Cloud-based data-intensive framework towards fault diagnosis in large-scale petrochemical plants

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE

HAZOP: Our Primary Guide in the Land of Process Risks: How can we improve it and do more with its results?

PresentationAll risk management starts in determining what can happen. Reliable predictive analysis is key. So, we perform process hazard analysis, which should result in scenario identification and definition. Apart from material/substance properties, thereby, process conditions and possible deviations and mishaps form inputs. Over the years HAZOP has been the most important tool to identify potential process risks by systematically considering deviations in observables, by determining possible causes and consequences, and, if necessary, suggesting improvements. Drawbacks of HAZOP are known; it is effort-intensive while the results are used only once. The exercise must be repeated at several stages of process build-up, and when the process is operational, it must be re-conducted periodically. There have been many past attempts to semi- automate the HazOp procedure to ease the effort of conducting it, but lately new promising developments have been realized enabling also the use of the results for facilitating operational fault diagnosis. This paper will review the directions in which improved automation of HazOp is progressing and how the results, besides for risk analysis and design of preventive and protective measures, also can be used during operations for early warning of upcoming abnormal process situations

Data-Based Semi-Automatic Hazard Identification for More Comprehensive Identification of Hazardous Scenarios

As chemical process plants have become more involved and complex, the likelihood of hazardous incidents has increased simultaneously. That is, the more complex a facility’s systems, the more factors engineers must consider. This results in a higher likelihood of potential hazards being overlooked; thus, the possibility of incidents occurring increases. Many companies and organizations are struggling to identify their weaknesses and reduce hazardous issues by developing hazard identification (HAZID) tools, particularly for large and complex processes. Even though a considerable number of companies merely pursue this objective to conform to government regulations, their efforts play a critical role in improving their reputations and financial profits. Therefore, the advancement of HAZID tools in the process industries has taken significant strides over the last 40 years. Despite the substantial development of HAZID methods, traditional HAZID tools need further development because of their weaknesses in identifying possible hazards. In other words, it is evident that unintended incidents that occasionally occur in the chemical process industry require more enhanced HAZID methodologies. Therefore, this study attempts to ascertain the drawbacks of existing HAZID tools so that a new HAZID methodology, data-based semi-automatic hazard identification (DAHAZID), is proposed. Considering potential HAZID methodologies, this study seeks to identify possible scenarios with a semi-automatic and systemic approach. Based on the two traditional HAZID tools, Hazard Operability study (HAZOP) and Failure Mode, Effects, and Criticality Analysis (FMECA), the DAHAZID method will minimize the limitations of each individual method. Additionally, rather than depending on the HAZID tools to achieve the connectivity of the process system, this study will consider connections with other new technologies in advance. Then, this method can be integrated with proper guidelines regarding process design and safety analysis. To examine its usefulness, the method will be applied to two case studies, and its outcome will be compared to the actual result, performed previously by a traditional HAZOP meeting. Hopefully, this research can contribute to the further development of the process safety field in practice

Digital strategy implementation in process manufacturing firms: the Sirmax case.

The elaboration aims to investigate how to effectively implement a digital strategy in process manufacturing firms. After having analyzed literature and benchmark cases, the focus is on the digital strategy implementation proposal for Sirmax, a process manufacturing firm.ope

Energy digital twin technology for industrial energy management: Classification, challenges and future

Digitalisation of the process and energy industries through energy digital twin technology promises step-improvements in energy management and optimisation, better servicing and maintenance, energy-efficient design and evolution of existing sites, and integration with locally and regionally generated renewable energy. This systematic and critical review aims to accelerate the understanding, classification, and application of energy digital twin technology. It adds to the literature by developing an original multi-dimensional digital twin classification framework, summarising the applications of energy digital twins throughout a site's lifecycle, and constructing a proposal of how to apply the technology to industrial sites and local areas to enable a reduction in carbon and other environmental footprints. The review concludes by identifying key challenges that face uptake of energy digital twins and a framework to apply the energy digital twins

Monitoring and Failure Recovery of Cloud-Managed Digital Signage

Digitaal signage kasutatakse laialdaselt erinevates valdkondades, nagu näiteks transpordisüsteemid, turustusvõimalused, meelelahutus ja teised, et kuvada teavet piltide, videote ja teksti kujul. Nende ressursside usaldusväärsus, vajalike teenuste kättesaadavus ja turvameetmed on selliste süsteemide vastuvõtmisel võtmeroll. Digitaalse märgistussüsteemi tõhus haldamine on teenusepakkujatele keeruline ülesanne. Selle süsteemi rikkeid võib põhjustada mitmeid põhjuseid, nagu näiteks vigased kuvarid, võrgu-, riist- või tarkvaraprobleemid, mis on üsna korduvad. Traditsiooniline protsess sellistest ebaõnnestumistest taastumisel hõlmab sageli tüütuid ja tülikaid diagnoose. Paljudel juhtudel peavad tehnikud kohale füüsiliselt külastama, suurendades seeläbi hoolduskulusid ja taastumisaega.Selles väites pakume lahendust, mis jälgib, diagnoosib ja taandub tuntud tõrgetest, ühendades kuvarid pilvega. Pilvepõhine kaug- ja autonoomne server konfigureerib kaugseadete sisu ja uuendab neid dünaamiliselt. Iga kuva jälgib jooksvat protsessi ja saadab trace’i, logib süstemisse perioodiliselt. Negatiivide puhul analüüsitakse neid serverisse salvestatud logisid, mis optimaalselt kasutavad kohandatud logijuhtimismoodulit. Lisaks näitavad ekraanid ebaõnnestumistega toimetulemiseks enesetäitmise protseduure, kui nad ei suuda pilvega ühendust luua. Kavandatud lahendus viiakse läbi Linuxi süsteemis ja seda hinnatakse serveri kasutuselevõtuga Amazon Web Service (AWS) pilves. Peamisteks tulemusteks on meetodite kogum, mis võimaldavad kaugjuhtimisega kuvariprobleemide lahendamist.Digital signage is widely used in various fields such as transport systems, trading outlets, entertainment, and others, to display information in the form of images, videos, and text. The reliability of these resources, availability of required services and security measures play a key role in the adoption of such systems. Efficient management of the digital signage system is a challenging task to the service providers. There could be many reasons that lead to the malfunctioning of this system such as faulty displays, network, hardware or software failures that are quite repetitive. The traditional process of recovering from such failures often involves tedious and cumbersome diagnosis. In many cases, technicians need to physically visit the site, thereby increasing the maintenance costs and the recovery time. In this thesis, we propose a solution that monitors, diagnoses and recovers from known failures by connecting the displays to a cloud. A cloud-based remote and autonomous server configures the content of remote displays and updates them dynamically. Each display tracks the running process and sends the trace and system logs to the server periodically. These logs, stored at the server optimally using a customized log management module, are analysed for failures. In addition, the displays incorporate self-recovery procedures to deal with failures, when they are unable to create connection to the cloud. The proposed solution is implemented on a Linux system and evaluated by deploying the server on the Amazon Web Service (AWS) cloud. The main result of the thesis is a collection of techniques for resolving the display system failures remotely

Digital twins: a survey on enabling technologies, challenges, trends and future prospects

Digital Twin (DT) is an emerging technology surrounded by many promises, and potentials to reshape the future of industries and society overall. A DT is a system-of-systems which goes far beyond the traditional computer-based simulations and analysis. It is a replication of all the elements, processes, dynamics, and firmware of a physical system into a digital counterpart. The two systems (physical and digital) exist side by side, sharing all the inputs and operations using real-time data communications and information transfer. With the incorporation of Internet of Things (IoT), Artificial Intelligence (AI), 3D models, next generation mobile communications (5G/6G), Augmented Reality (AR), Virtual Reality (VR), distributed computing, Transfer Learning (TL), and electronic sensors, the digital/virtual counterpart of the real-world system is able to provide seamless monitoring, analysis, evaluation and predictions. The DT offers a platform for the testing and analysing of complex systems, which would be impossible in traditional simulations and modular evaluations. However, the development of this technology faces many challenges including the complexities in effective communication and data accumulation, data unavailability to train Machine Learning (ML) models, lack of processing power to support high fidelity twins, the high need for interdisciplinary collaboration, and the absence of standardized development methodologies and validation measures. Being in the early stages of development, DTs lack sufficient documentation. In this context, this survey paper aims to cover the important aspects in realization of the technology. The key enabling technologies, challenges and prospects of DTs are highlighted. The paper provides a deep insight into the technology, lists design goals and objectives, highlights design challenges and limitations across industries, discusses research and commercial developments, provides its applications and use cases, offers case studies in industry, infrastructure and healthcare, lists main service providers and stakeholders, and covers developments to date, as well as viable research dimensions for future developments in DTs