Advances in Technology that Impact Industrial Automation
Published on : Wednesday 09-02-2022
Which forthcoming advances in technology will impact industrial automation? Jasbir Singh elaborates.
Technology is an ever evolving phenomenon in the world; may it be applicable to manufacturing industries or any other sector. The new technologies are originating from the world’s prevalent issues. New technologies are emerging to make the available technology easy to implement, provide innovative solutions in the current production process, and/or to achieve high productivity and higher financial growth expected from projected advance systems. Predictions are always made for new technologies that have answers in time to come. New and upcoming technologies and innovative solutions make the work easier in the coming years and are expected to transform our life.
1. Defence and aerospace sectors are looking for innovation to develop zero fuel aircrafts, advanced space propulsion systems, advanced automation, improved material science, use of 3D printing for producing many components and application of blockchain in this sector. These developments are taking shape fast enough but at a consistent pace.
2. 5G networks are getting more popular as work from home is advised by many companies due to Covid, wide use of video conferencing for remote meetings, digital collaboration and reliable connectivity and good bandwidth. 5G connectivity is helping many companies to reinvent business as people are producing results even from remote working. Developments in 5G technology and making it affordable to users is the target for many companies. The estimated growth rate of 5G technology is substantially high in the coming years.
3. Edge computing is another area of development. All contemporary technologies like collaboration with artificial intelligence, 5G and global networks leading to faster, reliable and more efficient data processing are making edge computing the best choice during the Covid-19 pandemic. From traditional rugged embedded computers to high performance Edge servers for AI and other data intensive applications, companies are offering fast and reliable solutions.
4. Extended reality development is taking shape and getting popular at a much faster rate. This includes augmented and virtual reality. In conjunction with other technologies, this tackles the challenges posed by the current situation due to Covid. This technology will revolutionise manufacturing, healthcare, education and lifestyle areas among others. This will guide them through training, remote assistance of product and system faults in plants running units, malls, business houses and office infrastructure.
5. Artificial intelligence or AI shall be one of the most transformative evolutions of the current time. Due to the current world scenario, artificial intelligence is more promising than ever. The volume of data collected from machines can be used to fine tune the set point of production to healthcare where it can be used to protect the infectious spread. Machine learning (ML) is of great help and it became increasingly sophisticated in the solutions it uncovered. In the current year AI will make predictions on the health of humans and connect to hospitals to give advance warning to people. Artificial intelligence shall be integrated in every development of future as it be a device, system, planning, production, predictive information, learning, and cybersecurity systems. Artificial intelligence shall be a strong pillar for machine learning.
How are manufacturing industries leveraging AI/ML? How do automation controllers provide the necessary platform?
Artificial intelligence and machine learning tools enable humans to work smarter and more efficiently in manufacturing industries. Together, these automate fault detection and update information on dashboards for adjudicators to respond immediately. The system generates automatic calculations for processing the action to control the deviation and mitigate the fault, resulting in faster processing time and fewer human errors. Proactive fault detection and timely action to ensure the correction is possible by way of machine learning using artificial intelligence and the action which improves over the period with more data collection from the system. AI-powered machine learning models are developed/deployed to detect behavioural patterns from large datasets.
AI and ML need massive amounts of data to be gathered by IoT devices. What strategies do industry plan to collaborate in data collection?
IoT devices are proliferating exponentially around the world and generating a continuous flow of valuable data. These intelligent devices generate more data than ever before. Humanly it is only possible to track a fraction of this incoming data, analyse it to quickly extract business intelligence needed or spot issues in real time. The collection of more and more data provides massive information to carry out deep analysis to understand the right patterns generated by these large data sets. Using machine learning process systems follow data to learn the behaviours and build configurable rules that fine tune with new information that is received into the system. It will uncover patterns that are likely associated with system behaviour that an engineer might have missed or difficult to define during the design phase.
Right data collection is a major challenge for the development of good machine learning solutions. It is mainly due to the confirmed design of the right module that data collection becomes a critical issue. Machine learning is now becoming widely popular for various advanced applications to fine tune the result. We find it does not necessarily have enough historically stored labelled data for many applications. Machine learning is based on the platform having the use of computer algorithms improve automatically through available and process generated data while in operation by the use of these collective data. Use of artificial intelligence modules on ML are consistently improving and represent the right guided path for the machine to perform.
How can AI and ML help companies create predictive models, analyse operations, make accurate forecasts and automate supply chains?
Machine learning algorithms are developed to build a model using some representative and sample data, to make the predictions or decisions without being regularly programmed to improve. Designers work especially on the data pipeline source and by use of artificial intelligence-based algorithms, so that it gets integrated and interacts together to deal with issues like model drift, ongoing model learning process for improving the implemented models. A real-time digital twin technique provides a powerful tool to run and check these ML algorithms in real time functioning virtually and at scale. This digital twin technique generates a physical data source to a unique virtual digital twin. Each component of it runs on a computing platform which hosts ML algorithms along with associated state of information required to validate data source. Next to machine learning, deep learning techniques are also used, which automatically generate features to fine tune the model, however it requires large amounts of available labelled data.
The full potential of AI and ML is realised only when the scale of operations is big enough. How can the average SME benefit with their limited resources?
Data collection is not only used for machine learning, but for the data management community that understands the importance of handling large amounts of data. A comprehensive source of data, defines its collection from a data management point of view. Data collection consists mainly of data acquisition, data labelling, and its use for the improvement/strengthening of existing data quality or models. The integration of data management and machine learning data collection is part of Big Data and Artificial Intelligence integration, which opens doors to benefit the average SME also. Industry-specific analytics solutions developed on data-driven model-based architecture deliver and provide high-impact on the business value, which accelerate process by the rapid digital transformation. In small or medium size organisations, responsibility across management tends to be undefined or under-defined, which is basically having a lean environment to manage the systems and development goals. Due to limited resources, engineering people typically play multiple technical and operational roles. The management and engineering staff in SME companies are lean, it requires a clear vision, where engineering and technical manpower are empowered and accountable with transparency by autonomous functioning.
The human element remains critical in deployment of new technologies. How is skill development to be planned in a scenario of not yet mature technological advances?
The human element is the key in every stage of digital transformation/deployment of new technologies such as man machine collaboration, skill development, cultural change, people-empowerment and multiskilling. People don’t want digitalisation and they in fact value human and face-to-face interactions for the tasks to be performed. Non-digital interactions and transactions during digital transformation plays a vital role for empowering people in industry and more to the direct workers. Change management training and skill-up to assign new activities within various departments across the organisation is key for digital transformation success. Strategy should aim to create the level up capabilities for fully leveraging the possibilities and opportunities within the organisation to accept the new technologies and their impact in better and more innovative ways of functioning in future. This journey needs a forward stepped approach with a clear roadmap to achieve the vision involving all stakeholders, breaking their silos developed in time due to internal/external limitations. This roadmap is built in a way that the end goals shall continue to accept/drive the digital transformation ongoing journey and change process with continuous digital innovation.
Jasbir Singh is an Automation Expert having long experience in Factory Automation, Line Automation, Implementation Strategist, Business Coach, Regular writer on automation, Artificial Intelligence, Robots/Cobots, Digital Technology, Network Communication, Industrial Internet of Things (IIoT), Wireless Communication, Block Chain and use of advance digital technologies. He has established a long association with Business Houses/large production houses to improve factory automation in their production lines as well as productivity improvement in factories in India and overseas; and in advising and designing the units to transform into digital platforms by use of Artificial Intelligence. Email: [email protected]
