for identifying the abnormal conditions of FMF based on deep learning and multi-information fusion. First, the Wasserstein distance based generative adversarial networks with gradient penalty is developed to solve the problem of obtaining overheating condition image samples with obvious visual characteristics. Then, the image-based deep convolutional neural network model is established to recognize the abnormal condition images and extract image features whose training set is the combination of the generated images and the original images. Finally, based on multisource information fusion, the traditional process current data are fused with the extracted image features, and a support vector machine model is established to complete the classification and identification of the FMF working conditions. Various experiments on actual industrial data show that the proposed method is effective and advanced.

Identification of Unknown Abnormal Conditions in Catalytic Cracking Process Based on Two-Step Clustering Analysis and Signed Directed Graph

There are many unknown abnormal working conditions in industrial production. It is difficult to identify unknown abnormal working conditions because there are few relative sample and experience in this field. To solve this problem, a new identification method combining two-step clustering analysis and signed directed graph (TSCA-SDG) is proposed. Firstly, through correlation analysis and R-type clustering analysis, the variables are effectively selected and extracted. Then, a two-step clustering analysis was carried out on the selected variables to obtain the cluster results. Through the establishment of the signed directed graph (SDG) model, the causes of abnormal working conditions and their mutual influence are deduced from the mechanism. The application of the TSCA-SDG method in the catalytic cracking process shows that this method has good performance for abnormal condition identification.

Optimal Temperature-Based Condition Monitoring System for Wind Turbines

With the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.

Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

Abstract Identification of abnormal conditions is essential in the chemical process. With the rapid development of artificial intelligence technology, deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently. In the high-dimensional data identification using deep neural networks, problems such as insufficient data and missing data, measurement noise, redundant variables, and high coupling of data are often encountered. To tackle these problems, a feature based deep belief networks (DBN) method is proposed in this paper. First, a generative adversarial network (GAN) is used to reconstruct the random and non-random missing data of chemical process. Second, the feature variables are selected by Spearman's rank correlation coefficient (SRCC) from high-dimensional data to eliminate the noise and redundant variables and, as a consequence, compress data dimension of chemical process. Finally, the feature filtered data is deeply abstracted, learned and tuned by DBN for multi-case fault identification. The application in the Tennessee Eastman (TE) process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process, compared with the traditional fault identification algorithms.

Abnormal Condition Identification via OVR-IRBF-NN for the Process Industry with Imprecise Data and Semantic Information

Data-driven methods are commonly used to identify abnormal operating conditions to maintain the health and safety of processes, which assume that the process data are precisely known and single-val...

Abnormal Condition Identification Modeling Method Based on Bayesian Network Parameters Transfer Learning for the Electro-Fused Magnesia Smelting Process

When the data of target domain are scarce, the established model will not be accurate enough to analyze the target problem. For the abnormal condition identification modeling problem of electro-fused magnesia smelting process, this paper proposes the new Bayesian network (BN) parameters transfer learning method based on the expert knowledge from target domain to increase the accuracy of abnormal condition identification. First of all, the electro-fused magnesia smelting process is introduced and the existing research results on the abnormal condition identification are analyzed. The problem to solve in this paper is described. Furthermore, the constraints from expert knowledge for the target model are shown in two forms. The new BN parameters transfer learning method is proposed. Finally, the proposed method is verified by the Asia network, and it is applied to establish the abnormal exhausting condition identification model for the electro-fused magnesia smelting process. The simulation results demonstrate the effectiveness of proposed method which owns the better performances.

Abnormal condition identification and safe control scheme for the electro-fused magnesia smelting process

This paper presents a control framework to identify the abnormal condition and make safe control scheme by fusing the related multi-source information based on the analysis of the abnormal exhausting condition in the electro-fused magnesia smelting process. For the abnormal condition identification, the case-based reasoning is used to infer the degree of the abnormal condition. When the similarity of the matched case doesn't exceed the set threshold, Bayesian network is used to infer the degree. For the safe control scheme, taking advantage of the idea of the remaining lifetime predicting, and the adjustment of the control variables is obtained. The application results show that the proposed method is effective and it owns better performance than the manual and the existing research results.

Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process

To improve the performance of the abnormal condition identification, the multi-source information of the abnormal conditions in the electro-fused magnesia smelting process is analyzed in this paper. An intelligent abnormal condition identification method is proposed based on Bayesian network (BN). By analyzing three main abnormal conditions and the experience of the operators, the characteristics related with the abnormal conditions are extracted. The BNs are established to identify the abnormal conditions by fusing the multi-source information. The simulation results show that the proposed method can realize abnormal condition identification, distinguish the degree of the abnormal condition, and obtain better performance.

A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training.

BackgroundWe described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system.MethodsA 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated.ResultsThe 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%–65.2%, and complete agreement rate of all-three raters was 5.7%–43.3%. As for diagnoses, agreement of at-least two raters was 35.6%–65.6%, and complete agreement rate was 11.0%–40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties.ConclusionThis novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

Data-Driven Abnormal Condition Identification and Self-Healing Control System for Fused Magnesium Furnace

In the smelting process of fused magnesium furnaces (FMFs), frequent changes in the raw material granule size and impurity constituent will cause the arc resistance between the lower end of the electrode and the surface of the molten pool to vary, and thus, the smelting currents fluctuate. Consequently, abnormal conditions, which can arise if the setpoints of electrode currents are not properly adjusted on time, will causethe performance to deteriorate or even the overall operation to stall. Through analysis of the characteristics of different operating conditions, this paper presents a data-driven abnormal condition identification and self-healing control system. The proposed system extracts the identification rules according to the current tracking error, as well as the rate and duration of the current fluctuations, and identifies the abnormal conditions based on rule-based reasoning. The self-healing control is developed using case-based reasoning to correct the current setpoints based on the identification results. The outputs of the control loop track the corrected setpoints, thereby forcing the process to recover from the abnormal conditions. The proposed method and the developed control system have been applied to a real FMF, and substantial improvement is achieved with many benefits provided to the factory. The implementation results show that occurrence of abnormal conditions has been reduced by more than 50%, and the product quality has been increased by more than 2%.