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How AI drives the smart factory concept

How AI drives the smart factory concept
The smart factory concept has taken shape in more and more manufacturing facilities in the world. Specifically, operators are now relying on sensors and the data they generate to predict downtimes and machine failure. In this regard, AI and machine learning algorithms can play a significant role.
That was the rationale behind which Israel-based Presenso launched its AI-based predictive maintenance solution.
Needless to say, factories’ primary objectives are to increase productivity and reduce downtimes. A machine failure can result in shutdowns of operations and thereby cost manufacturers significantly in the forms of delayed production and time-to-market. The importance of predictive maintenance, then, is increasingly being recognized by top management.
“A few months ago, we conducted a research study with Emory University students on the attitudes of industrial plant workers. We found that most operations and maintenance employees are satisfied with current predictive maintenance solutions and do not see much evidence that plant level employees are driving demand,” said Eitan Vesely, CEO and co-founder of Presenso. “This is very much a top-down requirement from senior management that recognize the economic potential from improving asset uptime. In summary, this is a financial play that is directed by C-level executives.”
That said, manufacturers are increasingly relying on sensors and the data they generate for uptime optimization and predictive maintenance, Vesely said. “Within the exabytes of sensor data generated by industrial machines are micro-patterns that can tell us when a machine is likely to fail. Until now these patterns were undetectable to even the most advanced industrial monitoring tools. Data scientists working to improve machines uptime lacked the tools to find these patterns, and industrial plants lacked the data scientists to even try,” said. “At Presenso we provide industrial plants with advanced notice of evolving machine failure so that remediation can occur prior to breakdown.”


According to Vesely, Presenso's solution collects data from hundreds of machines and thousands of sensors and streams the data to the cloud where they are processed and analyzed for impending machine failure. The system also provides valuable information about the remaining time to failure and its origin within the machine.
The real value of the solution, however, lies in AI and automated machine learning (AutoML), which Vesely said signficantly reduces tedious and repeitive tasks that would oridnarily be performed by Big Data professionals.
“Our solution is based on an unsupervised machine learning methodology. Using Advanced AI, the algorithm recognizes data patterns without receiving prior training on the underlying asset. Vast amounts of data are analyzed, and in some cases, the algorithm itself can generate the labels needed for its own training. Once trained, the algorithm is looking for abnormal patterns that are indicative of evolving failures,” Vesely said. “Our experience shows that AutoML is the best way to automate many of the time-consuming and repetitive machine learning tasks such as big data preprocessing, feature engineering and model selection and validation. This speeds up customer onboarding and widespread solution adoption.”
A beta version was launched in early 2017 and was deployed at multiple customers' sites. After testing in multiple production environments, the company has officially released the solution to the wider industrial market.
“We provide a software-as-a-service solution. Data that is generated by industrial machinery is analyzed in the Presenso cloud, mainly on the backend.  We provide the frontend with real-time machine stability indicators that alert on asset degradation and failure,” Vesely said. “Presenso relies on the existing sensors that are embedded within machinery, and there is no additional hardware or software installation requirements.”
According to Vesely, manufacturers from a variety of sectors can benefit from their solution. “Industries such as power generation, oil and gas, chemical processing and pulp and paper industries can benefit.  In these industries, the process is steady and continuous and thus makes it easier for the artificial intelligence engine to learn it. The data contains subtle patterns which, if detected, can be used to predict future failure,” he said.

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