Analysis and Application of Energy Management in Industry 4.0 with TRIZ Methodology

The Internet of Things (IoT) has the potential to revolutionize energy management by enabling the collection and analysis of real-time data from various energy sources. This research paper investigates the impact of the Internet of Things (IoT) on energy management. The paper provides an overview of IoT and its potential applications in energy management, including improved efficiency, reduced costs, and better resource utilization. The benefits of using IoT for energy management and the major challenges that may arise in implementing IoT-enabled energy management are discussed. Potential solutions to these challenges, such as artificial intelligence and cloud computing, are presented, along with case studies of IoT-enabled energy management in different industries. The paper also analyzes the impact of IoT on energy efficiency in telecommunications and cloud infrastructure. Finally, the future outlook for IoT and energy management is discussed, including potential developments in edge computing, advanced analytics, and 5G networks. Overall, this paper highlights the potential of IoT to revolutionize energy management and provides insights into the challenges and opportunities of implementing IoT-enabled energy management solutions.

In this Paper, we introduce a novel FMEA (Failure Mode and effect Analysis) system, It can achieve FMEA more practicable and valuable compared with current FMEA application status as record archives. The novel FMEA basic unit is module and related modules are combined to form a whole FMEA, Six new link/experience function modules are introduced into the standard FMEA format to integrate the database, and the module unit exists independently so that different FMEA file cross share the similar failure modules. Three new link function modules can connect FMEA system with other related production systems to embed FMEA useful resource into production as guidance, this link function can prevent potential and old failure modes timely occurring timely, further reduce defect and improve production efficiency and quality. This novel FMEA tool can make FMEA more value and important functions in semiconductor process.

As the world advances into the information age, the proliferating demand for energy entails an increasing need for effective smart energy management systems. The resulting smart grids and smart energy management solutions are beginning to generate Big Data; high-variety data at cumulative volumes and velocity. Simultaneously, data analytics techniques and methodologies are being introduced to comprehend this changing nature of data. A number of conventional data mining techniques have successfully transitioned into the Big Data landscape. However, integration of multi-source information in this landscape remains hitherto unaddressed. In this paper, we present a novel data fusion technique that incrementally integrates information from multiple sources. Based on an incremental, unsupervised learning algorithm and possibilistic fusion, the technique overcomes limitations to continuous learning and integrating information of mixed granularity. The technique is also extensible into the Big Data landscape. The collective effect of these features postulate smart energy management as a fitting application domain for the proposed technique. We demonstrate practical applicability of the technique using a household energy and utility consumption dataset. Results and analytics outcomes from these experiments confirm its effectiveness as a data fusion technique and its extensibility into further high-volume applications in smart energy management.

Internet of things (IoT) is a developing concept, which aims to associate billions of devices with each other. The IoT devices sense, assemble, and transfer important information from their environments. This exchange of very large amount of information among billions of devices makes an enormous energy need. The radical growth in urbanization over the last few years needs sustainable, proficient, and smart clarifications for transport, governance, environment, quality of life, and so on. The IoT propose many urbane and universal applications for smart homes. The energy demand of IoT applications is greater than before; while IoT devices carry on to grow in both numbers and necessities. Therefore, IoT-based smart home and its automation must have the capability to competently consume energy and control the allied challenges. Energy management is considered as a key prototype for the comprehension of composite energy systems in smart homes. Further, smart home solutions have to be energy-efficient from both the users’ and environment’s points of view. In other words, smart home solutions have to be energy-efficient, cost-efficient, reliable, secure, and so on. For example, IoT devices should operate in a self-sufficient way without compromising quality of service (QoS) in order to enhance the performance with unremitting network operations. Therefore, the energy efficiency and life span of IoT devices are the key issues to next generation smart home solutions. It has been studied the electrical energy consumption from a prevailing house to make it further efficient presenting as much as possible IoT applications. The smart home applications that are straightforwardly associated with energy efficiency are obviously the light and the temperature monitoring. Hence, they are significant to assure the energy saving. Other smart home arrangements, similar to Fire Detection, Security, are not straightforwardly linked with the energy efficiency. Keywords: IoT, smart home, energy efficiency, Home Appliances, smart grid Cite this Article Partha Ghosh, Suradhuni Ghosh. IoT and Machine Learning in Green Smart Home Automation and Green Building Management. Journal of Alternate Energy Sources & Technologies . 2019; 10(3): 8–36p.

Failure Modes and Effects Analysis (FMEA) is a systematic, learning retention vehicle originally developed by Ford Motor Company in the 1970s to aid the engineer in assessing potential failure modes and design in risk prevention measures for the automotive industry. This same approach can be applied, in much the same fashion, to the semiconductor manufacturing industry and result in retained learnings and a ranked priority of fab and die yield improvement activities. The FMEA system, as it is being applied to a National Semiconductor fab consists of a series of information templates that properly documents relevant information for each major processing step of each major process technology. This paper describes this FMEA system as it has been modified for use by National Semiconductor. The FMEA information template is detailed and the development and implementation approach on several of National Semiconductors major process technology flows is reviewed. The fit of the FMEA tool in the total process control scheme is discussed. In addition, specific examples of completed FMEAs for specific fabrication processing steps are presented along with actions taken to minimize calculated risk factors.

The use of Failure Mode and Effect Analysis (FMEA) has become widespread in various industries to address quality and reliability issues that may occur in a system or process. However, the risk assessment process using traditional FMEA is very time-consuming because it requires the evaluator to assess the risk for each identified potential failure mode one by one, based on the extracted historical data. Without proper consideration of the system or process being evaluated, the evaluator may make incorrect judgments, causing the FMEA results to be inaccurate and unreliable. In this study, an interactive FMEA tool that uses a standard risk factor input as the main reference was developed using Excel software and Tableau software. Semi-structured interviews were conducted at the initial stage of the study to obtain basic information about the existing FMEA approach in the semiconductor industry. With this information, a standard reference for risk factor input was established for the FMEA tool. The developed tool was then tested in the real semiconductor industry to validate its effectiveness and practicality. The results show that the developed interactive FMEA tool holds significant potential for industrial usage in terms of streamlining the FMEA analysis process, providing a comprehensive visualization of the identified potential failure modes, and improving information sharing within the organization. With the presence of this interactive FMEA tool, users can not only produce more accurate and reliable FMEA results based on built-in risk factor input standards, users can also view the generated FMEA results online through the Tableau dashboard.

Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management

This article explored the application of smart energy management in the green Internet of Things (IoT), with the goal of improving energy utilization efficiency and achieving energy conservation and emission reduction. Through analysis of existing research, it was found that traditional energy management methods had many shortcomings in real-time monitoring, data processing speed, and intelligence level. To address these issues, this article proposed an intelligent energy management method based on the DRL (Deep Reinforcement Learning) algorithm. Based on the DRL algorithm, the average energy consumption was 11.5 kWh. Secondly, in terms of device collaboration capability, the DRL algorithm significantly reduced data transmission latency. In the final reliability evaluation of intelligent energy management system based on DRL algorithm, the average recovery time of DRL algorithm was 40 seconds, and the average task completion rate was 90.2%. From the data conclusion, it can be seen that the intelligent energy management system based on DRL algorithm has significant advantages in improving energy utilization efficiency, optimizing equipment collaboration ability, and enhancing system reliability.

A review of barriers to and driving forces for improved energy efficiency in Swedish industry– Recommendations for successful in-house energy management

The emergence of Internet of Things (IoT) the networking of objects and sensors remotely plays an important role in many applications of smart home, elder care, transportation, medical, agriculture, energy management and etc. The wireless sensor network (WSN) as a subset of IoT, facilitates IoT based networking by their placement of sensors for monitoring and recoding. The Received Signal Strength Indicator (RSSI), as the quantitative indicator of Received Signal Strength (RSS), is mostly used on indoor localization by placing sensors in WSN. In this research we use to place Wi-Fi nodes in WSN for acquisition of RSSI remotely over on cloud architecture for IoT. Predominantly this research compares two position estimation approaches used with WSN for localization, which essentially a part of object tracking in indoor environment. The Artificial neural network (ANN) based Feed Forward Neutral Network (FFNN) and deterministic trilateration techniques are used for position estimation, and their results are compared to put forward the appropriate localization technique for indoor environment. The paper presents distinctly the FFNN is the most suitable two dimensional (2D) positioning model for indoor localization.

Big data driven smart energy management: From big data to big insights

Objective To explore the application effect of Failure Mode and Effect Analysis (FMEA)Mode in venous indwelling needle in pediatric emergency department.Methods 240 cases of patients with venous indwelling needle in pediatric emergency department from January to December 2011 were enrolled in the research.120 cases from January to June who used indwelling needle before FMEA were the control group,and 120 cases from July to December who used indwelling needle after FMEA were the observation group.FMEA was used to search the possible failure modes,causes and results,and calculate the RPN values.Reform measures were implemented according to the high risk factors of the lien family influence.Two groups' catheter obstruction,extravasation,accidental withdrawal/removal of needles,RPN values,and retention time were compared.Results After FMEA,the RPN values in terms of safety and quality including accidental withdrawal of needles,catheter obstruction and extravasation were obviously lower in the observation group (48.00± 9.13,72.00 ± 27.96,140.00 ± 18.39,respectively) than in the control group (96.00 ± 12.47,288.00 ± 37.40,168.00 ±31.24,respectively),and the differences were statistically significant (t =8.79,13.08,2.22,respectively; P ＜ 0.05).Retention time in the observation group (2.02 d) was also shorter than that in the control group (3.95 d),and the difference was statistically significant (t =5.39,P ＜ 0.01).Conclusions The application of FMEA mode is conducive to improve the risk management of venous indwelling needle in pediatric emergency department,ensure the infusion safety,reduce complications and prolong retention time,thus is worth promoting in the pediatric emergency department. Key words: Pediatric department; Outpatient clinics and emergency department; FMEA; Indwelling needle

Objective To consummate the cleaning process of medical power tool so as to improve the quality of cleaning of medical power tool. Methods The failure mode and effect analysis (FMEA) project group was built up in April 2016. The cleaning process of medical power tool was analyzed with the method of FMEA. The intervention was carried out aiming at high risk factor. Besides, the cleaning process of medical power tool was consummated and improved continually combining the detection results of ATP bioluminescence assay. The risk assessment of the cleaning process of medical power tool was carried out to determine high risk factors and formulate as well as implement improvement measures. The control effect of risk priority number (RPN) of cleaning on medical power tool and cleaning effect were compared before (March 2016) and after (September 2016) FMEA. Results The RPN of the cleaning process of medical power tool decreased from (218.17±89.14) before implementing FMEA to (33.26±9.54) after implementing FMEA. The qualified rate of ATP bioluminescence assay in washing quality increased from 68.6% to 98.5% before and after implementation with a significant difference (χ2=22.94, P<0.01) . Conclusions The application of FMEA can effectively control the risk factors of the cleaning process of medical power tool, improve the cleaning quality of power tool and reduce the opportunity for infection in patients. Key words: Failure mode and effect analysis; Cleaning; Medical power tool; Management

Nowadays, industrial manufacturing companies are expecting and facing strong demand to increase their productivity by realizing smart factories and smart manufacturing. Machine automation, monitoring of equipment and machines, predictive maintenance, production traceability, and energy management are essential and inevitable. This chapter presents ideas about the importance of automation in energy management with the help of Industries 4.0 ecosystem. Industry 4.0 revolves around technologies like intelligence production incorporated with Internet of Things, cloud technology, and Big Data. There are four important concepts of Industry 4.0 in energy and utility management application: extensive monitoring; industrial internet of things; analysis of large volumes of data; and efficiency and sustainability. Behind the entire investment in Industry 4.0 lies a common objective: increasing the efficiency and competitiveness of an operation. The motivation comes from a combination of environmental aspects, cost pressure, and regulation, as well as the proactiveness of organizations when it comes to efficient consumption of energy and utilities.

Objective To study the value of failure mode and effect analysis (FMEA) in patients undergoing digestive endoscopy. Methods By convenience sampling, a total of 96 cases undergoing endoscopy admitted in Xinxiang Central Hospital from March 2017 to February of 2019 were selected as the study subjects. The 48 patients enrolled from March 2017 to February 2018 were taken as the control group and given conventional nursing management mode; the other 48 patients enrolled from March 2018 to February 2019 were taken as the observation group and given FMEA management. The two groups were analyzed in the average waiting time for endoscopy examination, recovery time of enterological function after simple examination and compared in terms of the incidence of adverse events regarding nursing safety. Results The RPN value of 12 main failure modes in the observation group after FMEA management was lower than that before FMEA Management with statistical significance (P<0.01) . The average waiting time of endoscopy, the recovery time of gastrointestinal function after endoscopy, the recovery time of gastrointestinal function after endoscopy and the hospitalization time of simple examination in the observation group were shorter than those in the control group, and the differences were statistically significant (P<0.01) . The incidence of nursing safety adverse events in the observation group was 4.17% (2/48) , lower than 18.75% (9/48) in the control group, the difference was statistically significant (P<0.05) ; among them, the observation group nursing safety adverse events were mostly drug and diet, while the control group was mainly drug and other categories. Conclusions After FMEA intervention, RPN value of failure mode items decreased significantly, which could promote early recovery of gastrointestinal function, shorten hospitalization time, and reduce the incidence of nursing safety adverse events in the process of endoscopy. Key words: Digestion; Failure mode and effects analysis; Digestive endoscopy examination; Crisis value; Adverse events