An improved intelligent early warning method based on MWSPCA and its application in complex chemical processes

Fault Diagnosis of Dynamic Chemical Processes Based on Improved Residual Network Combined with a Gated Recurrent Unit

Aiming at the challenge of distinguishing the contributions of variables in dynamic chemical process data, this paper proposes a novel fault diagnosis method based on the IResNet-GRU model. First, we utilize principal component analysis to compute a correlation matrix, which serves as the input for an attention module. This approach enables the evaluation of feature contributions to predictions, thereby identifying the root-cause variables responsible for faults. Concurrently, we enhance the residual network (ResNet) with the attention module to assign weights to the extracted features. The improved ResNet (IResNet) can differentiate the significance of the monitored variables. Second, we augment the raw data into two-dimensional data using sliding window technology, capturing spatial and temporal data features. Finally, a gated recurrent unit is integrated to extract dynamic features from the augmented two-dimensional data effectively. The proposed method is validated using the Tennessee-Eastman chemical process. The diagnosis results demonstrate that the proposed method outperforms conventional methods.

An intelligent moving window sparse principal component analysis-based case based reasoning for fault diagnosis: Case of the drilling process

The drilling process is an important step in petrochemical industries, but the drilling process is risky and costly. In order to improve the safety and cost the impact of faults in the drilling process, this paper proposes intelligent moving window based sparse principal component analysis (MWSPCA) integrating case-based reasoning (CBR) (MWSPCA-CBR) in the fault diagnosis of the drilling process in the petrochemical industry. Through introducing sparsity into the PCA model, the Lasso constraint function of the MWSPCA method is used to optimize the sparse principals. The corresponding T² and Q statistics calculated by the selected sparse principals decide whether the faults have occurred, and the occurrence time of the anomaly is quickly located based on the MWSPCA method. Then the CBR method is used to analyze the anomaly data to identify the possible fault types, and provide the relational handling methods for real-time monitoring experts. Finally, the MWSPCA method is verified based on the intelligent diagnosis of the Tennessee Eastman (TE) process, reducing false negatives and false positives and improving the accuracy rate and the diagnosis speed. Furthermore, the proposed method is applied to analyze the data of the drilling process. The experimental results demonstrate that the proposed method can effectively diagnosis faults in the drilling process and reduce risks and costs in the petrochemical industry.

Short-Time Wavelet Entropy Integrating Improved LSTM for Fault Diagnosis of Modular Multilevel Converter

The modular multilevel converter (MMC) is the main part of MMC-based high-voltage direct current (HVDC) system. The MMC bridge arm inductance fault and the submodule IGBT fault have the greatest influence on the transmission quality of transmission systems. Therefore, this article proposes a novel fault diagnosis method based on short-time wavelet entropy integrating the long short-term memory network (LSTM) and the support vector machine (SVM). The proposed short-time wavelet entropy calculation method is used to extract the fault information. First, the optimal short-term wavelet packet calculation period is determined. Moreover, the improved LSTM topology can process the wavelet entropy fault information in the time dimension. Then, the output of the LSTM is set as the input of the SVM to obtain the fault diagnosis result based on the adaptive classification. Finally, through the MMC fault diagnosis experiment of the double-ended MMC-HVDC transmission system, the effectiveness of the proposed method is verified. Compared with the traditional fault diagnosis method, the proposed method has better robustness, adaptability, and accuracy, which can greatly reduce the number of electrical signal samples and realize the fault diagnosis of multiple fault types by collecting a single signal.

Designing Smart Space Services by Virtual Reality-Interactive Learning Model on College Entrepreneurship Education

The purpose was to improve the limitations of traditional entrepreneurship education, realize the virtual interactive learning between college students and teachers, and stimulate students’ exploration of entrepreneurship. This work first discusses the working principle of Virtual Reality (VR) and builds an Interactive Learning Model (ILM) using VR. Then, the VR-ILM is used to design the Smart Space services. Harris Corner Detector (HCD) is used to detect the pixel grayscale change in the Smart Space image window. Further, the VR-ILM-based Smart Space is proposed according to the Smart Space design requirements and principles. Finally, the proposed VR-ILM-based Smart Space is applied to College Entrepreneurship Education (CEE). Its impact on the CEE market, employment in different industries, and students’ satisfaction with CEE are studied. The results show that the proposed VR-ILM-based Smart Space has increased the entrepreneurship teaching courses, entrepreneurship coaching activities, and entrepreneurship practice activities by 4, 6, and 24%, respectively. It has reduced entrepreneurship competitions and other forms of entrepreneurship education by 4 and 16%. The proposed VR-ILM-based Smart Space has dramatically improved the practical teaching of CEE. Meanwhile, real estate services have felt the most significant impact of the proposed VR-ILM-based Smart Space, with an employment increase of 43%. Lastly, students’ satisfaction with entrepreneurship education practice and teaching methods has increased by nearly 50%. The satisfaction with the internal environment has increased to 78%. The satisfaction with the curriculum system, teachers, and industry financing has increased from 30 to 45%, 24 to 36%, and 45 to 63%, respectively. The satisfaction with the teaching goal has increased to 62%. Thus, the proposed VR-ILM-based Smart Space has dramatically improved students’ satisfaction with CEE and has a different impact on the market, industry, and satisfaction with CE. The finding has a certain reference for the VR interactive model.

Pseudo fourth-order moment based bearing fault feature reconstruction and diagnosis

In this paper, a novel bearing fault diagnosis approach based on pseudo fourth-order moment is proposed and verified. Based on the wavelet decomposition of different vibration signals, the pseudo fourth-order moment is calculated, and a new fault feature-the feature angle is proposed. Feature angle between these pseudo fourth-order moments is obtained, and corresponding working condition maybe characterized by this feature angle. The ranges of these feature angles are determined by simulation experiments with a large amount of data. An assessment index (namely selection index of optimal data size) is constructed to select the optimal amount of computational data. Meanwhile, extreme learning machine (ELM) model is established to classify different working conditions. According to the ELM model obtained from training data, the accuracy of test data classification reaches 95.42%, which proves the effectiveness of present bearing fault diagnosis method.

Chemical static equipment commonly used sensor fault detection and isolation methods

This paper conducts a study on the faults of common sensors involved in chemical static equipment. Firstly, the types and characteristics of commonly used sensors of chemical static equipment are analyzed, and the characteristics of sensor output signal changes are summarized with the working characteristics of chemical equipment. Then the faults of static equipment sensors are classified and a fault model is established. Through the study of sensor fault detection and isolation methods at home and abroad, the overall scheme of sensor system fault detection and isolation combining single sensor fault detection and isolation method and multi-sensor fault detection and isolation method is proposed. According to the characteristics that chemical processes are generally in a dynamic and stable state and there is a certain correlation between the signals of each detection point in the equipment, a sensor system model is established by using the correlation of multiple sensors on the equipment, and when a sensor in the sensor system fails, the system model changes beyond the threshold value, and a different form of residual generation is used to determine which sensor is faulty and achieve the detection and isolation of faulty sensors. The fault detection method is simulated and studied by using relevant software, combined with a support vector machine and neural network toolbox. The results show that the method proposed in this paper can effectively complete the fault detection and isolation of sensors commonly used in chemical static equipment. The accuracy and reliability of the prediction model are high.

Using Augmented Reality and Internet of Things for Control and Monitoring of Mechatronic Devices

At present, computer networks are no longer used to connect just personal computers. Smaller devices can connect to them even at the level of individual sensors and actuators. This trend is due to the development of modern microcontrollers and singleboard computers which can be easily connected to the global Internet. The result is a new paradigm—the Internet of Things (IoT) as an integral part of the Industry 4.0; without it, the vision of the fourth industrial revolution would not be possible. In the field of digital factories it is a natural successor of the machine-to-machine (M2M) communication. Presently, mechatronic systems in IoT networks are controlled and monitored via industrial HMI (human-machine interface) panels, console, web or mobile applications. Using these conventional control and monitoring methods of mechatronic systems within IoT networks, this method may be fully satisfactory for smaller rooms. Since the list of devices fits on one screen, we can monitor the status and control these devices almost immediately. However, in the case of several rooms or buildings, which is the case of digital factories, ordinary ways of interacting with mechatronic systems become cumbersome. In such case, there is the possibility to apply advanced digital technologies such as extended (computer-generated) reality. Using these technologies, digital (computer-generated) objects can be inserted into the real world. The aim of this article is to describe design and implementation of a new method for control and monitoring of mechatronic systems connected to the IoT network using a selected segment of extended reality to create an innovative form of HMI.

Consumers’ Perceptions of the Supply of Tap Water in Crisis Situations

This paper first presents the results of polling on the subject of potable water in crisis situations, with respondents from south-eastern Poland’s Subcarpathian region asked for their opinions on the level of nuisance associated with water supply interruptions and water quality, levels of consumption and water companies’ quality of service. Among the respondents 53% regard the quality of the water they receive as satisfactory, while a quarter see it as only average. However, respondents are relatively satisfied with the corporate response when supplies are interrupted, as methods and means of notification are judged effective by 60%. Continuing with work to assess possibilities for water companies to improve their performance in crisis situations, the present study generates an Analytical Hierarchy Process allowing recipients to determine importance criteria where quality of service is concerned. This could facilitate management by water companies, providing for centralised control and comparison that help secure services of appropriate quality. The process can also help protect different groups of recipients, as safety is evaluated through analysis of functioning, and of failures and losses.

Water Network-Failure Data Assessment

The water-supply system is one of the basic and most important critical infrastructures. Water supply service disruption (water quality or quantity) may have serious consequences in modern societies. Water supply service is subject to various failure modes. Failure modes are specified by their degradation mechanisms, criticality, occurrence frequency and intensity. These failure modes have a random nature that impacts on the network disruption indicators, such as disruption frequency, network downtime, network repair time and network back-to-service time, i.e., the network resilience. This paper focuses on the water leakage failure mode. The water leakage failure mode assessment considers the unavoidable annual real water losses and the infrastructure leakage index recommended by the International Water Association’s Water Loss Task Force specialist group. Probabilistic statistical modelling was implemented to assess the seasonal index, the failure rates and the expectation value of the “mean time between failures.” The assessment is based on real operational data of the network. Specific attention is paid to the sensitivity of failures to seasonal variations. The presented methodology of the analysis of the water leakage failure mode is extendable to other failure modes and can help in developing new strategies in the management of the water-supply system in normal operation and crisis situations.