Such an assumption is being proved wrong by the engineers who work hard on finding solutions to convert these fossil fuel-consuming machines to also operate on renewable fuels. ICE-based power plants have a crucial role in the green transition as a balancing element for the fluctuating nature of wind and solar energy production.

Vibration analysis and machine learning methods

The future goals impose new requirements and raise uncertainties considering the whole lifecycle of the power plants. They generate a need for the development of new tools and methods in a wide range. Within the operational phase of the lifecycle, particularly in the domain of structural condition monitoring, vibration analysis techniques have long been the cornerstone of getting precise insights into the health of rotating machinery and along with the operational data estimating their remaining useful life. On the other hand, increased computing power, and the emergence of the industrial internet of things (IIoT) have created a foundation for continuous operational monitoring in real-time, or at least in near real-time. In this context, vibration analysis (VA) and machine learning (ML) methods can be used to build precise and efficient state recognition models for rotating machines as shown in this case.

Operational state recognition of a generating set

This article introduces simple and computationally light models for the operational state recognition of a generating set (genset). The models were developed in a research project (Digibuzz-VTT) forming part of a joint research effort called DigiBuzz financed by Business Finland and are thoroughly described in a master’s thesis [1]. DigiBuzz was led by LUT University between 10/2019 and 01/2022. One of the partner companies in DigiBuzz, Wärtsilä Finland Oy, provided the dataset for building the operational state recognition models. The data consists of accelerations acquired from a Wärtsilä 20V31SG genset measured at various constant power output levels, as well as during some occasional fault situations. Gensets combine an ICE and an electric generator. They are typically used for producing power to the electric grid. While the electric grids have constant frequency, the power demand fluctuates. As a result, the gensets operate at constant speeds but with variable power output. The grids may encounter occasional disturbances which cause abnormal operation of a genset. Thus, the dataset effectively covers the acceleration response of a genset within its typical operational range.

Inertia forces and gas forces

The operational state recognition models discussed in this article are built around the cyclic nature of the operation of ICEs. The general assumption is that the dynamic behaviour, at steady load and constant rotational velocity across engine cycles, repeats itself and that load variation can be seen as a notable change in the dynamic response. Thanks to Newton, most of us know that acceleration and vibration is caused by force, and think that the relation between them is linear. Considering ICEs, the principal forces exciting vibrations can be divided into inertia and gas forces. The origin of the inertia forces are the moving parts of the engine, namely the crank and piston mechanisms. Thus, at constant rotation speed the inertia forces remain periodically stationary. However, due to the virtual linearity between force and acceleration, the gas forces, provoked by the cylinder pressure, do vary in sync with load variations, even though the rotation speed remains constant, since they are responsible of making the engine run and they must adjust to the load demand. Normalized tangential forces at crank pin for different loads during one engine cycle (four-stroke) are presented in Figure 1.

Therefore, if the detection of variations in the load is of interest, it is crucial to extract only the effect of the gas forces on the vibration response. Unlike the gas forces, the inertia forces have an analytic solution which happens to be periodic. It states that the inertia forces have cyclic components only at the frequency of rotation and its second multiple, which then leads to all the other frequency components of the vibration response to depend only on the gas forces. The harmonic frequency components of a signal can be efficiently computed using fast Fourier transform (FFT). The harmonic coefficients of the torque of a four-stroke gasoline engine at full load and at idle presented in Figure 2 were determined by Porter as early as in 1943 [2]. For a four-stroke engine one engine cycle equals two rotations of the crankshaft. In Figure 2 order 1.0 equals the rotation frequency and hence order 0.5 the engine cycle frequency.

Accurate ML models are seldom trained using raw data. The training of ML models often needs features that are sensitive to changes in the quantity being predicted by the model. Considering ICEs (and all the previous explanations), a feature sensitive to power output variation is extracted from the vibration response by simply computing the harmonic coefficient at order 1.5. This feature extracted from the three signals of only one suitably placed triaxial accelerometer can be used for training an accurate classifier of different power output levels of a Wärtsilä 20V31SG genset. The accuracy of the classifier model can be increased by adding the signal power of the three signals to the features of the model.

Smoothing out cyclic variations is possible

However, the operation of an ICE in practice is never perfectly constant between engine cycles even at steady load and therefore there is always cyclic variation in the acceleration response as well. This is typical especially considering spark ignited engines, such as the Wärtsilä 20V31SG, for which the peak cylinder pressure between consecutive cycles varies significantly. Considering the presented state recognition models the effect of the cyclic variation can be smoothened by extracting the feature values from signal segments that are multiple engine cycles long. By extending the length of the signal segment the prediction given by the model gets further away from real-time. Therefore, the right balance between the accuracy and timeliness of the model must be sought depending on the application and needs. In this case the accuracy is very high even when using signal segment length of two engine cycles. At the nominal operation speed of the genset, that is at 750 rpm, one engine cycle lasts 0.16 seconds.

The confusion matrix of a classifier trained with features extracted from two engine cycles long signal segments is presented in Figure 3. Logistic regression was used as the classifier algorithm and the features were the acceleration amplitude at order 1.5 and the signal power extracted from the signals of one triaxial accelerometer. The classes are different power output levels givens as percentages of the rated power of the genset: 0 %, 50 %, 75 %, 90 %, 95 %, and 100%.

Novelty detection can recognise abnormal operation

The recognition of abnormal operation can be done using novelty detection. Novelty detection is a subtype of binary classification in which a trained model predicts if a data sample belongs to the same class of the data it was trained with or not. The same features that were used for training the classifier model can be used for training the novelty detection models as well. Separate novelty detection models can be built for each power output level. The result of two novelty detectors trained using different algorithms, One-class support vector machine (OC SVM) and local outlier factor (LOF), are presented in Figure 4.

Features extracted from continuous one-minute-long signals of one triaxial accelerometer were given as input for the novelty detectors. The novelty detector value 0 represents normal operation and value 1 abnormal operation. The result is given as a moving average taken over a window of one engine cycle and step size of one. The abnormal operation of the genset took place at around 30 seconds which is clearly detected by both novelty detectors. [1]

Further development of the recognition models

An ambitious future goal is not only the timely detection of abnormal operation but also the recognition and classification of different types of faults. The scarcity of data measured during fault situations hinders the development of such models. However, one possible solution could be the production of data through simulations of fault situations. In fact, the first steps in that direction have already been taken using finite element method simulations of a genset (Figure 5) [3]. Further development of the operational state recognition models and their deployment in industrial settings has been planned to take place soon as part of new joint development projects between Wärtsilä, VTT, and (hopefully a long list of) other interested parties.

 

Photo: Wärtsilä corporation

References
[1] Junttila, J., 2021, Operational State Recognition of a Rotating Machine Based on Measured Mechanical Vibration Data. Master’s thesis, Arcada University of Applied Sciences (2021)
[2] Porter, F.P., 1943, Harmonic Coefficients of Engine Torque Curves. In: ASME, Journal of Applied Mecchanics, 10(1): A33-A48. DOI: https://doi.org/10.1115/1.4009248
[3] Junttila, J., Sillanpää, A. Lämsä, V.S., 2022, Validation of Simulated Mechanical Vibration Data for Operational State Recognition System, 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI), San Diego, CA, USA, 2022, pp. 138-143, doi: 10.1109/IRI54793.2022.00040.

The ongoing energy transition is fundamental, affecting all levels of society. It is also highly political, challenging existing markets and business models. Not to forget that digitalisation adds a third “cyber” layer to the more traditional socio-physical systems. Digitalisation is being considered the primary solution to control the increasingly electrified, fragmented and sector coupled energy production and consumption systems.

The concept of energy transition does not automatically equal the use of renewables nor sustainability outcomes. It can also entail the change from one polluting source or unsustainable behaviour to another.

Historically, energy transitions have been driven by the need and availability of energy sources. For example, Fouquet and Pearson (2012) define energy transition as “the switch from an economic system dependent on one or a series of energy sources and technologies to another”.

Research shows that most transitions seem to have unfolded over long periods of time; for example, oil was drilled from the first commercial well in the US in 1859, but the market share of 25% was passed in 1953. Then, there is evidence of quick energy transitions as well. For example, Brazil managed to increase ethanol production and substitute ethanol for petroleum in conventional vehicles so that in 1981, six years after the Proálcool program started in November 1975, over 90% of all new vehicles sold in Brazil could run on ethanol (see Sovacool 2017). One could suggest that the ongoing European “Green Deal” or the global “Grand transition” (a name coined by the World Energy Council) seem to be moving relatively fast compared to most historical transitions. Time will tell how they compare to them.

Considering the current global geopolitical situation and its effects on the investment landscape, countries dealing with energy scarcity and security issues, shifting power balances between big economies, as well as new innovations entering the markets, we are definitely in the middle of a great shift. The Paris Agreement (COP21), with its aim to halt global warming, is still working as a backbone for international cooperation and guiding national energy strategies in many countries. The outcomes of what has been put into motion by these international agreements are being materialised at the national and local level.

It has been suggested that energy transitions are becoming more of a social or political priority in ways that previous transitions have not been. In earlier times, the transitions may have been accidental or circumstantial, whereas future shifts have become more planned and coordinated. It is important to remember that something inherent to the consumption and production of energy is human power dynamics. According to Avelino (2017), understanding the politics of transitions requires careful attention to the question of who wins or loses when new innovations emerge and get implemented and which vision(s) of the future predominate in deciding the direction of energy transitions. Politics is linked to issues of power and agency and are closely related to the theme of governance and the implementation of transitions.

The last ten years have introduced us to concepts such as prosumers, energy communities, microgrids, smart cities, carbon sinks, net zero buildings, energy poverty, flexibility markets and so on, involving “ordinary” people with energy issues, compared to what was earlier considered something of a “plug in the wall” commodity. Especially now, in the aftermath of the so-called EU energy crisis (I am writing this paper in September 2023), many Finns, together with the rest of the EU, are probably wondering how the coming winter weather will affect the electricity prices after the first “expensive winter”.

Understanding the socio-cultural embeddedness of energy

On the EU level, the Roadmap 2030 and European Green Deal are shaping the energy market towards, for example, a massive growth in wind power investments and instalments of solar power (also on household level). The next step seems to be the roll-out of hydrogen solutions, all in the support of the increasing electrification and digitalisation of the energy sector. As new technologies, modes of operating, actors, services, and applications enter local markets, they inevitably cause positive and negative disruptions to people’s lives.

The age of specialisation in a highly technological society, such as the Western society, means that our daily lives are embedded in technology that requires expertise and different outside services. Even if most of us agree that modern society has come a long way in making life comfortable and safe, it seems we might forget some of the basics that humans are psycho-physical beings. Our senses capture information on many levels and the rational mind is just the tip of an iceberg compared to the subconscious mind. We are also creatures of habit and “cultural animals” formed by our socio-cultural contexts. This means many shared collective beliefs set the base for our well-being and a sense of belonging to certain landscape(s), nature, music, family, and community. When something disrupts the existing order of things, it also challenges our inner (subconscious) feeling of safety – whether we are aware of it or not.

It still seems to surprise many tech developers that suddenly – “out of the blue” – people start opposing a solution which seems perfectly straightforward… at least from the perspective of the person designing it. Still, there is a good chance that it disrupts something of intrinsic value to people. As in, for example, wind parks built in a popular outdoor area where local people have hunted, picked berries, or just wandered for generations. Thus, the technological function and its usefulness are understood, but they collide with other values, leading to adverse feelings and reactions.

The art of listening

Although the energy technology and digital solutions are the same (or similar) in most countries, their implementation is not. This is because society, culture, habits, institutions, and geography differ. The so-called socio-cultural aspects of a nation and region affect how people use or accept new innovations brought to their doorstep.

Knowing your customer-citizen is an obvious element of the fundamental understanding required for a company or policymaker to successfully manage transitions in the desired direction. But there are certain pitfalls and challenges, especially if the business or governance approach is geared towards “one size fits all” solutions, meaning that the segmentation and target group is very narrowly defined and understood.

For example, research on municipal energy transition (Berg et al 2021) shows that it is quite common that only a small group of decision-makers and experts, as well as some energy-interested inhabitants, are consulted when planning local energy solutions. The majority of local people do not participate; they will not sign up for discussion and workshop events even if the events are open for everyone. Still, the main users of the future energy solutions or those who could benefit economically might be in those groups remaining outside the discussions and planning, thus affecting the actual realisation of them. Examples of negative outcomes include protests against new instalments such as wind power, solar power and smart meters or non-compliance to agreements.

Even if renewable, clean energy solutions could present opportunities to boost regional wealth and livelihoods, there is always a chance of the actual gain landing somewhere else, on someone else’s plate. Whilst there might be a significant investment in a new renewable energy facility in a municipality, the economic gain might go to a multinational company. The locals are left with the negative side effects of the construction phase, restricted land use and other changes in the living environment. Unwanted externalities are unfortunately commonplace in most market systems, and the energy sector is no exception.

So, why do so many people remain outside important planning processes, one might ask? Especially if there has been a clear invitation to join? One explanation, outside the lack of personal interest and knowledge, might be found in the hidden and/or visible power hierarchies. Power dynamics are inherent to energy transitions. The social and cultural structures of a country, region and local context affect who will be heard and considered an expert. How can we break these invisible hierarchies and power structures so that more people can have a say in development that is clearly affecting their lives? There are many positive examples of local (energy) communities where many different actors have started working together towards a common goal. These groups are usually “bottom-up”, created by a clearly defined need or challenge.

We as humans need connection to each other, nature, and our roots (culture). A safe place for well-being might look different to different people, but it is usually connected to what we consider our home. What if there was more focus on the local and “home” levels in the planning phase of new energy solutions? Would it make a difference to the success of projects and new innovations, or maybe people would choose differently?

Smart cities, smart households, digital IDs, electric vehicles, and ultimately people are becoming part of the Internet of Things at a time when global policies and “big tech” are driving the Western energy market(s) towards electrification.
All of this is taking place in the name of sustainability. One can wonder whether there is a “stop button”, i.e. a right to opt out and find alternative solutions to our energy futures. Perhaps there are alternative possibilities or visions accessible to us that would equally encourage a healthier world?

 

Petra Berg – Postdoctoral Researcher School of Marketing and Communication and VEBIC, University of Vaasa

ydrogen gas is one of the most realistic complementary ways to sustain our modern lifestyle. It is the most abundant element in the universe (15%) and applies to industrial energy and processes in its gaseous form. This molecular Hydrogen is increasingly produced as “green hydrogen” by using renewable energy, such as solar or wind, for splitting and liberating it from water. Alternatively, it could be produced in the low-energy route as biohydrogen, exploiting the metabolic potentials of anaerobic bacteria. This method is the most sustainable and can also be used in a localized pattern. This ensures maintenance security for unit plants as biomasses and side streams could be used as raw material sources.

Why has the Hydrogen launch been delayed?

Some fifteen years ago, the US Environmental Protection Agency estimated that in the year 2025, the USA would move into a “Hydrogen economy,” meaning that Hydrogen would produce more energy than fossil sources. This has yet to happen since there has been a transition period where numerous sustainable energy sources have been developed. There have also been some issues with, for example, the storage of Hydrogen. However, at the moment it provides a promising solution for energy storage. Hydrogen can also be further processed into methane or methanol. “Green ammonia” can also be produced from green Hydrogen or biohydrogen, and it can be used for storing energy and then being converted back to Hydrogen when needed. In the future, the use of these gaseous compounds will grow intensely. They can also provide solutions for boat, air, and heavy road traffic.

The Industrial networks for distributing Hydrogen have already been established in places like the Ruhr area in Germany, the Midwest in England, and industrial Japan. For instance, traffic solutions are also tested and implemented in California and South Korea. In Luleå, Sweden, SSAB Ab started a steel factory in 2020 using Hydrogen gas as the reducing agent.

This is important from the climate point of view since 7% of the global emissions come from steelmaking industries.

Lucrative options for future maintenance and energy security

Compared with the vast energy and chemical needs described above, biohydrogen is in the very first stages of development. However, it could offer a flexible solution for decentralized energy sources that serve unit plants ecologically and sustainably, providing increased maintenance security as the production units can be protected better than pipelines, for example. Moreover, the local biomass raw materials and side streams offer flexible sources for the processes and production. Economically, combining bacterial biohydrogen production with the manufacturing of organic chemicals and fertilizers is easy. Thus, the biohydrogen way could be an essential future avenue for industrial development globally. It could also provide energy and reduce the power needed for recycling materials and cleaning up pollution or contamination in ecosystems, cities, or agricultural fields.

In some countries, biohydrogen production has been started in smaller units like big animal farms or other distributed units. The diminished scale in such cases provides flexibility. In other words, the strong point of microbial biotechnology can be utilized, as the same installation could easily apply various biomass sources. In this sense, biohydrogen production could resemble, for some parts, biogas production, which has been taken into use besides the agricultural or smaller industrial units and the municipal water treatment systems in many places.

Biohydrogen is omnipotent

Since biological materials are found almost everywhere, it is relatively easy to imagine their use for biohydrogen production, which will not produce waste but diminish or shrink its volumes. The numerous bacterial strains could be used in various processes for different organic raw materials. This versatility of planning options of the bioprocess could make biohydrogen the mainstream technology in future. This easiness of planning could make biohydrogen the mainstream technology in future. It could provide multiple industries with flexible and secured energy sources and options for future development.

Finnoflag’s biorefinery experience

In recent decades, our R&D company, Finnoflag Oy, has carried out more than ten industrial pilot projects using microbes or their enzymes as biocatalysts. In such trials as the European Union Baltic Sea Biorefinery Project ABOWE, we realized that cohesively with the production of biochemicals, we could obtain significant amounts of biohydrogen.

The project was participated by six countries: Germany, Lithuania, Estonia, Poland, Sweden and Finland. The movable pocket-sized biorefinery was tested for potato industry side streams in Poland, agricultural and abattoir waste in Sweden, and Paper and Pulp industry side streams in Finland. In all cases, biohydrogen was emitted into the carrier gas in the bioreactors with a maximal concentration of 3-4%. Savonia University of Applied Sciences constructed the movable biorefinery unit in Kuopio under the supervision of the undersigned and Finnoflag Oy in 2013, and its testing in three countries took place in 2014. Besides biohydrogen, many organic acids were formed, such as lactate, butyrate, acetate and valerate, and alcohols or sugar alcohols like ethanol, butanol, propanol, pentanol, and 2,3-butanediol. The residual fraction could be refined into organic soil improvement. The reliable and accurate NMR method (Nucleic Magnetic Resonance) was used for measuring the products by the School of Pharmacy of the University of Eastern Finland.

A few years later, in 2018-19, we produced biochemicals, energy gases, and fertilizing agents from environmentally deposited cellulosic waste in the lake bottom sediment in Tampere, Finland. In these trials, the biohydrogen levels exceeded 1-2% in the outflowing gas. Mälardalen University of Västerås, Sweden, participated in the downstream processing of chemical commodities such as lactate. The gas levels were detected from the airspace of the horizontal bioreactor unit of 15 cubic meters of liquid space. In this case, the gas flow space was even more significant. These production levels could be elevated, and the current productivities are a good start for novel biological process thinking by the Finnoflag method using non-aseptic fermentation. This approach lowers the investment expenses to about 25 % of the traditional industrial fermentation costs at best.

Global hope in biorefining

Most importantly, biohydrogen and its associated products of microbial biorefineries could make it possible to establish various novel industries which would act economically and sustainably. They could be used for cleaning up the environment in ecosystem engineering projects. The biohydrogen approach is also compatible with developing Hydrogen and other energy production, storage, security, transfer and equipment maintenance techniques at any scale.

Elias Hakalehto, PhD, Adj. Prof., Microbiologist,
Biotechnologist, CEO and inventor, Finnoflag Oy

Prominent industry players monitor the condition of their critical assets by scrutinizing data sourced from myriad sensors to pinpoint trends and anomalies. The resulting insights drive maintenance strategies grounded in simplistic rule-based algorithms.

However, the effectiveness of the algorithms hinges on the expertise and knowledge of personnel analysing the data and devising rules, thus, rendering the algorithms resource-intensive and occasionally unreliable. Furthermore, they fail to identify issues tied to asset disparities, operating environments, and customer utilization patterns.

Recent research has honed in on “stochastic dependence,” modelling interactive asset behaviour within complex systems, dispelling the notion of independent and isolated silos. Nonetheless, for PHM and CBM to thrive, two pivotal challenges must be tackled:

• Facilitating data and insight sharing among assets to foster system-wide visibility of deterioration and performance enhancements for optimal asset health.

• Empowering assets to autonomously and collaboratively make maintenance and operational decisions grounded in overall system performance, rather than individual asset performance, ushering in not only comprehensive fleet and individual asset health assessment but also resource-efficient maintenance allocation.

Embracing Resilience and a Human-Centric Approach: The Catalyst of COVID

The global COVID-19 pandemic ushered in a new era of challenges for European industrial firms, with a particular impact on small and medium-sized enterprises (SMEs) navigating a fiercely competitive manufacturing landscape, as economies worldwide reopen following a prolonged disruption. Concurrently, recent years have witnessed tumultuous shifts in the socio-political arena, alongside conspicuous signs of climate change and an ongoing energy dilemma. Bolstering competitiveness within EU industries rests on a critical imperative: industrial assets and systems must possess the capability to adapt to their intricate and costly operational demands through innovative designs and unwavering reliability throughout their lifecycles. Vigilant management of equipment and system health and the associated risks is paramount to safeguard a secure and thriving industrial sector.

Consequently, European industries should channel their efforts towards astute asset management, improving availability, maintainability, quality, and safety. Within this framework, Europe’s pursuit of global leadership hinges on establishing an internationally appealing, secure, and dynamic data-savvy economy, underpinned by a trustworthy artificial intelligence (AI) ecosystem.

Over the past decade, remarkable strides have been made in research and development, uniting novel data science methodologies in machine learning (ML) and AI to confront pivotal challenges in industrial automation and control. The technological evolution encapsulated by Industry 4.0 has advanced equipment diagnostics and prognostics from conventional physics-based models to data-driven ML techniques. Current strides in digitization, however, amplify the demand for human skill in dissecting extensive data sets. This calls for proficiency in data science, statistics, and programming, a paradigm shift that risks alienating the majority of “boots on the ground” – factory workers, maintenance engineers, and technicians – compelling them to either upskill or risk redundancy. Thus, humanizing digital technologies is a pressing imperative for this decade.

Beyond the human aspects, several fundamental obstacles are slowing the widespread industrial adoption of these emerging technologies. Firstly, AI algorithms rely on operational data, confining their applicability to scenarios with copious volumes of data. Secondly, prevailing strategies for mitigating data scarcity or imbalance hinge on aggregating data from vast asset fleets, and this approach falls short of delivering an optimal solution because the average behaviour of assets in a fleet fails to represent the intricacies of any individual asset. Thirdly, integration poses a formidable challenge within a system-of-systems framework, where an industrial ecosystem comprises diverse equipment types, often originating from various original equipment manufacturers (OEMs). These heterogeneous assets must flawlessly collaborate to attain overarching system objectives.

Achieving peak system performance necessitates pinpointing the ideal combination of actions across these assets in a dynamic and uncertain environment. Present-day AI techniques cannot really learn the intricate interrelationships between diverse system assets, impeding their utility in supporting decision-support systems reliant on equipment cohesion to achieve system-optimized outcomes. Collaborative AI emerges as a pivotal enabler, facilitating asset communication, data sharing among kindred assets, collective failure pattern learning, and behavioural optimization. Nonetheless, these techniques remain underdeveloped and unrefined for industrial equipment. For instance, clustering assets based on dynamic behavioural data similarity remains an elusive endeavour. Once akin assets are identified, seamless communication and operational status exchange, coupled with control action dissemination, become imperative. In this context, a fundamental challenge arises in machine-to-machine communications, exacerbated by a profusion of standards and protocols. This proliferation hampers communication between disparate equipment types from multiple OEMs—typical within intricate industrial systems.

These multifaceted challenges collectively relegate system-level optimization to a distant aspiration. Yet system-level optimization constitutes the very essence of efficient and effective 21st-century enterprises. How can data, information, and insights be seamlessly shared among asset fleets and human stakeholders, culminating in system-level optimization?
In the realm of consumer technology, data sharing through Internet of Things (IoT)-enabled devices via social networks is on the rise, offering avenues for benchmarking and performance optimization. Notably, companies like Garmin and Nike have pioneered platforms enabling consumers to share and compare data on their exercise routines. These data, collected through GPS and IoT-enabled wristbands, can provide an inherent health boost, as the exchange of health data, habits, and insights among peers promotes collective well-being. This social application of data holds immense promise in the realm of asset health management.

Presently, the bulk of research and development attempts to leverage IoT and social media to target end-consumers. These endeavours range from smart home appliances to data mining to harness consumer data from social networks and drive more refined and targeted marketing strategies. Our overarching objective, however, is to channel the potential of these groundbreaking technologies into the domain of manufacturing and industrial systems.

Elevating Digital Twins into Social Entities

The advent of the Digital Twin (DT) concept has ushered in the era of digitally replicating physical assets. It endows assets with true intelligence by incorporating software agents, paving the way for machines to communicate, collaborate, and cooperate indirectly, through their digital doppelgängers within what is known as the metaverse. This innovation holds the promise of surmounting the challenge posed by incompatible data standards and protocols, while bestowing assets with collaborative learning and decision-making capabilities.

Although a gamut of DT models with varying functionalities has emerged in the era of Industry 4.0, integrating these DTs for deployment in complex systems and fleets remains a formidable task. The detailed exploration of architectures that amalgamate collections of DTs is largely uncharted territory. The focus has been on individual assets, necessitating more concerted efforts to materialize an interconnected federation of DTs. In a complex and dynamic system, such as infrastructure networks or expansive industrial plants, we envision a hierarchical structure for DTs. DTs representing virtual collections (e.g., subsystems or sub-fleets) of assets will reside in the upper levels of the hierarchy. These collections may dynamically form based on the “friendships” cultivated among social assets. Within this hierarchy, a “supervisory” DT that encapsulates the entire collection becomes indispensable. The demand for cognitive DTs equipped to design and deploy other DTs dynamically and autonomously, coupled with seamless interaction with humans in close cooperation – integrating avatars as part of the process – is a commendable aspiration within this context.

However, the technology for facilitating communication between DTs is still in its nascent stages; standardization is wanting, and industry-wide best practices are notably absent. Multiple disparate working groups have already developed a medley of standards describing heterogeneous assets at various levels. These standards offer generalized blueprints for DTs and have yet to gain substantial traction within the industry. Indeed, existing standards often suffer from overgeneralization or fall short of accommodating the swift evolution of DTs. Consequently, data shared across contemporary cyber-physical systems seldom adhere to a format readily comprehensible by human stakeholders beyond data specialists. Addressing this imperative entails devising solutions for sharing learning data and information. Messages exchanged between DTs and the social platform must incorporate ample context to ensure transparency, safety, and robustness, while enabling human agents to seamlessly participate in message processing. Augmenting transparency necessitates crafting a schema vocabulary for a standardizable information model, extending its capabilities to match the communication requisites between DTs and between DTs and human stakeholders. Safeguarding safety and robustness hinges on the implementation of explicit indicators for device health and data integrity.

Cultivating a Collaborative Digital Ecosystem: The Vision of Social Networks for Industrial Assets

Picture a world where individual machines in factories across the globe and infrastructure assets within vast networks compile, upload, and disseminate condition and operational performance data, alongside human-comprehensible “status updates,” via a purpose-built social network platform. The potential for learning and optimization within this landscape is immense. Assets spanning a system or network can engage in collaborative learning, pattern recognition, and problem diagnosis, harnessing collective wisdom to adapt their behaviour, alleviate the burden on ailing equipment, and bolster long-term system performance. Operators, maintenance engineers, and managers gain the ability to peruse these status updates, pinpointing opportunities for efficiency enhancements and orchestrating measures to optimize their system’s performance.

The industrial systems of the future will comprise genuinely intelligent collaborating assets, seamlessly leveraging AI in conjunction with human expertise, fostering heightened efficiency and judicious resource allocation. The underpinning models driving these systems will be fortified by explainable AI (XAI), instilling trust in autonomous behaviour among human managers.
Realizing a vision of collaborative industrial assets through a social network within the realm of AI is a tough challenge. This interconnected and intelligent “social network” of assets draws inspiration from the social networks observed in biological entities. In its pursuit, the construction of a multi-agent ecosystem for a collaborative asset social network via a dedicated social network platform must duly acknowledge the indispensable presence of human stakeholders. This necessitates a robust foundation encompassing techniques for efficacious social network formation, algorithms empowering agents to cultivate contextual awareness, federated algorithms facilitating collaborative learning, and multi-agent reinforcement learning strategies for cooperative decision-making. The ultimate objective is to cultivate a network of diverse assets adept at collectively optimizing operations while mitigating climate impact and data vulnerabilities. The framework prominently involves humans, serving as essential contributors who both instruct and learn from software agents through cutting-edge data mining algorithms adept at deciphering intricate data and distilling actionable insights.

Professors Diego Galar, Ramin Karim and Uday Kumar from the Luleå University of Technology, Sweden

Unlike motors, pumps are rated by head and flow, not by power. There’s no such thing as a 50 hp pump or a 100 kW pump. A pump can operate over a range of heads and flows, and the power required is determined by those and by the pump’s efficiency at the particular head-flow operating point. It’s helpful to know that “head” correlates to a measure of pressure. For water, it’s a simple conversion: 2.31 ft head = 1 psi (1 m head = 9.8 kPa). Here’s a simple formula that describes the relationship between head, flow, pump efficiency and pump power: (where k depends on chosen units)

While this formula is helpful for quickly estimating the power required for a rotodynamic pumping application with known head and flow values, you can only get accurate power values from the manufacturer’s pump curve. How to read pump curves is beyond the scope of this article. What is important here is that the power requirements vary with flow rate, so knowing the range of flow rates for the pump is essential to sizing a motor to the pump.

Sizing the pump

The process of sizing a pump and motor starts with sizing the pump for the application’s range of head and flow requirement. The following basic concepts are evident on the pump curve.

Flow requirement. A pump may operate across a wide range of flow rates, known as the Allowable Operating Range. Ideally, the pump should be designed to operate as close as possible to the Best Efficiency Point (BEP) and within the Preferred Operating Range. Pump efficiency will drop dramatically as flow rates move away from the BEP, and turbulent flow will reduce the reliability of the pump.

Head requirement. The head that a pump can deliver must match the application. If the maximum pump head is below the system demand, the pump will not produce flow (bad!). If the maximum pump head is much greater than the system demand (more than double), the operating point will not be near the BEP, and both efficiency and pump reliability will suffer.
Cavitation. Another important concern when selecting a pump for a specific application is the possibility that cavitation may occur. If the pump is to operate across a range of flow rates (rather than always operating near a single flow rate), cavitation will be more likely at the higher flow rates. Pumps have Net Positive Suction Head Required (NPSHR) ratings, which allow evaluation of the likelihood of cavitation at any flow rate using NPSHR values from the pump curve. Generally, lower-speed pumps are less susceptible to cavitation than higher-speed pumps. If the application has low suction head demands, a lower operating speed will be an advantage. At lower operating speeds, a larger pump impeller diameter will be required, and thus a physically larger and more expensive pump may be needed.

Sizing the motor

Once a pump of the proper size is selected for the application’s range of head and flow, the motor can be sized and selected to match the pump’s requirements.

Minimum power requirement. For most pumps, the power requirement varies with flow rates. Power requirements may increase or decrease with increased flow. The pump curve will provide that information. Obviously, the motor must have adequate power to meet the pump demand at the application flow rate with the highest power requirement. That’s the minimum power requirement for the motor. But it is likely the pump will have an Allowable Operating Range wider than the application demands.
Maximum power requirement. If application demands were to change at some future time, the pump might be expected to operate at a point where the power requirements are greater than the minimum power requirement. Therefore, it’s wise to consider the maximum power the pump could require under any operating conditions. This value is provided on the pump curve as the No Overload Power (NOL) rating. In some cases, the difference between the minimum power requirement for the application and the NOL rating may be absorbed by the motor service factor. In other instances, sizing for NOL power may require a higher power motor.

Conclusion

Sizing a pump and a pump motor for an application is not a trivial endeavor. The application head and flow requirements must be known. The pump power formula provided above, with the “k” to match the selected units, will provide a good estimate of the size of the machine. Pump vendors have pump selection charts which are generalized versions of the pump curve that will help with pump selections. Those charts and related reference data will provide NOL power ratings. The person responsible for selecting a pump and motor should have the appropriate pump curves and motor data and know how to read them.

Eugene Vogel, Pump and vibration specialist at at the Electrical Apparatus Service Association (EASA, Inc.)