Industrial Analytics: Blending Engineering Knowledge with AI and IoT
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Richard Büssow, Managing Director
Innovative strides are often made by solving a quintessential problem within the industry. And, the story of how Industrial Analytics, a disruptive AI-based rotating machinery solutions provider, came into existence began with a similar resolution that evolved into one of the most innovative approaches to redefining the manner in which industrial facilities monitored and managed their machinery. Richard Büssow and three of his colleagues recognised that many original equipment manufacturing enterprises lacked the agility to expedite production processes. At around the same time, digitalisation was quickly growing in popularity, becoming a necessity almost. Additionally, depending on whether they have a monitoring solution or not, data collected by businesses are often not analysed and sorted in a resourceful manner. Industrial Analytics does not employ statistical methods, unlike most of its competitors, which do not involve the dynamical behaviour of the machinery. The company leverages physical-based models, recreating a plant itself and comparing the expected values against the expected behaviours. Additionally, organisations in the field were in need of a solution that interprets the data and enables them to gain actionable engineering knowledge; and Industrial Analytics’ offerings deliver exactly on this requirement.
The company maintains the engineering expertise to build such comprehensive solutions, all while being fully-informed of the machinery in use. Moreover, several software companies in the field unfortunately do not have access to highly-skilled professionals who are capable of interpreting the data accurately and obtaining desirable results. These factors motivated the four extremely talented employees to take up the opportunity and fulfil their vision to revolutionise monitoring solutions with the power of AI.
Catering to the oil and gas, energy, utilities, and chemical industries, Industrial Analytics combines deep engineering knowledge and AI with Internet of Things (IoT) technologies to develop solutions that help operators effectively interpret data from varied machinery within manufacturing facilities, refineries, and energy plants among many others. The company has built a physics-based, first-principle model that generates metrics indicating machine health and maintenance conditions.
We empower our clients to reduce maintenance costs, enhance uptime of their plants, and effectively monitor their machines, all while improving their operational efficiency
The company has built a physics-based, first-principle model that generates metrics indicating machine health and maintenance conditions.The solution analyses information and presents it in a specialised dashboard with dynamic reports through which operators can check actionable insights and recommendations made by the machine learning algorithms, and Industrial Analytics’ engineering experts then build the solution.
The company offers a comprehensive AI-service model that integrates the solution into an organisation’s existing data infrastructure, training the algorithm on specific equipment and then retraining it again after six months. Industrial Analytics maintains prebuild models for different components like motors, compressors, steam turbines, and gas turbines as well. Vibration analysis is an additional service provided by the company that procures more insights as vibration signals are extremely sensitive indicators. The monitoring solution itself can be used without adding any more sensors by simply leveraging the existing data within the company. Industrial Analytics’ monitoring solution generates highly accurate, reliable results and fewer false alarms as well. “As an industry leader, we empower our clients to reduce maintenance costs, enhance uptime of plants, and effectively monitor their machines, all while improving their operational efficiency,” adds Büssow.
ndustrial Analytics also excels at visualising data, analysing it, and helping machine supervisors and machine owners better understand their equipment and facilities—new or old—alongside a wealth of statistic insights. Owing to its AI capabilities, Industrial Analytics’ solution learns and retains the knowledge and actions of previous operators, providing new supervisors with their predecessors’ expertise and comprehension, effectively increasing productivity levels. The company’s clients greatly appreciate this proactive approach, benefitting from its innovative and ground-breaking competencies.
In one particular instance, a client’s facility experienced repeated failures in the functioning of their signal interface components, caused due to changing pressures within the compressors. Industrial Analytics assessed the plant’s operations, developed a digital twin, perfectly simulated its working, and detected the cause of the failure.
The company then provided the necessary solutions and consultations to drive the desired results. Since their collaboration with Büssow and his team, the client’s signal interfaces have worked flawlessly, requiring little to no manual intervention. On another occasion, Industrial Analytics helped a client extend the lifetime of their machines and save numerous resources.
With such comprehensive offerings and high-quality service delivery, the company is set to build more partnerships with a host of educational and research institutions to improve the capabilities of its services. Industrial Analytics, since its founding, has already been collaborating with Hasso Plattner Institute (HPI) and intends to add many more in the near future. Having established a strong presence in the German market, Industrial Analytics is currently aiming at extending its services overseas; to Spain, Switzerland, the Middle-east, and Asia. “Our highly skilled and experienced team blends engineering knowledge with contemporary technology to develop advanced solutions. We are currently exploring our solution’s capabilities and its applications in global industrial ecosystems,” concludes Büssow