Efficiency Of Industrial Systems Research Articles

Predictive Maintenance in Industrial Systems Using Machine Learning

Now, a lot of different areas need predictive maintenance (PdM). The goal is to cut down on downtime and make work go faster by finding out when things will break. This study looks at how machine learning can be used to figure out when to fix manufacturing systems. The study is all about using old business records, monitoring data, and upkeep records to make good prediction models. To make prediction tools that can quickly and accurately find places where industrial machinery might break down, we plan to carefully use advanced machine learning techniques such as supervised learning, time series analysis, and anomaly detection. Our idea could make it easier to stick to repair plans. Breakdowns would happen less often, and overall, running costs would go down in many fields. To prove that our expected method for maintenance works and can be used in the real world, we use careful case studies and thorough empirical validations. This research is a big step toward making models for planned maintenance, giving ways for proactive maintenance, and improving the dependability and efficiency of industrial systems in the real world.

An Interpretable Digital Twin for Self-Aware Industrial Machines.

This paper presents a proposed three-step methodology designed to enhance the performance and efficiency of industrial systems by integrating Digital Twins with particle swarm optimization (PSO) algorithms while prioritizing interpretability. Digital Twins are becoming increasingly prevalent due to their capability to offer a comprehensive virtual representation of physical systems, thus facilitating detailed simulations and optimizations. Concurrently, PSO has demonstrated its effectiveness for real-time parameter estimation, especially in identifying both standard and unknown components that influence the dynamics of a system. Our methodology, as exemplified through DC Motor and Hydraulic Actuator simulations, underscores the potential of Digital Twins to augment the self-awareness of industrial machines. The results indicate that our approach can proficiently optimize system parameters in real-time and unveil previously unknown components, thereby enhancing the adaptive capacities of the Digital Twin. While the reliance on accurate data to develop Digital Twin models is a notable consideration, the proposed methodology serves as a promising framework for advancing the efficiency of industrial applications. It further extends its relevance to fault detection and system control. Central to our approach is the emphasis on interpretability, ensuring a more transparent understanding and effective usability of such systems.

Forecasting the Efficiency of Innovative Industrial Systems Based on Neural Networks

Approaches presented today in the scientific literature suggest that there are no methodological solutions based on the training of artificial neural networks to predict the direction of industrial development, taking into account a set of factors—innovation, environmental friendliness, modernization and production growth. The aim of the study is to develop a predictive model of performance management of innovative industrial systems by building neural networks. The research methods were correlation analysis, training of neural networks (species—regression), extrapolation, and exponential smoothing. As a result of the research, the estimation efficiency technique of an innovative industrial system in a complex considering the criteria of technical modernization, development, innovative activity, and ecologization is developed; the prognostic neural network models allow to optimize the contribution of signs to the formation of target (set) values of indicators of efficiency for macro and micro-industrial systems that will allow to level a growth trajectory of industrial systems; the priority directions of their development are offered. The following conclusions: the efficiency of industrial systems is determined by the volume of sales of goods, innovative products and waste recycling, which allows to save resources; the results of forecasting depend significantly on the DataSet formulated. Although multilayer neural networks independently select important features, it is advisable to conduct a correlation analysis beforehand, which will provide a higher probability of building a high-quality predictive model. The novelty of the research lies in the development and testing of a unique methodology to assess the effectiveness of industrial systems: it is based on a multidimensional system approach (takes into account factors of innovation, environmental friendliness, modernization and production growth); it combines a number of methodological tools (correlation, ranking and weighting); it expands the method of effectiveness assessment in terms of the composition of variables (previously presented approaches are limited to the aspects considered).

The Environmental Efficiency Analysis Based on the Three-Step Method for Two-Stage Data Envelopment Analysis

This paper suggests that the efficiency of a system (decision-making unit) and its subsystem cannot be properly measured using a two-stage data envelopment analysis (DEA) model either in cooperative or non-cooperative evaluation. Indeed, the existing methods subjectively determine the status of the subsystems in the whole system. The two-stage DEA models, either cooperative game or non-cooperative game, are used to analyze the environmental efficiency. However, when the actual relationship between the two subsystems is inconsistent with the subjective relationship assumptions, the overall efficiency of the system and the efficiency of each subsystem will be biased. The conventional two-stage DEA models require predetermining the relationship between the subsystems within the system based on the subjective judgment of the decision-maker. Based on this, this paper proposes a three-step method to solve the two-stage DEA. First, the position relation among subsystems is determined according to the optimal weights through the model. According to the status relationship among subsystems, the decision units are grouped, and the two-stage DEA model of cooperative game or non-cooperative game is used to analyze the efficiency in each group. This method reduces the subjectivity of decision making and analyzes the efficiency of each decision unit applying the most appropriate two-stage DEA model to find the source of inefficiency. Finally, this paper verifies the rationality and validity of the method by analyzing the water use efficiency of industrial systems in China. It is found that most regions in China value economic development more than environmental protection (as evidenced by the DEA weights). What is more, the method proposed by the paper can be generalized for any two-stage DEA problem.

A Zoning-Based Secure Control Approach Against Actuator Attacks in Industrial Cyber-Physical Systems

The integration of cyber and physical domains improves the flexibility and efficiency of industrial systems. However, it also introduces security threats into the physical processes of the systems. More seriously, most of field devices, such as sensors and actuators, in industrial systems are vulnerable to security threats because they are usually not deployed with security countermeasures. Therefore, how to secure the physical processes of these industrial cyber-physical systems becomes an emerging problem. To address this problem, a zoning-based approach is presented in this article for identification and mitigation of actuator attacks in multiple-input multiple-output processes governed by linear time-invariant systems. It partitions the set of actuators into multiple zones that ensures the controllability of the surviving zones when a zone is compromised. When a zone is intruded, the system states of the compromised zone and other zones cooperate to confirm that the detected attack is against actuators. Then, to mitigate the impact of the actuator attack on the system functions, the attacked actuators are isolated. After that, the residual actuators in the surviving zones are used to reconfigure the control loops of the systems. Case studies are conducted to illustrate the effectiveness of the presented approach.

Methodology of Adaptive Prognostics and Health Management Using Streaming Data in Big Data Environment

Prognostics and health management (PHM) has gradually become an essential technique to improve the availability and efficiency of industrial systems. With the rapid advancement of sensor technology and communication technology, a huge amount of real-time data is generated from various applications industry, which brings new challenges to PHM in the context of big data streams. On one hand, high-volume stream data places a heavy demand on data storage, communication, and PHM modeling. On the other hand, continuous fluctuation and drift are essential properties of stream data in an online environment, which requires the PHM model to be capable to capture the new formation in stream data adaptively and continuously. This research proposes a systematic methodology to develop an effective online evolving PHM method with adaptive sampling mechanism against continuous stream data. An adaptive sample selection strategy is developed to effectively select the representative samples in both off-line and online environment. Meanwhile, a probabilistic theory-based modeling approach is developed to update the model with newly selected samples. Finally, the whole methodology is validated with real-world industrial cases. The result comparison between the proposed methodology and state-of-art methods verifies the superiority of the proposed method.

QRM Implementation as a Way to Increase the Efficiency in Single and Small Batch Production

Статья посвящена анализу характерных особенностей концепции быстрореагирующего производства, обеспечивающей повышение эффективности функционирования производственных систем. Применение данной концепции обеспечивает наиболее значительные результаты применительно к единичному и мелкосерийному производству, ориентированному непосредственно на удовлетворение потребностей конечного потребителя производимой продукции. Рассматриваются некоторые аспекты взаимодействия данной концепции с информационными системами, применяемыми для управления производством.

工业系统生态化研究的国际态势及其启示 International Research Advance of Industrial System Ecologization and Its Reflection

根据工业既是资源配置器，又是资源消耗和污染物产生的控制体这一特征，在过程上促进从“资源–产品–废弃物”的开环流程到“资源–产品–资源”的闭环流程的转换，对产业共生关系和环境协调，促进工业系统的可持续发展具有重要意义。针对工业活动对自然生态系统的影响，20世纪80年代末逐步形成了工业生态学(IE)的思想，20世纪90年代以来，国际学术界对工业系统降低环境影响、提高生态化水平的调控研究不断拓展，文章从工业生态学理论和应用工具、工业系统生态效率、工业系统能值、工业系统风险和脆弱性、工业系统生态模拟、工业系统绿色化和持续性管理等6个方面的研究进展进行了梳理和总结，在此背景下，获得了工业系统生态化研究的战略视野由“调整”转向“适应”、工业系统生态化研究的学科基础由“单一”转向“综合”的重要启示。In line with the feature of industry’s configurator of resource, as well as controller of pollutants discharge, it is of significant importance to shift from an open loop system of “resource-product-waste” to a closed loop system of “resource-product-waste”, especially in coordination between industrial symbiosis and the environment, and in sustainability of industrial system. Based on the impact of industrial activities on natural ecosystem, the idea of industrial ecology has formed gradually since 1980s; therefore, the research of industrial system-based ecology is expanding continuously in international academic society. This paper summarizes the international research advances and orientations in six aspects, including industrial ecology and practical tools, ecological efficiency of industrial system, emergy analysis of industrial system, risk and vulnerability of industrial system, ecological stimulation of industrial system, cleaning and sustainability of industrial system. And author provides twofold of research reflections, which are strategically shifting from “adjustment” to “adaptation” of industrial system in research perspective, shifting from “simplicity” to “integrity” in discipline base.

Code, culture and cash: The fading altruism of open source development (originally published in Volume 6, Number 12, December 2001)

The nexus of open source development appears to have shifted to Europe over the last ten years. This paper explains why this trend undermines cultural arguments about "hacker ethics" and "post-scarcity" gift economies. It suggests that classical economic theory offers a more succinct explanation for the peculiar international distribution of open source development: hacking rises and falls inversely to its opportunity cost. This finding throws doubt on the Schumpeterian assumption that the efficiency of industrial systems can be measured without reference to the social institutions that bind them.

Code, culture and cash: The fading altruism of open source development

The nexus of open source development appears to have shifted to Europe over the last ten years. This paper explains why this trend undermines cultural arguments about "hacker ethics" and "post-scarcity" gift economies. It suggests that classical economic theory offers a more succinct explanation for the peculiar international distribution of open source development: hacking rises and falls inversely to its opportunity cost. This finding throws doubt on the Schumpeterian assumption that the efficiency of industrial systems can be measured without reference to the social institutions that bind them.